Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

COMPUTER SCIENCE (1305 journals)            First | 1 2 3 4 5 6 7 | Last

Showing 601 - 800 of 872 Journals sorted alphabetically
International Journal of Digital Enterprise Technology     Hybrid Journal   (Followers: 1)
International Journal of Digital Literacy and Digital Competence     Full-text available via subscription   (Followers: 6)
International Journal of Digital Signals and Smart Systems     Hybrid Journal   (Followers: 4)
International Journal of Education and Development using Information and Communication Technology     Open Access   (Followers: 9)
International Journal of Electrical and Computer Engineering     Open Access   (Followers: 8)
International Journal of Electronic Banking     Hybrid Journal   (Followers: 3)
International Journal of Electronic Business     Hybrid Journal   (Followers: 2)
International Journal of Electronic Commerce     Full-text available via subscription   (Followers: 10)
International Journal of Electronic Government Research     Full-text available via subscription   (Followers: 3)
International Journal of Embedded and Real-Time Communication Systems     Full-text available via subscription   (Followers: 9)
International Journal of Engineering and Manufacturing     Open Access   (Followers: 3)
International Journal of Engineering Science     Hybrid Journal   (Followers: 5)
International Journal of Entertainment Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Experimental Design and Process Optimisation     Hybrid Journal   (Followers: 5)
International Journal of Foundations of Computer Science     Hybrid Journal   (Followers: 3)
International Journal of Fuzzy Computation and Modelling     Hybrid Journal   (Followers: 2)
International Journal of Fuzzy System Applications     Full-text available via subscription   (Followers: 3)
International Journal of General Systems     Hybrid Journal   (Followers: 1)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of Green Computing     Full-text available via subscription  
International Journal of Grid and High Performance Computing     Full-text available via subscription   (Followers: 2)
International Journal of Grid and Utility Computing     Hybrid Journal  
International Journal of Handheld Computing Research     Full-text available via subscription  
International Journal of Heritage in the Digital Era     Full-text available via subscription   (Followers: 7)
International Journal of High Performance Computing and Networking     Hybrid Journal   (Followers: 4)
International Journal of High Performance Computing Applications     Hybrid Journal   (Followers: 4)
International Journal of High Performance Systems Architecture     Hybrid Journal   (Followers: 6)
International Journal of Human Capital and Information Technology Professionals     Full-text available via subscription   (Followers: 3)
International Journal of Human-Computer Interaction     Hybrid Journal   (Followers: 22)
International Journal of Human-Computer Studies     Hybrid Journal   (Followers: 20)
International Journal of Humanitarian Technology     Hybrid Journal   (Followers: 1)
International Journal of Humanities and Arts Computing     Hybrid Journal   (Followers: 11)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
International Journal of ICT Research and Development in Africa     Full-text available via subscription   (Followers: 4)
International Journal of Imaging Systems and Technology     Hybrid Journal   (Followers: 1)
International Journal of Impact Engineering     Hybrid Journal   (Followers: 9)
International Journal of Industrial and Systems Engineering     Hybrid Journal   (Followers: 7)
International Journal of Industrial Electronics and Drives     Hybrid Journal   (Followers: 3)
International Journal of Information and Coding Theory     Hybrid Journal   (Followers: 6)
International Journal of Information and Communication Technology Education     Full-text available via subscription   (Followers: 13)
International Journal of Information Communication Technologies and Human Development     Full-text available via subscription   (Followers: 4)
International Journal of Information Quality     Hybrid Journal   (Followers: 3)
International Journal of Information Retrieval Research     Full-text available via subscription   (Followers: 28)
International Journal of Information Science and Management     Open Access   (Followers: 5)
International Journal of Information Science and Technology     Open Access  
International Journal of Information Systems and Management     Hybrid Journal   (Followers: 2)
International Journal of Information Systems and Project Management     Free   (Followers: 12)
International Journal of Information Systems and Software Engineering for Big Companies     Open Access   (Followers: 2)
International Journal of Information Technology and Computer Science     Open Access   (Followers: 3)
International Journal of Information Technology and Web Engineering     Hybrid Journal   (Followers: 2)
International Journal of Information Technology Project Management     Full-text available via subscription   (Followers: 9)
International Journal of Information Technology, Communications and Convergence     Hybrid Journal   (Followers: 14)
International Journal of Innovation in the Digital Economy     Full-text available via subscription   (Followers: 5)
International Journal of Innovative Computing and Applications     Hybrid Journal   (Followers: 3)
International Journal of Innovative Technology and Research     Open Access   (Followers: 1)
International Journal of Intelligence and Sustainable Computing     Hybrid Journal  
International Journal of Intelligence Science     Open Access   (Followers: 3)
International Journal of Intelligent Engineering Informatics     Hybrid Journal  
International Journal of Intelligent Enterprise     Hybrid Journal   (Followers: 1)
International Journal of Intelligent Information and Database Systems     Hybrid Journal   (Followers: 3)
International Journal of Intelligent Internet of Things Computing     Hybrid Journal   (Followers: 2)
International Journal of Intelligent Networks     Open Access  
International Journal of Intelligent Systems Technologies and Applications     Hybrid Journal   (Followers: 2)
International Journal of Intercultural Relations     Hybrid Journal   (Followers: 16)
International Journal of IT Standards and Standardization Research     Full-text available via subscription  
International Journal of IT/Business Alignment and Governance     Full-text available via subscription  
International Journal of Knowledge and Systems Science     Full-text available via subscription   (Followers: 1)
International Journal of Knowledge Engineering and Soft Data Paradigms     Hybrid Journal   (Followers: 1)
International Journal of Knowledge Society Research     Full-text available via subscription  
International Journal of Leadership in Education: Theory and Practice     Hybrid Journal   (Followers: 23)
International Journal of Logistics Research and Applications : A Leading Journal of Supply Chain Management     Hybrid Journal   (Followers: 16)
International Journal of Management & Information Technology     Open Access   (Followers: 2)
International Journal of Management Innovation Systems     Open Access  
International Journal of Mathematical Modelling & Computations     Open Access   (Followers: 3)
International Journal of Mathematical Sciences and Computing     Open Access  
International Journal of Mathematics & Computation     Full-text available via subscription  
International Journal of Mathematics in Operational Research     Hybrid Journal   (Followers: 2)
International Journal of Medical Engineering and Informatics     Hybrid Journal   (Followers: 4)
International Journal of Medical Informatics     Hybrid Journal   (Followers: 10)
International Journal of Metadata, Semantics and Ontologies     Hybrid Journal   (Followers: 9)
International Journal of Metaheuristics     Hybrid Journal   (Followers: 1)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 8)
International Journal of Mobile Computing and Multimedia Communications     Full-text available via subscription   (Followers: 2)
International Journal of Mobile Network Design and Innovation     Hybrid Journal   (Followers: 1)
International Journal of Modeling, Simulation, and Scientific Computing     Hybrid Journal   (Followers: 3)
International Journal of Modelling, Identification and Control     Hybrid Journal   (Followers: 1)
International Journal of Modern Education and Computer Science     Open Access   (Followers: 2)
International Journal of Multimedia Data Engineering and Management     Full-text available via subscription   (Followers: 2)
International Journal of Multimedia Information Retrieval     Partially Free   (Followers: 8)
International Journal of Nanotechnology and Molecular Computation     Full-text available via subscription   (Followers: 4)
International Journal of Natural Computing Research     Hybrid Journal  
International Journal of Neural Systems     Hybrid Journal   (Followers: 4)
International Journal of Online Marketing     Full-text available via subscription   (Followers: 5)
International Journal of Organizational and Collective Intelligence     Hybrid Journal   (Followers: 1)
International Journal of Parallel, Emergent and Distributed Systems     Hybrid Journal   (Followers: 3)
International Journal of Pattern Recognition and Artificial Intelligence     Hybrid Journal   (Followers: 12)
International Journal of Performance Arts and Digital Media     Hybrid Journal   (Followers: 12)
International Journal of Pervasive Computing and Communications     Hybrid Journal   (Followers: 3)
International Journal of Polymer Science     Open Access   (Followers: 25)
International Journal of Process Systems Engineering     Hybrid Journal   (Followers: 1)
International Journal of Quantum Information     Hybrid Journal   (Followers: 6)
International Journal of Reasoning-based Intelligent Systems     Hybrid Journal  
International Journal of Reconfigurable and Embedded Systems     Open Access   (Followers: 6)
International Journal of Reconfigurable Computing     Open Access  
International Journal of Refractory Metals and Hard Materials     Hybrid Journal   (Followers: 5)
International Journal of Reliability, Quality and Safety Engineering     Hybrid Journal   (Followers: 14)
International Journal of Reliable and Quality E-Healthcare     Full-text available via subscription   (Followers: 2)
International Journal of Research Studies in Computing     Open Access  
International Journal of RF and Microwave Computer-Aided Engineering     Hybrid Journal   (Followers: 26)
International Journal of Sediment Research     Full-text available via subscription   (Followers: 2)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 2)
International Journal of Service and Computing Oriented Manufacturing     Hybrid Journal   (Followers: 2)
International Journal of Shape Modeling     Hybrid Journal   (Followers: 1)
International Journal of Signs and Semiotic Systems     Full-text available via subscription  
International Journal of Smart Grid and Green Communications     Hybrid Journal   (Followers: 2)
International Journal of Social and Organizational Dynamics in IT     Full-text available via subscription   (Followers: 1)
International Journal of Sociotechnology and Knowledge Development     Full-text available via subscription   (Followers: 1)
International Journal of Soft Computing and Networking     Hybrid Journal   (Followers: 2)
International Journal of Soft Computing and Software Engineering     Open Access   (Followers: 13)
International Journal of Software Engineering and Knowledge Engineering     Hybrid Journal   (Followers: 6)
International Journal of Spatio-Temporal Data Science     Hybrid Journal  
International Journal of Speech Technology     Hybrid Journal   (Followers: 7)
International Journal of Strategic Change Management     Hybrid Journal   (Followers: 7)
International Journal of Strategic Communication     Hybrid Journal   (Followers: 5)
International Journal of Strategic Information Technology and Applications     Full-text available via subscription   (Followers: 1)
International Journal of Stress Management     Full-text available via subscription   (Followers: 6)
International Journal of Student Project Reporting     Hybrid Journal   (Followers: 4)
International Journal of Swarm Intelligence     Hybrid Journal   (Followers: 2)
International Journal of Swarm Intelligence Research     Full-text available via subscription   (Followers: 3)
International Journal of System Dynamics Applications     Full-text available via subscription  
International Journal of Systems Science     Hybrid Journal   (Followers: 2)
International Journal of Systems Science : Operations & Logistics     Hybrid Journal  
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
International Journal of Technoethics     Full-text available via subscription   (Followers: 2)
International Journal of Technology and Educational Marketing     Full-text available via subscription   (Followers: 2)
International Journal of Technology and Human Interaction     Full-text available via subscription   (Followers: 2)
International Journal of Technology Diffusion     Full-text available via subscription   (Followers: 1)
International Journal of Technology Marketing     Hybrid Journal   (Followers: 3)
International Journal of Telecommunications & Emerging Technologies     Full-text available via subscription   (Followers: 1)
International Journal of the Digital Human     Hybrid Journal   (Followers: 2)
International Journal of Trust Management in Computing and Communications     Hybrid Journal   (Followers: 1)
International Journal of Ultra Wideband Communications and Systems     Hybrid Journal  
International Journal of Virtual Reality     Open Access   (Followers: 1)
International Journal of Virtual Technology and Multimedia     Hybrid Journal   (Followers: 2)
International Journal of Web Services Research     Full-text available via subscription  
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 12)
International Journal of Wireless Information Networks     Hybrid Journal   (Followers: 2)
International Journal on Advances in ICT for Emerging Regions (ICTer)     Open Access   (Followers: 2)
International Journal on Artificial Intelligence Tools     Hybrid Journal   (Followers: 9)
International Journal on Digital Libraries     Hybrid Journal   (Followers: 544)
International Journal on Document Analysis and Recognition (IJDAR)     Hybrid Journal   (Followers: 2)
International Journal on Smart Sensing and Intelligent Systems     Open Access  
International Journal on Software Tools for Technology Transfer (STTT)     Hybrid Journal   (Followers: 4)
International Review of Law, Computers & Technology     Hybrid Journal   (Followers: 3)
International Review of Research in Open and Distance Learning     Open Access   (Followers: 24)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Internet of Things     Hybrid Journal   (Followers: 2)
Internet of Things and Cyber-Physical Systems     Open Access   (Followers: 1)
Internet Technology Letters     Hybrid Journal  
IoT     Open Access  
IPSJ Transactions on Computer Vision and Applications     Open Access   (Followers: 1)
Iran Journal of Computer Science     Hybrid Journal  
ISPRS Open Journal of Photogrammetry and Remote Sensing     Open Access   (Followers: 3)
ISSS Journal of Micro and Smart Systems     Hybrid Journal   (Followers: 3)
Issues in Informing Science and Information Technology     Open Access   (Followers: 2)
IT Journal Research and Development     Open Access  
ITM Web of Conferences     Open Access  
ITNOW     Hybrid Journal   (Followers: 1)
J-ENSITEC : Journal Of Engineering and Sustainable Technology     Open Access   (Followers: 4)
JISTEM : Journal of Information Systems and Technology Management     Open Access   (Followers: 6)
JMIR mHealth and uHealth     Open Access   (Followers: 3)
Johnson Matthey Technology Review     Open Access  
Jornal Brasileiro de TeleSSaúde     Open Access  
Journal of Computer Science & Systems Biology     Open Access   (Followers: 3)
Journal of 3D Printing in Medicine     Hybrid Journal  
Journal of Advanced Computer Science & Technology     Open Access   (Followers: 3)
Journal of Advances in Information Systems and Technology     Open Access  
Journal of Advances in Mathematics and Computer Science     Open Access  
Journal of Aggression Maltreatment & Trauma     Hybrid Journal   (Followers: 5)
Journal of Algorithms & Computational Technology     Open Access  
Journal of Altmetrics     Open Access   (Followers: 7)
Journal of Ambient Intelligence and Humanized Computing     Hybrid Journal   (Followers: 1)
Journal of Applied & Computational Mathematics     Open Access  
Journal of Applied and Computational Topology     Hybrid Journal  
Journal of Applied Bioinformatics & Computational Biology     Hybrid Journal   (Followers: 4)
Journal of Applied Communication Research     Hybrid Journal   (Followers: 10)
Journal of Applied Informatics and Technology     Open Access  
Journal of Applied Intelligent System     Open Access  
Journal of Approximation Theory     Hybrid Journal   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Automated Reasoning     Hybrid Journal  
Journal of Automation and Control     Open Access   (Followers: 9)
Journal of Banking and Financial Technology     Hybrid Journal   (Followers: 1)
Journal of Big Data     Open Access   (Followers: 16)
Journal of Bioinformatics and Computational Biology     Hybrid Journal   (Followers: 19)
Journal of Biomedical Informatics     Partially Free   (Followers: 9)
Journal of Cases on Information Technology     Full-text available via subscription   (Followers: 3)
Journal of Chemical Information and Modeling     Hybrid Journal   (Followers: 18)
Journal of Chemical Theory and Computation     Hybrid Journal   (Followers: 21)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)

  First | 1 2 3 4 5 6 7 | Last

Similar Journals
Journal Cover
Journal of Bioinformatics and Computational Biology
Journal Prestige (SJR): 0.388
Citation Impact (citeScore): 1
Number of Followers: 19  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0219-7200 - ISSN (Online) 1757-6334
Published by World Scientific Homepage  [120 journals]
  • Denoising of scanning electron microscope images for biological
           ultrastructure enhancement

    • Free pre-print version: Loading...

      Authors: Sheng Chang, Lijun Shen, Linlin Li, Xi Chen, Hua Han
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure. However, due to the requirements of data throughput and electron dose of biological samples in the imaging process, the SEM image of biological samples is often occupied by noise which severely affects the observation of ultrastructure. Therefore, it is necessary to analyze and establish a noise model of SEM and propose an effective denoising algorithm that can preserve the ultrastructure. We first investigated the noise source of SEM images and introduced a signal-related SEM noise model. Then, we validated the effectiveness of the noise model through experiments, which are designed with standard samples to reflect the relation between real signal intensity and noise. Based on the SEM noise model and traditional variance stabilization denoising strategy, we proposed a novel, two-stage denoising method. In the first stage variance stabilization, our VS-Net realizes the separation of signal-dependent noise and signal in the SEM image. In the second stage denoising, our D-Net employs the structure of U-Net and combines the attention mechanism to achieve efficient noise removal. Compared with other existing denoising methods for SEM images, our proposed method is more competitive in objective evaluation and visual effects. Source code is available on GitHub (https://github.com/VictorCSheng/VSID-Net).
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-04-23T07:00:00Z
      DOI: 10.1142/S021972002250007X
       
  • DeepBtoD: Improved RNA-binding proteins prediction via integrated deep
           learning

    • Free pre-print version: Loading...

      Authors: XiuQuan Du, XiuJuan Zhao, YanPing Zhang
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis and identification of RBPs is an essential issue. However, although many computational methods have been developed for predicting RBPs, a few studies simultaneously consider local and global information from the perspective of the RNA sequence. Facing this challenge, we present a novel method called DeepBtoD, which predicts RBPs directly from RNA sequences. First, a [math]-BtoD encoding is designed, which takes into account the composition of [math]-nucleotides and their relative positions and forms a local module. Second, we designed a multi-scale convolutional module embedded with a self-attentive mechanism, the ms-focusCNN, which is used to further learn more effective, diverse, and discriminative high-level features. Finally, global information is considered to supplement local modules with ensemble learning to predict whether the target RNA binds to RBPs. Our preliminary 24 independent test datasets show that our proposed method can classify RBPs with the area under the curve of 0.933. Remarkably, DeepBtoD shows competitive results across seven state-of-the-art methods, suggesting that RBPs can be highly recognized by integrating local [math]-BtoD and global information only from RNA sequences. Hence, our integrative method may be useful to improve the power of RBPs prediction, which might be particularly useful for modeling protein-nucleic acid interactions in systems biology studies. Our DeepBtoD server can be accessed at http://175.27.228.227/DeepBtoD/.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-04-21T07:00:00Z
      DOI: 10.1142/S0219720022500068
       
  • Quantitative structure–activity relationship modeling reveals the
           minimal sequence requirement and amino acid preference of sirtuin-1’s
           deacetylation substrates in diabetes mellitus

    • Free pre-print version: Loading...

      Authors: X. Shao, W. Kong, Y. Li, S. Zhang
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide (NAD[math]-dependent deacetylase involved in multiple glucose metabolism pathways and plays an important role in the pathogenesis of diabetes mellitus (DM). The enzyme specifically recognizes its deacetylation substrates’ peptide segments containing a central acetyl-lysine residue as well as a number of amino acids flanking the central residue. In this study, we attempted to ascertain the minimal sequence requirement (MSR) around the central acetyl-lysine residue of SIRT1 substrate-recognition sites as well as the amino acid preference (AAP) at different residues of the MSR window through quantitative structure–activity relationship (QSAR) strategy, which would benefit our understanding of SIRT1 substrate specificity at the molecular level and is also helpful to rationally design substrate-mimicking peptidic agents against DM by competitively targeting SIRT1 active site. In this procedure, a large-scale dataset containing 6801 13-mer acetyl-lysine peptides (and their SIRT1-catalyized deacetylation activities) were compiled to train 10 QSAR regression models developed by systematic combination of machine learning methods (PLS and SVM) and five amino acids descriptors (DPPS, T-scale, MolSurf, [math]-score, and FASGAI). The two best QSAR models (PLS+FASGAI and SVM+DPPS) were then employed to statistically examine the contribution of residue positions to the deacetylation activity of acetyl-lysine peptide substrates, revealing that the MSR can be represented by 5-mer acetyl-lysine peptides that meet a consensus motif X[math]X[math]X[math](AcK)0X[math]. Structural analysis found that the X[math] and (AcK)0 residues are tightly packed against the enzyme active site and confer both stability and specificity for the enzyme–substrate complex, whereas the X[math], X[math] and X[math] residues are partially exposed to solvent but can also effectively stabilize the complex system. Subsequently, a systematic deacetylation activity change profile (SDACP) was created based on QSAR modeling, from which the AAP for each residue position of MSR was depicted. With the profile, we were able to rationally design an SDACP combinatorial library with promising deacetylation activity, from which nine MSR acetyl-lysine peptides as well as two known SIRT1 acetyl-lysine peptide substrates were tested by using SIRT1 deacetylation assay. It is revealed that the designed peptides exhibit a comparable or even higher activity than the controls, although the former is considerably shorter than the latter.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-04-21T07:00:00Z
      DOI: 10.1142/S0219720022500081
       
  • RPfam: A refiner towards curated-like multiple sequence alignments of the
           Pfam protein families

    • Free pre-print version: Loading...

      Authors: Qingting Wei, Hong Zou, Cuncong Zhong, Jianfeng Xu
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      High-quality multiple sequence alignments can provide insights into the architecture and function of protein families. The existing MSA tools often generate results inconsistent with biological distribution of conserved regions because of positioning amino acid residues and gaps only by symbols. We propose RPfam, a refiner towards curated-like MSAs for modeling the protein families in the Pfam database. RPfam refines the automatic alignments via scoring alignments based on the PFASUM matrix, restricting realignments within badly aligned blocks, optimizing the block scores by dynamic programming, and running refinements iteratively using the Simulated Annealing algorithm. Experiments show RPfam effectively refined the alignments produced by the MSA tools ClustalO and Muscle with reference to the curated seed alignments of the Pfam protein families. Especially RPfam improved the quality of the ClustalO alignments by 4.4% and the Muscle alignments by 2.8% on the gp32 DNA binding protein-like family. Supplementary Table is available at http://www.worldscinet.com/jbcb/.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-04-14T07:00:00Z
      DOI: 10.1142/S0219720022400029
       
  • Analysis to determine the effect of mutations on binding to small chemical
           molecules

    • Free pre-print version: Loading...

      Authors: T. V. Koshlan, K. G. Kulikov
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      In this paper, the authors present and describe, in detail, an original software-implemented numerical methodology used to determine the effect of mutations on binding to small chemical molecules, on the example of gefitinib, AMPPNP, CO-1686, ASP8273, erlotinib binding with EGFR protein, and imatinib binding with PPARgamma. Furthermore, the developed numerical approach makes it possible to determine the stability of a molecular complex, which consists of a protein and a small chemical molecule. The description of the software package that implements the presented algorithm is given in the website: https://binomlabs.com/.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-04-14T07:00:00Z
      DOI: 10.1142/S0219720022400030
       
  • RNA modification writers influence tumor microenvironment in gastric
           cancer and prospects of targeted drug therapy

    • Free pre-print version: Loading...

      Authors: Peng Song, Sheng Zhou, Xiaoyang Qi, Yuwen Jiao, Yu Gong, Jie Zhao, Haojun Yang, Zhifen Qian, Jun Qian, Liming Tang
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Background: RNA adenosine modifications are crucial for regulating RNA levels. N6-methyladenosine (m6A), N1-methyladenosine (m1A), adenosine-to-inosine RNA editing, and alternative polyadenylation (APA) are four major RNA modification types. Methods: We evaluated the altered mRNA expression profiles of 27 RNA modification enzymes and compared the differences in tumor microenvironment (TME) and clinical prognosis between two RNA modification patterns using unsupervised clustering. Then, we constructed a scoring system, WM_score, and quantified the RNA modifications in patients of gastric cancer (GC), associating WM_score with TME, clinical outcomes, and effectiveness of targeted therapies. Results: RNA adenosine modifications strongly correlated with TME and could predict the degree of TME cell infiltration, genetic variation, and clinical prognosis. Two modification patterns were identified according to high and low WM_scores. Tumors in the WM_score-high subgroup were closely linked with survival advantage, CD4[math] T-cell infiltration, high tumor mutation burden, and cell cycle signaling pathways, whereas those in the WM_score-low subgroup showed strong infiltration of inflammatory cells and poor survival. Regarding the immunotherapy response, a high WM_score showed a significant correlation with PD-L1 expression, predicting the effect of PD-L1 blockade therapy. Conclusion: The WM_scoring system could facilitate scoring and prediction of GC prognosis.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-03-14T07:00:00Z
      DOI: 10.1142/S0219720022500044
       
  • A sequence-based two-layer predictor for identifying enhancers and their
           strength through enhanced feature extraction

    • Free pre-print version: Loading...

      Authors: Santhosh Amilpur, Raju Bhukya
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Enhancers are short regulatory DNA fragments that are bound with proteins called activators. They are free-bound and distant elements, which play a vital role in controlling gene expression. It is challenging to identify enhancers and their strength due to their dynamic nature. Although some machine learning methods exist to accelerate identification process, their prediction accuracy and efficiency will need more improvement. In this regard, we propose a two-layer prediction model with enhanced feature extraction strategy which does feature combination from improved position-specific amino acid propensity (PSTKNC) method along with Enhanced Nucleic Acid Composition (ENAC) and Composition of k-spaced Nucleic Acid Pairs (CKSNAP). The feature sets from all three feature extraction approaches were concatenated and then sent through a simple artificial neural network (ANN) to accurately identify enhancers in the first layer and their strength in the second layer. Experiments are conducted on benchmark chromatin nine cell lines dataset. A 10-fold cross validation method is employed to evaluate model’s performance. The results show that the proposed model gives an outstanding performance with 94.50%, 0.8903 of accuracy and Matthew’s correlation coefficient (MCC) in predicting enhancers and fairly does well with independent test also when compared with all other existing methods.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-03-09T08:00:00Z
      DOI: 10.1142/S0219720022500056
       
  • Tensor decomposition based on the potential low-rank and [math]-shrinkage
           generalized threshold algorithm for analyzing cancer multiomics data

    • Free pre-print version: Loading...

      Authors: Hang-Jin Yang, Yu-Xia Lei, Juan Wang, Xiang-Zhen Kong, Jin-Xing Liu, Ying-Lian Gao
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Tensor Robust Principal Component Analysis (TRPCA) has achieved promising results in the analysis of genomics data. However, the TRPCA model under the existing tensor singular value decomposition ([math]-SVD) framework insufficiently extracts the potential low-rank structure of the data, resulting in suboptimal restored components. Simultaneously, the tensor nuclear norm (TNN) defined based on [math]-SVD uses the same standard to handle various singular values. TNN ignores the difference of singular values, leading to the failure of the main information that needs to be well preserved. To preserve the heterogeneous structure in the low-rank information, we propose a novel TNN and extend it to the TRPCA model. Potential low-rank space may contain important information. We learn the low-rank structural information from the core tensor. The singular value space contains the association information between genes and cancers. The [math]-shrinkage generalized threshold function is utilized to preserve the low-rank properties of larger singular values. The optimization problem is solved by the alternating direction method of the multiplier (ADMM) algorithm. Clustering and feature selection experiments are performed on the TCGA data set. The experimental results show that the proposed model is more promising than other state-of-the-art tensor decomposition methods.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-02-21T08:00:00Z
      DOI: 10.1142/S0219720022500020
       
  • A protein succinylation sites prediction method based on the hybrid
           architecture of LSTM network and CNN

    • Free pre-print version: Loading...

      Authors: Die Zhang, Shunfang Wang
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      The succinylation modification of protein participates in the regulation of a variety of cellular processes. Identification of modified substrates with precise sites is the basis for understanding the molecular mechanism and regulation of succinylation. In this work, we picked and chose five superior feature codes: CKSAAP, ACF, BLOSUM62, AAindex, and one-hot, according to their performance in the problem of succinylation sites prediction. Then, LSTM network and CNN were used to construct four models: LSTM-CNN, CNN-LSTM, LSTM, and CNN. The five selected features were, respectively, input into each of these four models for training to compare the four models. Based on the performance of each model, the optimal model among them was chosen to construct a hybrid model DeepSucc that was composed of five sub-modules for integrating heterogeneous information. Under the 10-fold cross-validation, the hybrid model DeepSucc achieves 86.26% accuracy, 84.94% specificity, 87.57% sensitivity, 0.9406 AUC, and 0.7254 MCC. When compared with other prediction tools using an independent test set, DeepSucc outperformed them in sensitivity and MCC. The datasets and source codes can be accessed at https://github.com/1835174863zd/DeepSucc.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-02-21T08:00:00Z
      DOI: 10.1142/S0219720022500032
       
  • Clarifying real receptor binding site between coronavirus HCoV-HKU1 and
           9-O-Ac-Sia based on molecular docking

    • Free pre-print version: Loading...

      Authors: Xiaoyu Liu, Jingying Zhao, Sicong Li, Cai Wei, Shihang Wang, Xuanyu Xu, Yin Zheng, Xiangyu Deng, Wenliang Yuan, Xiaomin Zeng, Sihua Peng
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      HCoV-HKU1 is a [math]-coronavirus with low pathogenicity, which usually leads to respiratory diseases. At present, a controversial issue is that whether the receptor binding site (RBS) of HCoV-HKU1 is located in the N-terminal domain (NTD) or the C-terminal domain (CTD) in the HCoV-HKU1 S protein. To address this issue, we used molecular docking technology to dock the NTD and CTD with 9-oxoacetylated sialic acid (9-O-Ac-Sia), respectively, with the results showing that the RBS of HCoV-HKU1 is located in the NTD (amino acid residues 80–95, 25–32). Our findings clarified the structural basis and molecular mechanism of the HCoV-HKU1 infection, providing important information for the development of therapeutic antibody drugs and the design of vaccines.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-01-20T08:00:00Z
      DOI: 10.1142/S0219720021500347
       
  • Female reproduction-specific proteins, origins in marine species, and
           their evolution in the animal kingdom

    • Free pre-print version: Loading...

      Authors: Laura Rebeca Jimenez-Gutierrez
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      The survival of a species largely depends on the ability of individuals to reproduce, thus perpetuating their life history. The advent of metazoans (i.e. pluricellular animals) brought about the evolution of specialized tissues and organs, which in turn led to the development of complex protein regulatory pathways. This study sought to elucidate the evolutionary relationships between female reproduction-associated proteins by analyzing the transcriptomes of representative species from a selection of marine invertebrate phyla. Our study identified more than 50 reproduction-related genes across a wide evolutionary spectrum, from Porifera to Vertebrata. Among these, a total of 19 sequences had not been previously reported in at least one phylum, particularly in Porifera. Moreover, most of the structural differences between these proteins did not appear to be determined by environmental pressures or reproductive strategies, but largely obeyed a distinguishable evolutionary pattern from sponges to mammals.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-01-12T08:00:00Z
      DOI: 10.1142/S0219720022400017
       
  • Optimized splitting of mixed-species RNA sequencing data

    • Free pre-print version: Loading...

      Authors: Xuan Song, Hai Yun Gao, Karl Herrup, Ronald P. Hart
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Gene expression studies using xenograft transplants or co-culture systems, usually with mixed human and mouse cells, have proven to be valuable to uncover cellular dynamics during development or in disease models. However, the mRNA sequence similarities among species presents a challenge for accurate transcript quantification. To identify optimal strategies for analyzing mixed-species RNA sequencing data, we evaluate both alignment-dependent and alignment-independent methods. Alignment of reads to a pooled reference index is effective, particularly if optimal alignments are used to classify sequencing reads by species, which are re-aligned with individual genomes, generating [math] accuracy across a range of species ratios. Alignment-independent methods, such as convolutional neural networks, which extract the conserved patterns of sequences from two species, classify RNA sequencing reads with over 85% accuracy. Importantly, both methods perform well with different ratios of human and mouse reads. While non-alignment strategies successfully partitioned reads by species, a more traditional approach of mixed-genome alignment followed by optimized separation of reads proved to be the more successful with lower error rates.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2022-01-06T08:00:00Z
      DOI: 10.1142/S0219720022500019
       
  • EdClust: A heuristic sequence clustering method with higher sensitivity

    • Free pre-print version: Loading...

      Authors: Ming Cao, Qinke Peng, Ze-Gang Wei, Fei Liu, Yi-Fan Hou
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[math] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-12-23T08:00:00Z
      DOI: 10.1142/S0219720021500360
       
  • Clinical drug response prediction from preclinical cancer cell lines by
           logistic matrix factorization approach

    • Free pre-print version: Loading...

      Authors: Akram Emdadi, Changiz Eslahchi
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-12-17T08:00:00Z
      DOI: 10.1142/S0219720021500359
       
  • Identifying duplications and lateral gene transfers simultaneously and
           rapidly

    • Free pre-print version: Loading...

      Authors: Zhi-Zhong Chen, Fei Deng, Lusheng Wang
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      This paper deals with the problem of enumerating all minimum-cost LCA-reconciliations involving gene duplications and lateral gene transfers (LGTs) for a given species tree [math] and a given gene tree [math]. Previously, [Tofigh A, Hallett M, Lagergren J, Simultaneous identification of duplications and lateral gene transfers, IEEE/ACM Trans Comput Biol Bioinf 517–535, 2011.] gave a fixed-parameter algorithm for this problem that runs in [math] time, where [math] is the number of vertices in [math], [math] is the number of vertices in [math], and [math] is the minimum cost of an LCA-reconciliation between [math] and [math]. In this paper, by refining their algorithm, we obtain a new one for the same problem that finds and outputs the solutions in a compact form within [math] time. In the most interesting case where [math], our algorithm is [math] times faster.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-12-09T08:00:00Z
      DOI: 10.1142/S0219720021500335
       
  • Identification of cancer-related module in protein–protein interaction
           network based on gene prioritization

    • Free pre-print version: Loading...

      Authors: Jingli Wu, Qi Zhang, Gaoshi Li
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      With the rapid development of deep sequencing technologies, a large amount of high-throughput data has been available for studying the carcinogenic mechanism at the molecular level. It has been widely accepted that the development and progression of cancer are regulated by modules/pathways rather than individual genes. The investigation of identifying cancer-related active modules has received an extensive attention. In this paper, we put forward an identification method ModFinder by integrating both biological networks and gene expression profiles. More concretely, a gene scoring function is devised by using the regression model with [math]-step random walk kernel, and the genes are ranked according to both of their active scores and degrees in the PPI network. Then a greedy algorithm NSEA is introduced to find an active module with high score and strong connectivity. Experiments were performed on both simulated data and real biological one, i.e. breast cancer and cervical cancer. Compared with the previous methods SigMod, LEAN and RegMod, ModFinder shows competitive performance. It can successfully identify a well-connected module that contains a large proportion of cancer-related genes, including some well-known oncogenes or tumor suppressors enriched in cancer-related pathways.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-12-03T08:00:00Z
      DOI: 10.1142/S0219720021500311
       
  • O-glycosylation site prediction for Homo sapiens by combining properties
           and sequence features with support vector machine

    • Free pre-print version: Loading...

      Authors: Yan Zhu, Shuwan Yin, Jia Zheng, Yixia Shi, Cangzhi Jia
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal–Wallis (K–W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-11-19T08:00:00Z
      DOI: 10.1142/S0219720021500293
       
  • A new Bayesian approach for QTL mapping of family data

    • Free pre-print version: Loading...

      Authors: Daiane Aparecida Zuanetti, Luis Aparecido Milan
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs’ effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents’ genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs’ position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-11-19T08:00:00Z
      DOI: 10.1142/S021972002150030X
       
  • Mining sponge phenomena in RNA expression data

    • Free pre-print version: Loading...

      Authors: Fabrizio Angiulli, Teresa Colombo, Fabio Fassetti, Angelo Furfaro, Paola Paci
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      In the last few years, the interactions among competing endogenous RNAs (ceRNAs) have been recognized as a key post-transcriptional regulatory mechanism in cell differentiation, tissue development, and disease. Notably, such sponge phenomena substracting active microRNAs from their silencing targets have been recognized as having a potential oncosuppressive, or oncogenic, role in several cancer types. Hence, the ability to predict sponges from the analysis of large expression data sets (e.g. from international cancer projects) has become an important data mining task in bioinformatics. We present a technique designed to mine sponge phenomena whose presence or absence may discriminate between healthy and unhealthy populations of samples in tumoral or normal expression data sets, thus providing lists of candidates potentially relevant in the pathology. With this aim, we search for pairs of elements acting as ceRNA for a given miRNA, namely, we aim at discovering miRNA-RNA pairs involved in phenomena which are clearly present in one population and almost absent in the other one. The results on tumoral expression data, concerning five different cancer types, confirmed the effectiveness of the approach in mining interesting knowledge. Indeed, 32 out of 33 miRNAs and 22 out of 25 protein-coding genes identified as top scoring in our analysis are corroborated by having been similarly associated with cancer processes in independent studies. In fact, the subset of miRNAs selected by the sponge analysis results in a significant enrichment of annotation for the KEGG32 pathway “microRNAs in cancer” when tested with the commonly used bioinformatic resource DAVID. Moreover, often the cancer datasets where our sponge analysis identified a miRNA as top scoring match the one reported already in the pertaining literature.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-11-18T08:00:00Z
      DOI: 10.1142/S0219720021500220
       
  • Amino acid environment affinity model based on graph attention network

    • Free pre-print version: Loading...

      Authors: Xueheng Tong, Shuqi Liu, Jiawei Gu, Chunguo Wu, Yanchun Liang, Xiaohu Shi
      Abstract: Journal of Bioinformatics and Computational Biology, Ahead of Print.
      Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that can be identified by three-dimensional locations and local neighborhoods in which the structure or function exists. Understanding the amino acid environment affinity is essential for additional protein structural or functional studies, such as mutation analysis and functional site detection. In this study, an amino acid environment affinity model based on the graph attention network was developed. Initially, we constructed a protein graph according to the distance between amino acid pairs. Then, we extracted a set of structural features for each node. Finally, the protein graph and the associated node feature set were set to input the graph attention network model and to obtain the amino acid affinities. Numerical results show that our proposed method significantly outperforms a recent 3DCNN-based method by almost 30%.
      Citation: Journal of Bioinformatics and Computational Biology
      PubDate: 2021-11-13T08:00:00Z
      DOI: 10.1142/S0219720021500323
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.236.50.79
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-