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BIOTECHNOLOGY (244 journals)                  1 2 | Last

Showing 1 - 200 of 244 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 8)
Advanced Biomedical Research     Open Access  
Advances in Bioscience and Biotechnology     Open Access   (Followers: 17)
Advances in Genetic Engineering & Biotechnology     Hybrid Journal   (Followers: 9)
Advances in Regenerative Medicine     Open Access   (Followers: 3)
African Journal of Biotechnology     Open Access   (Followers: 6)
Algal Research     Partially Free   (Followers: 11)
American Journal of Biochemistry and Biotechnology     Open Access   (Followers: 69)
American Journal of Bioinformatics Research     Open Access   (Followers: 7)
American Journal of Polymer Science     Open Access   (Followers: 33)
Amylase     Open Access  
Anadolu University Journal of Science and Technology : C Life Sciences and Biotechnology     Open Access  
Animal Biotechnology     Hybrid Journal   (Followers: 8)
Annales des Sciences Agronomiques     Full-text available via subscription  
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 45)
Applied Biosafety     Hybrid Journal  
Applied Food Biotechnology     Open Access   (Followers: 3)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 67)
Applied Mycology and Biotechnology     Full-text available via subscription   (Followers: 4)
Arthroplasty Today     Open Access   (Followers: 1)
Artificial Cells, Nanomedicine and Biotechnology     Hybrid Journal   (Followers: 1)
Asia Pacific Biotech News     Hybrid Journal   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 9)
Asian Pacific Journal of Tropical Biomedicine     Open Access   (Followers: 2)
Australasian Biotechnology     Full-text available via subscription   (Followers: 1)
Banat's Journal of Biotechnology     Open Access  
BBR : Biochemistry and Biotechnology Reports     Open Access   (Followers: 5)
Beitr?ge zur Tabakforschung International/Contributions to Tobacco Research     Open Access   (Followers: 3)
Bio-Algorithms and Med-Systems     Hybrid Journal   (Followers: 2)
Bio-Research     Full-text available via subscription   (Followers: 4)
Bioactive Materials     Open Access   (Followers: 1)
Biocatalysis and Agricultural Biotechnology     Hybrid Journal   (Followers: 4)
Biocybernetics and Biological Engineering     Full-text available via subscription   (Followers: 5)
Bioethics UPdate     Hybrid Journal   (Followers: 1)
Biofuels     Hybrid Journal   (Followers: 11)
Biofuels Engineering     Open Access   (Followers: 1)
Biological & Pharmaceutical Bulletin     Full-text available via subscription   (Followers: 4)
Biological Cybernetics     Hybrid Journal   (Followers: 10)
Biomarkers and Genomic Medicine     Open Access   (Followers: 3)
Biomaterials Research     Open Access   (Followers: 4)
BioMed Research International     Open Access   (Followers: 4)
Biomédica     Open Access  
Biomedical and Biotechnology Research Journal     Open Access  
Biomedical Engineering Research     Open Access   (Followers: 6)
Biomedical Glasses     Open Access  
Biomedical Reports     Full-text available via subscription  
BioMedicine     Open Access  
Biomedika     Open Access  
Bioprinting     Hybrid Journal   (Followers: 1)
Bioresource Technology Reports     Hybrid Journal   (Followers: 1)
Bioscience, Biotechnology, and Biochemistry     Hybrid Journal   (Followers: 21)
Biosensors Journal     Open Access  
Biosimilars     Open Access   (Followers: 1)
Biosurface and Biotribology     Open Access  
Biotechnic and Histochemistry     Hybrid Journal   (Followers: 1)
BioTechniques : The International Journal of Life Science Methods     Full-text available via subscription   (Followers: 28)
Biotechnologia Acta     Open Access   (Followers: 1)
Biotechnologie, Agronomie, Société et Environnement     Open Access   (Followers: 2)
Biotechnology     Open Access   (Followers: 8)
Biotechnology & Biotechnological Equipment     Open Access   (Followers: 4)
Biotechnology Advances     Hybrid Journal   (Followers: 34)
Biotechnology and Applied Biochemistry     Hybrid Journal   (Followers: 44)
Biotechnology and Bioengineering     Hybrid Journal   (Followers: 160)
Biotechnology and Bioprocess Engineering     Hybrid Journal   (Followers: 6)
Biotechnology and Genetic Engineering Reviews     Hybrid Journal   (Followers: 13)
Biotechnology and Health Sciences     Open Access   (Followers: 1)
Biotechnology and Molecular Biology Reviews     Open Access   (Followers: 2)
Biotechnology Annual Review     Full-text available via subscription   (Followers: 5)
Biotechnology for Biofuels     Open Access   (Followers: 10)
Biotechnology Frontier     Open Access   (Followers: 2)
Biotechnology Journal     Hybrid Journal   (Followers: 17)
Biotechnology Law Report     Hybrid Journal   (Followers: 4)
Biotechnology Letters     Hybrid Journal   (Followers: 34)
Biotechnology Progress     Hybrid Journal   (Followers: 41)
Biotechnology Reports     Open Access  
Biotechnology Research International     Open Access   (Followers: 1)
Biotechnology Techniques     Hybrid Journal   (Followers: 10)
Biotecnología Aplicada     Open Access  
Bioteknologi (Biotechnological Studies)     Open Access  
BIOTIK : Jurnal Ilmiah Biologi Teknologi dan Kependidikan     Open Access  
Biotribology     Hybrid Journal   (Followers: 1)
BMC Biotechnology     Open Access   (Followers: 17)
Cell Biology and Development     Open Access  
Chinese Journal of Agricultural Biotechnology     Full-text available via subscription   (Followers: 4)
Communications in Mathematical Biology and Neuroscience     Open Access  
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computer Methods and Programs in Biomedicine     Hybrid Journal   (Followers: 8)
Copernican Letters     Open Access   (Followers: 1)
Critical Reviews in Biotechnology     Hybrid Journal   (Followers: 20)
Crop Breeding and Applied Biotechnology     Open Access   (Followers: 3)
Current Bionanotechnology     Hybrid Journal  
Current Biotechnology     Hybrid Journal   (Followers: 4)
Current Opinion in Biomedical Engineering     Hybrid Journal   (Followers: 1)
Current Opinion in Biotechnology     Hybrid Journal   (Followers: 55)
Current Pharmaceutical Biotechnology     Hybrid Journal   (Followers: 9)
Current Research in Bioinformatics     Open Access   (Followers: 13)
Current Trends in Biotechnology and Chemical Research     Open Access   (Followers: 3)
Current trends in Biotechnology and Pharmacy     Open Access   (Followers: 8)
DNA and RNA Nanotechnology     Open Access  
EBioMedicine     Open Access  
Electronic Journal of Biotechnology     Open Access  
Entomologia Generalis     Full-text available via subscription   (Followers: 1)
Environmental Science : Processes & Impacts     Full-text available via subscription   (Followers: 4)
Experimental Biology and Medicine     Hybrid Journal   (Followers: 3)
Folia Medica Indonesiana     Open Access  
Food Bioscience     Hybrid Journal  
Food Biotechnology     Hybrid Journal   (Followers: 9)
Food Science and Biotechnology     Hybrid Journal   (Followers: 8)
Frontiers in Bioengineering and Biotechnology     Open Access   (Followers: 6)
Frontiers in Systems Biology     Open Access   (Followers: 2)
Fungal Biology and Biotechnology     Open Access   (Followers: 2)
GM Crops and Food: Biotechnology in Agriculture and the Food Chain     Full-text available via subscription   (Followers: 1)
GSTF Journal of BioSciences     Open Access  
HAYATI Journal of Biosciences     Open Access  
Horticultural Biotechnology Research     Open Access  
Horticulture, Environment, and Biotechnology     Hybrid Journal   (Followers: 11)
IEEE Transactions on Molecular, Biological and Multi-Scale Communications     Hybrid Journal   (Followers: 1)
IET Nanobiotechnology     Hybrid Journal   (Followers: 2)
IN VIVO     Full-text available via subscription   (Followers: 4)
Indian Journal of Biotechnology (IJBT)     Open Access   (Followers: 2)
Indonesia Journal of Biomedical Science     Open Access   (Followers: 2)
Indonesian Journal of Biotechnology     Open Access   (Followers: 1)
Indonesian Journal of Medicine     Open Access  
Industrial Biotechnology     Hybrid Journal   (Followers: 18)
International Biomechanics     Open Access  
International Journal of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 14)
International Journal of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 4)
International Journal of Biomedical Research     Open Access   (Followers: 2)
International Journal of Biotechnology     Hybrid Journal   (Followers: 5)
International Journal of Biotechnology and Molecular Biology Research     Open Access   (Followers: 4)
International Journal of Biotechnology for Wellness Industries     Partially Free   (Followers: 1)
International Journal of Environment, Agriculture and Biotechnology     Open Access   (Followers: 5)
International Journal of Functional Informatics and Personalised Medicine     Hybrid Journal   (Followers: 4)
International Journal of Medicine and Biomedical Research     Open Access   (Followers: 1)
International Journal of Nanotechnology and Molecular Computation     Full-text available via subscription   (Followers: 3)
International Journal of Radiation Biology     Hybrid Journal   (Followers: 4)
Iranian Journal of Biotechnology     Open Access  
ISABB Journal of Biotechnology and Bioinformatics     Open Access  
Italian Journal of Food Science     Open Access   (Followers: 1)
JMIR Biomedical Engineering     Open Access  
Journal of Biometrics & Biostatistics     Open Access   (Followers: 3)
Journal of Bioterrorism & Biodefense     Open Access   (Followers: 6)
Journal of Petroleum & Environmental Biotechnology     Open Access   (Followers: 1)
Journal of Advanced Therapies and Medical Innovation Sciences     Open Access  
Journal of Advances in Biotechnology     Open Access   (Followers: 5)
Journal Of Agrobiotechnology     Open Access  
Journal of Analytical & Bioanalytical Techniques     Open Access   (Followers: 7)
Journal of Animal Science and Biotechnology     Open Access   (Followers: 4)
Journal of Applied Biomedicine     Open Access   (Followers: 2)
Journal of Applied Biotechnology     Open Access   (Followers: 2)
Journal of Applied Biotechnology Reports     Open Access   (Followers: 2)
Journal of Applied Mathematics & Bioinformatics     Open Access   (Followers: 5)
Journal of Biologically Active Products from Nature     Hybrid Journal   (Followers: 1)
Journal of Biomaterials and Nanobiotechnology     Open Access   (Followers: 6)
Journal of Biomedical Photonics & Engineering     Open Access  
Journal of Biomedical Practitioners     Open Access  
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription  
Journal of Bioprocessing & Biotechniques     Open Access  
Journal of BioScience and Biotechnology     Open Access  
Journal of Biosecurity Biosafety and Biodefense Law     Hybrid Journal   (Followers: 3)
Journal of Biotechnology     Hybrid Journal   (Followers: 63)
Journal of Biotechnology and Strategic Health Research     Open Access   (Followers: 1)
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 9)
Journal of Chitin and Chitosan Science     Full-text available via subscription   (Followers: 1)
Journal of Colloid Science and Biotechnology     Full-text available via subscription  
Journal of Commercial Biotechnology     Full-text available via subscription   (Followers: 6)
Journal of Crop Science and Biotechnology     Hybrid Journal   (Followers: 3)
Journal of Ecobiotechnology     Open Access  
Journal of Essential Oil Research     Hybrid Journal   (Followers: 2)
Journal of Experimental Biology     Full-text available via subscription   (Followers: 25)
Journal of Genetic Engineering and Biotechnology     Open Access   (Followers: 5)
Journal of Ginseng Research     Open Access  
Journal of Industrial Microbiology and Biotechnology     Hybrid Journal   (Followers: 18)
Journal of Integrative Bioinformatics     Open Access  
Journal of Medical Imaging and Health Informatics     Full-text available via subscription  
Journal of Molecular Biology and Biotechnology     Open Access  
Journal of Molecular Microbiology and Biotechnology     Full-text available via subscription   (Followers: 13)
Journal of Nano Education     Full-text available via subscription  
Journal of Nanobiotechnology     Open Access   (Followers: 4)
Journal of Nanofluids     Full-text available via subscription   (Followers: 1)
Journal of Organic and Biomolecular Simulations     Open Access  
Journal of Plant Biochemistry and Biotechnology     Hybrid Journal   (Followers: 4)
Journal of Science and Applications : Biomedicine     Open Access  
Journal of the Mechanical Behavior of Biomedical Materials     Hybrid Journal   (Followers: 13)
Journal of Trace Elements in Medicine and Biology     Hybrid Journal   (Followers: 1)
Journal of Tropical Microbiology and Biotechnology     Full-text available via subscription  
Journal of Yeast and Fungal Research     Open Access   (Followers: 1)
Marine Biotechnology     Hybrid Journal   (Followers: 4)
Meat Technology     Open Access  
Messenger     Full-text available via subscription  
Metabolic Engineering Communications     Open Access   (Followers: 4)
Metalloproteinases In Medicine     Open Access  
Microbial Biotechnology     Open Access   (Followers: 10)
MicroMedicine     Open Access   (Followers: 3)
Molecular and Cellular Biomedical Sciences     Open Access   (Followers: 1)
Molecular Biotechnology     Hybrid Journal   (Followers: 13)
Molecular Genetics and Metabolism Reports     Open Access   (Followers: 3)
Nanobiomedicine     Open Access  
Nanobiotechnology     Hybrid Journal   (Followers: 2)

        1 2 | Last

Journal Cover
Computer Methods and Programs in Biomedicine
Journal Prestige (SJR): 0.786
Citation Impact (citeScore): 3
Number of Followers: 8  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0169-2607
Published by Elsevier Homepage  [3155 journals]
  • Automatic quantification of the LV function and mass: a deep learning
           approach for cardiovascular MRI
    • Abstract: Publication date: Available online 12 December 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Ariel H. Curiale, Flavio D. Colavecchia, German Mato Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance as optimization objective function.Results: The CNNs were trained and evaluated with 140 patients from two public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas Project) by using a 5-fold cross-validation strategy. Our results demonstrate a suitable accuracy for myocardial segmentation ( ∼ 0.9 Dice’s coefficient), and a strong correlation with the most relevant physiological measures: 0.99 for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass, 0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac output.Conclusion: Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN to estimate different structural and functional features such as LV mass and EF which are commonly used for both diagnosis and treatment of different pathologies.Significance: This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring. Also, this approach may have important applications on motion quantification.
  • An Optimal Strategy for Prediction of Sudden Cardiac Death Through a
           Pioneering Feature-Selection Approach from HRV Signal
    • Abstract: Publication date: Available online 7 December 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Elias Ebrahimzadeh, Alireza Foroutan, Mohammad Shams, Raheleh Baradaran, Lila Rajabion, Mohammadamin Joulani, Farahnaz Fayaz Background and ObjectiveTaking into consideration the critical importance of Sudden Cardiac Death (SCD), as it could be the first and the last heart condition to be diagnosed in a person while continuing to claim millions of lives around the world, prediction of sudden cardiac death has increasingly been regarded as a matter of substantive significance. This study does not seek to once again define features to predict and detect SCD, as there already has been adequate discussion addressing feature extraction in our previous works and other recent studies. What seems to be lacking attention is the need for an appropriate strategy to manage the extracted features to such an extent that the best classification is presented. To this end, deploying a suitable tactic to select extracted features could bring about outstanding results compared to other works in the literature.MethodsThis research has accordingly applied a novel and automated approach to Local Feature Subset Selection through the most rigorous methodologies, which have formerly been developed in previous works of this team, for extracting features from nonlinear, time-frequency and classical processes. We are therefore enabled to select features that differ from one another in each minute before the incident through the agency of optimal feature selection in each one-minute interval of the signal.ResultsUsing the proposed algorithm, SCD can be predicted 13 minutes before the onset, thus, better results are achieved compared to other techniques proposed in this field. Additionally, through defining a Utility Function and employing statistical analysis the alarm threshold has been effectively determined as 84% for the prediction accuracy. Having selected the best combination of features, based on their ability to generate the highest classification accuracy, the two classes are classified by means of the Multilayer Perceptron (MLP), K- Nearest Neighbor (KNN) the Support Vector Machine (SVM), and the Mixture of Expert (ME) classifier. The Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the MIT-BIH database which led to sensitivity, specificity, and accuracy of 84.24%, 85.71%, and 82.85% respectively for thirtheen one-minute.ConclusionsThe outcome of the obtained prediction would be analyzed and compared to other results. The most applicable and effective features would subsequently be presented according to the number of times they have been chosen. Finally, principal features in each time interval are discussed and the importance of each type of processing will be drawn into focus. The results indicate the significant capacity of the proposed method for predicting SCD from Electrocardoigram (ECG) signals as well as selecting the appropriate processing method at any time before the incident.
  • A happiness degree predictor using the conceptual data structure for deep
           learning architectures
    • Abstract: Publication date: January 2019Source: Computer Methods and Programs in Biomedicine, Volume 168Author(s): Francisco Javier Pérez-Benito, Patricia Villacampa-Fernández, J. Alberto Conejero, Juan M. García-Gómez, Esperanza Navarro-Pardo Background and Objective: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires.Methods: A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining a HDP in which the network architecture enables the conceptual interpretation of psychological factors associated to happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence.Materials: A cross-sectional survey targeting non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors.Results: Our D-SDNN approach provided a better outcome (MSE: 1.46·10−2) than MLR (MSE: 2.30·10−2), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure.Conclusions: We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degree through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship.
  • Deep into Patient care: An automated deep learning approach for reshaping
           patient care in clinical setting
    • Abstract: Publication date: January 2019Source: Computer Methods and Programs in Biomedicine, Volume 168Author(s): Hsuan-Chia Yang, Tahmina Nasrin Poly, Yu-Chuan (Jack) Li
  • White blood cells identification system based on convolutional deep neural
           learning networks
    • Abstract: Publication date: January 2019Source: Computer Methods and Programs in Biomedicine, Volume 168Author(s): A.I. Shahin, Yanhui Guo, K.M. Amin, Amr A. Sharawi Background and objectivesWhite blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated.MethodsIn this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called “WBCsNet” is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network.ResultsDuring our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one.Conclusiona novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network.Graphical abstractImage, graphical abstract
  • Computer Aided Diagnosis System for Multiple Sclerosis Disease based on
           Phase to Amplitude Coupling in Covert Visual Attention
    • Abstract: Publication date: Available online 24 November 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Amirmasoud Ahmadi, Saeideh Davoudi, Mohammad Reza Daliri Background and ObjectiveComputer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. It is worth noting that research of this kind on the efficacy of attention tasks is limited in scope for MS patients; therefore, it is vital to develop a feature of EEG to characterize the patient's state with high sensitivity and specificity.MethodsWe evaluated the use of phase–amplitude coupling (PAC) of EEG signals to diagnose MS. It is assumed that the role of PAC for information encoding during visual attention in MS is greatly unknown; therefore, we made an attempt to investigate it via CAD systems. The EEG signals were recorded from healthy and MS patients while performing new visual attention tasks. Machine learning algorithms were also used to identify the EEG signals as to whether the disease existed or not. The challenge regarding the dimensionality of the extracted features was addressed through selecting the relevant and efficient features using T-test and Bhattacharyya distance criteria, and the validity of the system was assessed through leave-one-subject-out cross-validation method.ResultsOur findings indicated that online sequential extreme learning machine (OS-ELM) classifier with T-test feature selection method yielded peak accuracy, sensitivity and specificity in both color and direction tasks. These values were 91%, 83% and 96% for color task, and 90%, 82% and 96% for the direction task.ConclusionsBased on the results, it can be concluded that this procedure can be used for the automatic diagnosis of early MS, and can also facilitate the treatment assessment in patients.
  • Automated real-time method for ventricular heartbeat classification
    • Abstract: Publication date: Available online 20 November 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Silvia Ortín, Miguel C. Soriano, Miquel Alfaras, Claudio R. Mirasso Background and Objective: In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity. Methods: The proposed method uses an Echo State Network (ESN) to classify ECG signals following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations with an inter-patient scheme. To achieve real-time classification, the classifier itself and the feature extraction approach are fast and computationally efficient. In addition, our approach allows transferring the knowledge from one database to another without additional training. Results: The classification performance of the proposed model is validated on the MIT-BIH arrhythmia and INCART databases. The sensitivity and precision of the proposed method for MIT-BIH arrhythmia database are 95.3 and 88.8 for the modified lead II and 90.9 and 89.2 for the V1 lead. The results reported are further compared to the existing methodologies in literature. Our methodology is a competitive single lead ventricular heartbeat classifier, that is comparable to state-of-the-art algorithms using multiple leads. Conclusions: The proposed fully automated, single-lead and real-time heartbeat classifier of ventricular heartbeats reports an improved classification accuracy in different leads of the evaluated databases in comparison with other single lead heartbeat classifiers. These results open the possibility of applying our methodology to wearable long-term monitoring devices with an unconventional placement of the electrodes.
  • Predicting Combinative Drug Pairs via Multiple Classifier System with
           Positive Samples Only
    • Abstract: Publication date: Available online 15 November 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Jian-Yu Shi, Jia-Xin Li, Kui-Tao Mao, Jiang-Bo Cao, Peng Lei, Hui-Meng Lu, Siu-Ming Yiu Background and Objective: Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice.Methods: To address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS.Results: The comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways.Conclusions: The proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.
  • Analyzing hedyotis diffusa mechanisms of action from the genomics
    • Abstract: Publication date: Available online 31 October 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Chen-Jun Hu, Ju He, Guo-Zheng Li, Pei-Pei Fang, Jia-Dong Xie, You-Wei Ding, Yu-Qing Mao, Kong-Fa Hu Background and objectiveHedyotis diffusa is an herb used for anti-cancer, anti-oxidant, anti-inflammatory, and anti-fibroblast treatment in the clinical practice of Traditional Chinese Medicine. However, its pharmacological mechanisms have not been fully established and there is a lack of modern scientific verification. One of the best ways to further understand Hedyotis diffusa's mechanisms of action is to analyze it from the genomics perspective.MethodsIn this study, we used network pharmacology approaches to infer the herb-gene interactions, the herb-pathway interactions, and the gene families. We then analyzed Hedyotis diffusa's mechanisms of action using the genomics context combined with the Traditional Chinese Medicine clinical practice and the pharmacological research.ResultsThe results obtained in the pathway and gene family analysis were consistent with the Traditional Chinese Medicine clinical experience and the pharmacological activities of Hedyotis diffusa.ConclusionsOur approach can identify related genes and pathways correctly with little a priori knowledge, and provide potential directions to facilitate further research.
  • mQC: A post-mapping data exploration tool for ribosome profiling
    • Abstract: Publication date: Available online 28 October 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Steven Verbruggen, Gerben Menschaert Background and objectiveRibosome profiling is a recent next generation sequencing technique enabling the genome-wide study of gene expression in biomedical research at the translation level. Too often, researchers precipitously start trying to test their hypotheses after alignment of their data, without checking the quality and the general features of their mapped data. Despite the fact that these checks are essential to prevent errors and ensure valid conclusions afterwards, easy-to-use tools for visualizing the quality and overall outlook of mapped ribosome profiling data are lacking.MethodsWe present mQC, a modular tool implemented as a Bioconda package and also available in the Galaxy tool shed. Herewith both bio-informaticians as well as non-experts can easily perform the indispensable visualization of both the quality and the general features of their mapped P-site corrected ribosome profiling reads. The user manual, the raw code and more information can be found on its GitHub repository ( was tested on multiple datasets to assess its general applicability and was compared to other tools that partly perform similar tasks.ConclusionsOur results demonstrate that mQC can accomplish an unfilled but essential position in the ribosome profiling data analysis procedure by performing a thorough RIBO-Seq-specific exploration of aligned and P-site corrected ribosome profiling data.
  • compound.Cox: univariate feature selection and compound covariate for
           predicting survival
    • Abstract: Publication date: Available online 27 October 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Takeshi Emura, Shigeyuki Matsui, Hsuan-Yu Chen Background and objective: Univariate feature selection is one of the simplest and most commonly used techniques to develop a multigene predictor for survival. Presently, there is no software tailored to perform univariate feature selection and predictor construction.Methods: We develop the compound.Cox R package that implements univariate significance tests (via the Wald tests or score tests) for feature selection. We provide a cross-validation algorithm to measure predictive capability of selected genes and a permutation algorithm to assess the false discovery rate. We also provide three algorithms for constructing a multigene predictor (compound covariate, compound shrinkage, and copula-based methods), which are tailored to the subset of genes obtained from univariate feature selection. We demonstrate our package using survival data on the lung cancer patients. We examine the predictive capability of the developed algorithms by the lung cancer data and simulated data.Results: The developed R package, compound.Cox, is available on the CRAN repository. The statistical tools in compound.Cox allow researchers to determine an optimal significance level of the tests, thus providing researchers an optimal subset of genes for prediction. The package also allows researchers to compute the false discovery rate and various prediction algorithms.Graphical abstractImage, graphical abstract
  • End-to-End Syndrome Differentiation of Yin Deficiency and Yang Deficiency
           in Traditional Chinese Medicine
    • Abstract: Publication date: Available online 16 October 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Qinan Hu, Tong Yu, Jinghua Li, Qi Yu, Ling Zhu, Yueguo Gu Background and Objective Yin and Yang, two concepts adapted from classical Chinese philosophy, play a diagnostic role in Traditional Chinese Medicine (TCM). The Yin and Yang in harmonious balance indicate health, whereas imbalances to either side indicate unhealthiness, which may result in diseases. Yin-yang disharmony is considered to be the cause of pathological changes.Syndrome differentiation of yin-yang is crucial to clinical diagnosis. It lays a foundation for subsequent medical judgments, including therapeutic methods, and formula, among many others. However, because of the complexities of the mechanisms and manifestations of disease, it is difficult to exactly point out which one, yin or yang, is disharmonious.There has been inadequate research conducted on syndrome differentiation of yin and yang from a computational perspective. In this study, we present a computational method, viz. an end-to-end syndrome differentiation of yin deficiency and yang deficiency.Methods Unlike most previous studies on syndrome differentiation, which use structured datasets, this study takes unstructured texts in medical records as its inputs. It models syndrome differentiation as a task of text classification. This study experiments on two state-of-the-art end-to-end algorithms for text classification, i.e. a classic convolutional neural network (CNN) and fastText. These two systems take the n-grams of several types of tokens as their inputs, including characters, terms, and words.Results When evaluated on a data set with 7,326 modern medical records in TCM, it is observed that CNN and fastText generally give rise to comparable performances. The best accuracy rate of 92.55% comes from the system taking inputs as raw as n-grams of characters. It implies that one can build at least a moderate system for the differentiation of yin deficiency and yang deficiency even if he has no glossary or tokenizer at hand.Conclusions This study has demonstrated the feasibility of using end-to-end text classification algorithms to differentiate yin deficiency and yang deficiency on unstructured medical records.
  • Modelling intestinal glucose absorption in premature infants using
           continuous glucose monitoring data
    • Abstract: Publication date: Available online 3 October 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): J. Knopp, M. Signal, D. Harris, G Marics, P. Weston, J. Harding, P. Tóth-Heyn, J. Hómlok, B. Benyó, J.G Chase BackgroundModel-based glycaemic control protocols have shown promise in neonatal intensive care units (NICUs) for reducing both hyperglycaemia and insulin-therapy driven hypoglycaemia. However, current models for the appearance of glucose from enteral feeding are based on values from adult intensive care cohorts. This study aims to determine enteral glucose appearance model parameters more reflective of premature infant physiology.MethodsPeaks in CGM data associated with enteral milk feeds in preterm and term infants are used to fit a two compartment gut model. The first compartment describes glucose in the stomach, and the half life of gastric emptying is estimated as 20 minutes from literature. The second compartment describes glucose in the small intestine, and absorption of glucose into the blood is fit to CGM data. Two infant cohorts from two NICUs are used, and results are compared to appearances derived from data in highly controlled studies in literature.ResultsThe average half life across all infants for glucose absorption from the gut to the blood was 50 minutes. This result was slightly slower than, but of similar magnitude to, results derived from literature. No trends were found with gestational or postnatal age. Breast milk fed infants were found to have a higher absorption constant than formula fed infants, a result which may reflect known differences in gastric emptying for different feed types.ConclusionsThis paper presents a methodology for estimation of glucose appearance due to enteral feeding, and model parameters suitable for a NICU model-based glycaemic control context.
  • Deep generative learning for automated EHR diagnosis of traditional
           Chinese medicine
    • Abstract: Publication date: Available online 4 May 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Zhaohui Liang, Jun Liu, Aihua Ou, Honglai Zhang, Ziping Li, Jimmy Xiangji Huang BackgroundComputer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice. The purpose of this paper is to evaluate a deep learning architecture as an effective solution for CAMDM.MethodsA two-step model is applied in our study. At the first step, an optimized seven-layer deep belief network (DBN) is applied as an unsupervised learning algorithm to perform model training to acquire feature representation. Then a support vector machine model is adopted to DBN at the second step of the supervised learning. There are two data sets used in the experiments. One is a plain text data set indexed by medical experts. The other is a structured dataset on primary hypertension. The data are randomly divided to generate the training set for the unsupervised learning and the testing set for the supervised learning. The model performance is evaluated by the statistics of mean and variance, the average precision and coverage on the data sets. Two conventional shallow models (support vector machine / SVM and decision tree / DT) are applied as the comparisons to show the superiority of our proposed approach.ResultsThe deep learning (DBN + SVM) model outperforms simple SVM and DT on two data sets in terms of all the evaluation measures, which confirms our motivation that the deep model is good at capturing the key features with less dependence when the index is built up by manpower.ConclusionsOur study shows the two-step deep learning model achieves high performance for medical information retrieval over the conventional shallow models. It is able to capture the features of both plain text and the highly-structured database of EMR data. The performance of the deep model is superior to the conventional shallow learning models such as SVM and DT. It is an appropriate knowledge-learning model for information retrieval of EMR system. Therefore, deep learning provides a good solution to improve the performance of CAMDM systems.
  • Radial artery pulse waveform analysis based on curve fitting using
           discrete Fourier series
    • Abstract: Publication date: Available online 19 April 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Zhixing Jiang, David Zhang, Guangming Lu Background and objectives: Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients’ wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine.Methods: In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function.Results: Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model.Conclusions: The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states.
  • Exploring the molecular mechanisms of Traditional Chinese Medicine
           components using gene expression signatures and connectivity map
    • Abstract: Publication date: Available online 4 April 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Minjae Yoo, Jimin Shin, Hyunmin Kim, Jihye Kim, Jaewoo Kang, Aik Choon Tan Background and objectiveTraditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the “multi-component, multi-target” nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map.MethodsWe interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results.ResultsWe clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) – a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases.
  • Symptom-based network classification identifies distinct clinical
           subgroups of liver diseases with common molecular pathways
    • Abstract: Publication date: Available online 22 February 2018Source: Computer Methods and Programs in BiomedicineAuthor(s): Zixin Shu, Wenwen Liu, Huikun Wu, Mingzhong Xiao, Deng Wu, Ting Cao, Meng Ren, Junxiu Tao, Chuhua Zhang, Tangqing He, Xiaodong Li, Runshun Zhang, Xuezhong Zhou Background and objectiveLiver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment.MethodsIndividualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 6475 liver inpatient cases, we built a liver disease comorbidity network (LDCN) to show the complicated associations between liver diseases and their comorbidities, and then constructed a patient similarity network with shared symptoms (PSN). Finally, we identified liver patient subgroups using community detection methods and performed enrichment analyses to find both distinct clinical and molecular characteristics (with the phenotype-genotype associations and interactome networks) of these patient subgroups.ResultsFrom the comorbidity network, we found that clinical liver patients have a wide range of disease comorbidities, in which the basic liver diseases (e.g. hepatitis b, decompensated liver cirrhosis), and the common chronic diseases (e.g. hypertension, type 2 diabetes), have high degree of disease comorbidities. In addition, we identified 303 patient modules (representing the liver patient subgroups) from the PSN, in which the top 6 modules with large number of cases include 51.68% of the whole cases and 251 modules contain only 10 or fewer cases, which indicates the manifestation diversity of liver diseases. Finally, we found that the patient subgroups actually have distinct symptom phenotypes, disease comorbidity characteristics and their underlying molecular pathways, which could be used for understanding the novel disease subtypes of liver conditions. For example, three patient subgroups, namely Module 6 (M6, n = 638), M2 (n = 623) and M1 (n = 488) were associated to common chronic liver disease conditions (hepatitis, cirrhosis, hepatocellular carcinoma). Meanwhile, patient subgroups of M30 (n = 36) and M36 (n = 37) were mostly related to acute gastroenteritis and upper respiratory infection, respectively, which reflected the individual comorbidity characteristics of liver subgroups. Furthermore, we identified the distinct genes and pathways of patient subgroups and the basic liver diseases (hepatitis b and cirrhosis), respectively. The high degree of overlapping pathways between them (e.g. M36 with 93.33% shared enriched pathways) indicates the underlying molecular network mechanisms of each patient subgroup.ConclusionsOur results demonstrate the utility and comprehensiveness of disease classification study based on community detection of patient network using shared TCM symptom phenotypes and it can be used to other more complex diseases.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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