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Publisher: Hindawi   (Total: 298 journals)

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Showing 1 - 200 of 298 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.512, h-index: 32)
Active and Passive Electronic Components     Open Access   (Followers: 7, SJR: 0.157, h-index: 15)
Advances in Acoustics and Vibration     Open Access   (Followers: 27, SJR: 0.259, h-index: 6)
Advances in Agriculture     Open Access   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Astronomy     Open Access   (Followers: 38, SJR: 0.351, h-index: 17)
Advances in Bioinformatics     Open Access   (Followers: 18, SJR: 0.421, h-index: 8)
Advances in Chemistry     Open Access   (Followers: 14)
Advances in Civil Engineering     Open Access   (Followers: 33, SJR: 0.338, h-index: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 8, SJR: 0.248, h-index: 10)
Advances in Decision Sciences     Open Access   (Followers: 4, SJR: 0.231, h-index: 6)
Advances in Electrical Engineering     Open Access   (Followers: 20)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.258, h-index: 7)
Advances in Hematology     Open Access   (Followers: 9, SJR: 0.892, h-index: 18)
Advances in High Energy Physics     Open Access   (Followers: 19, SJR: 0.892, h-index: 19)
Advances in Human-Computer Interaction     Open Access   (Followers: 20, SJR: 0.439, h-index: 9)
Advances in Materials Science and Engineering     Open Access   (Followers: 32, SJR: 0.263, h-index: 11)
Advances in Mathematical Physics     Open Access   (Followers: 5, SJR: 0.332, h-index: 10)
Advances in Medicine     Open Access   (Followers: 2)
Advances in Meteorology     Open Access   (Followers: 18, SJR: 0.498, h-index: 10)
Advances in Multimedia     Open Access   (Followers: 2, SJR: 0.191, h-index: 10)
Advances in Nonlinear Optics     Open Access   (Followers: 5)
Advances in Numerical Analysis     Open Access   (Followers: 4)
Advances in Operations Research     Open Access   (Followers: 11, SJR: 0.343, h-index: 7)
Advances in Optical Technologies     Open Access   (Followers: 3, SJR: 0.283, h-index: 16)
Advances in OptoElectronics     Open Access   (Followers: 5, SJR: 0.973, h-index: 16)
Advances in Orthopedic Surgery     Open Access   (Followers: 9)
Advances in Orthopedics     Open Access   (Followers: 9)
Advances in Pharmacological Sciences     Open Access   (Followers: 6, SJR: 0.695, h-index: 13)
Advances in Physical Chemistry     Open Access   (Followers: 11, SJR: 0.297, h-index: 7)
Advances in Power Electronics     Open Access   (Followers: 25, SJR: 0.26, h-index: 6)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 20)
Advances in Tribology     Open Access   (Followers: 10, SJR: 0.267, h-index: 6)
Advances in Urology     Open Access   (Followers: 10, SJR: 0.629, h-index: 16)
Advances in Virology     Open Access   (Followers: 7, SJR: 1.04, h-index: 12)
AIDS Research and Treatment     Open Access   (Followers: 3, SJR: 1.125, h-index: 14)
Analytical Cellular Pathology     Open Access   (Followers: 2, SJR: 0.334, h-index: 12)
Anatomy Research Intl.     Open Access   (Followers: 2)
Anemia     Open Access   (Followers: 4, SJR: 0.991, h-index: 11)
Anesthesiology Research and Practice     Open Access   (Followers: 12, SJR: 0.513, h-index: 12)
Applied and Environmental Soil Science     Open Access   (Followers: 17, SJR: 0.53, h-index: 9)
Applied Bionics and Biomechanics     Open Access   (Followers: 8, SJR: 0.23, h-index: 13)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Archaea     Open Access   (Followers: 3, SJR: 1.248, h-index: 27)
Arthritis     Open Access   (Followers: 4)
Autism Research and Treatment     Open Access   (Followers: 29)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.909, h-index: 17)
Behavioural Neurology     Open Access   (Followers: 7, SJR: 0.696, h-index: 34)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 1.085, h-index: 17)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 9, SJR: 0.286, h-index: 19)
BioMed Research Intl.     Open Access   (Followers: 6, SJR: 0.725, h-index: 59)
Biotechnology Research Intl.     Open Access   (Followers: 2)
Bone Marrow Research     Open Access   (Followers: 2)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 4, SJR: 0.856, h-index: 53)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 4, SJR: 0.409, h-index: 25)
Canadian Respiratory J.     Open Access   (Followers: 1, SJR: 0.503, h-index: 42)
Cardiology Research and Practice     Open Access   (Followers: 8, SJR: 0.941, h-index: 17)
Cardiovascular Psychiatry and Neurology     Open Access   (Followers: 4, SJR: 1.091, h-index: 14)
Case Reports in Anesthesiology     Open Access   (Followers: 10)
Case Reports in Cardiology     Open Access   (Followers: 2)
Case Reports in Critical Care     Open Access   (Followers: 9)
Case Reports in Dentistry     Open Access   (Followers: 3)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 13)
Case Reports in Endocrinology     Open Access   (SJR: 0.326, h-index: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 3)
Case Reports in Genetics     Open Access   (Followers: 1)
Case Reports in Hematology     Open Access   (Followers: 2)
Case Reports in Hepatology     Open Access   (Followers: 1)
Case Reports in Immunology     Open Access   (Followers: 4)
Case Reports in Infectious Diseases     Open Access   (Followers: 5)
Case Reports in Medicine     Open Access   (Followers: 3)
Case Reports in Nephrology     Open Access   (Followers: 4)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 11)
Case Reports in Oncological Medicine     Open Access   (Followers: 2)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 3)
Case Reports in Orthopedics     Open Access   (Followers: 7)
Case Reports in Otolaryngology     Open Access   (Followers: 4)
Case Reports in Pathology     Open Access   (Followers: 5)
Case Reports in Pediatrics     Open Access   (Followers: 5)
Case Reports in Psychiatry     Open Access   (Followers: 11)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 9)
Case Reports in Rheumatology     Open Access   (Followers: 4)
Case Reports in Surgery     Open Access   (Followers: 8)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 8)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Chemotherapy Research and Practice     Open Access   (Followers: 1)
Child Development Research     Open Access   (Followers: 15)
Chinese J. of Engineering     Open Access   (Followers: 2)
Chinese J. of Mathematics     Open Access  
Cholesterol     Open Access   (Followers: 1, SJR: 0.906, h-index: 12)
Complexity     Hybrid Journal   (Followers: 6, SJR: 0.526, h-index: 27)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.415, h-index: 22)
Computational Intelligence and Neuroscience     Open Access   (Followers: 10, SJR: 0.232, h-index: 30)
Contrast Media & Molecular Imaging     Open Access   (Followers: 3, SJR: 0.932, h-index: 34)
Critical Care Research and Practice     Open Access   (Followers: 10, SJR: 0.916, h-index: 14)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.8, h-index: 12)
Depression Research and Treatment     Open Access   (Followers: 13, SJR: 0.77, h-index: 11)
Dermatology Research and Practice     Open Access   (Followers: 3, SJR: 0.576, h-index: 15)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.651, h-index: 18)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 5, SJR: 0.323, h-index: 24)
Disease Markers     Open Access   (Followers: 1, SJR: 0.774, h-index: 49)
Education Research Intl.     Open Access   (Followers: 18)
Emergency Medicine Intl.     Open Access   (Followers: 7)
Enzyme Research     Open Access   (Followers: 4, SJR: 0.457, h-index: 18)
Epilepsy Research and Treatment     Open Access   (Followers: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 18, SJR: 0.615, h-index: 50)
Experimental Diabetes Research     Open Access   (Followers: 11, SJR: 1.591, h-index: 30)
Gastroenterology Research and Practice     Open Access   (Followers: 3, SJR: 0.664, h-index: 21)
Genetics Research Intl.     Open Access   (Followers: 1)
Geofluids     Open Access   (Followers: 4, SJR: 0.693, h-index: 38)
Hepatitis Research and Treatment     Open Access   (Followers: 6)
HPB Surgery     Open Access   (Followers: 5, SJR: 0.798, h-index: 22)
Indian J. of Materials Science     Open Access  
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 7, SJR: 0.976, h-index: 34)
Influenza Research and Treatment     Open Access   (Followers: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 2, SJR: 0.763, h-index: 15)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 66, SJR: 0.241, h-index: 6)
Intl. J. of Agronomy     Open Access   (Followers: 8, SJR: 0.223, h-index: 2)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 11, SJR: 1.193, h-index: 25)
Intl. J. of Analysis     Open Access  
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.157, h-index: 2)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 11, SJR: 0.385, h-index: 15)
Intl. J. of Bacteriology     Open Access  
Intl. J. of Biodiversity     Open Access   (Followers: 4)
Intl. J. of Biomaterials     Open Access   (Followers: 5, SJR: 0.485, h-index: 10)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 5, SJR: 0.581, h-index: 23)
Intl. J. of Breast Cancer     Open Access   (Followers: 12)
Intl. J. of Cell Biology     Open Access   (Followers: 4, SJR: 2.658, h-index: 25)
Intl. J. of Chemical Engineering     Open Access   (Followers: 7, SJR: 0.361, h-index: 10)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 11, SJR: 0.213, h-index: 12)
Intl. J. of Corrosion     Open Access   (Followers: 11, SJR: 0.19, h-index: 7)
Intl. J. of Dentistry     Open Access   (Followers: 6, SJR: 0.558, h-index: 11)
Intl. J. of Differential Equations     Open Access   (Followers: 6, SJR: 0.363, h-index: 11)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.144, h-index: 10)
Intl. J. of Electrochemistry     Open Access   (Followers: 8)
Intl. J. of Endocrinology     Open Access   (Followers: 3, SJR: 0.961, h-index: 24)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 3)
Intl. J. of Evolutionary Biology     Open Access   (Followers: 9)
Intl. J. of Family Medicine     Open Access   (Followers: 2)
Intl. J. of Food Science     Open Access   (Followers: 3)
Intl. J. of Forestry Research     Open Access   (Followers: 4)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.721, h-index: 7)
Intl. J. of Hepatology     Open Access   (Followers: 3)
Intl. J. of Hypertension     Open Access   (Followers: 6, SJR: 0.823, h-index: 20)
Intl. J. of Inflammation     Open Access   (SJR: 0.876, h-index: 14)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.346, h-index: 27)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6)
Intl. J. of Microbiology     Open Access   (Followers: 5, SJR: 1.006, h-index: 18)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 3, SJR: 0.167, h-index: 5)
Intl. J. of Molecular Imaging     Open Access  
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.411, h-index: 7)
Intl. J. of Nephrology     Open Access   (Followers: 2, SJR: 0.926, h-index: 14)
Intl. J. of Optics     Open Access   (Followers: 7, SJR: 0.262, h-index: 7)
Intl. J. of Otolaryngology     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 5)
Intl. J. of Peptides     Open Access   (Followers: 4, SJR: 0.73, h-index: 16)
Intl. J. of Photoenergy     Open Access   (Followers: 2, SJR: 0.348, h-index: 28)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 1.578, h-index: 20)
Intl. J. of Polymer Science     Open Access   (Followers: 23, SJR: 0.265, h-index: 11)
Intl. J. of Population Research     Open Access   (Followers: 2)
Intl. J. of Proteomics     Open Access   (Followers: 1)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.182, h-index: 8)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 5)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 1.015, h-index: 18)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.402, h-index: 19)
Intl. J. of Spectroscopy     Open Access   (Followers: 8)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 4, SJR: 0.234, h-index: 19)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.753, h-index: 11)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 4, SJR: 0.757, h-index: 14)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.865, h-index: 16)
Intl. J. of Vehicular Technology     Open Access   (Followers: 4, SJR: 0.169, h-index: 6)
Intl. J. of Zoology     Open Access   (Followers: 1, SJR: 0.389, h-index: 8)
Intl. Scholarly Research Notices     Open Access   (Followers: 199)
ISRN Astronomy and Astrophysics     Open Access   (Followers: 7)
J. of Addiction     Open Access   (Followers: 10)
J. of Advanced Transportation     Hybrid Journal   (Followers: 11, SJR: 0.911, h-index: 24)
J. of Aging Research     Open Access   (Followers: 7, SJR: 1.259, h-index: 23)
J. of Allergy     Open Access   (Followers: 4)
J. of Amino Acids     Open Access   (Followers: 2)
J. of Analytical Methods in Chemistry     Open Access   (Followers: 1, SJR: 0.296, h-index: 13)
J. of Anthropology     Open Access   (Followers: 24)
J. of Applied Chemistry     Open Access   (Followers: 4)
J. of Applied Mathematics     Open Access   (Followers: 2, SJR: 0.341, h-index: 22)
J. of Biomarkers     Open Access  
J. of Biomedical Education     Open Access   (Followers: 2)
J. of Biophysics     Open Access   (Followers: 5, SJR: 0.22, h-index: 5)
J. of Blood Transfusion     Open Access   (Followers: 1)
J. of Botany     Open Access   (Followers: 3, SJR: 0.101, h-index: 2)
J. of Cancer Epidemiology     Open Access   (Followers: 7, SJR: 1.427, h-index: 12)
J. of Chemistry     Open Access   (Followers: 5, SJR: 0.225, h-index: 11)
J. of Combustion     Open Access   (Followers: 17, SJR: 0.27, h-index: 8)
J. of Complex Analysis     Open Access   (Followers: 3)
J. of Computational Engineering     Open Access   (Followers: 1)

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Journal Cover Computational and Mathematical Methods in Medicine
  [SJR: 0.415]   [H-I: 22]   [2 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1748-670X - ISSN (Online) 1748-6718
   Published by Hindawi Homepage  [298 journals]
  • Dendritic Immunotherapy Improvement for an Optimal Control Murine Model

    • Abstract: Therapeutic protocols in immunotherapy are usually proposed following the intuition and experience of the therapist. In order to deduce such protocols mathematical modeling, optimal control and simulations are used instead of the therapist’s experience. Clinical efficacy of dendritic cell (DC) vaccines to cancer treatment is still unclear, since dendritic cells face several obstacles in the host environment, such as immunosuppression and poor transference to the lymph nodes reducing the vaccine effect. In view of that, we have created a mathematical murine model to measure the effects of dendritic cell injections admitting such obstacles. In addition, the model considers a therapy given by bolus injections of small duration as opposed to a continual dose. Doses timing defines the therapeutic protocols, which in turn are improved to minimize the tumor mass by an optimal control algorithm. We intend to supplement therapist’s experience and intuition in the protocol’s implementation. Experimental results made on mice infected with melanoma with and without therapy agree with the model. It is shown that the dendritic cells’ percentage that manages to reach the lymph nodes has a crucial impact on the therapy outcome. This suggests that efforts in finding better methods to deliver DC vaccines should be pursued.
      PubDate: Sun, 20 Aug 2017 00:00:00 +000
       
  • Pathway Interaction Network Analysis Identifies Dysregulated Pathways in
           Human Monocytes Infected by Listeria monocytogenes

    • Abstract: In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.
      PubDate: Wed, 16 Aug 2017 09:10:06 +000
       
  • Products of Compartmental Models in Epidemiology

    • Abstract: We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis.
      PubDate: Wed, 16 Aug 2017 00:00:00 +000
       
  • A Novel Dynamic Model Describing the Spread of the MERS-CoV and the
           Expression of Dipeptidyl Peptidase 4

    • Abstract: The Middle East respiratory syndrome (MERS) coronavirus, a newly identified pathogen, causes severe pneumonia in humans. MERS is caused by a coronavirus known as MERS-CoV, which attacks the respiratory system. The recently defined receptor for MERS-CoV, dipeptidyl peptidase 4 (DPP4), is generally expressed in endothelial and epithelial cells and has been shown to be present on cultured human nonciliated bronchiolar epithelium cells. In this paper, a class of novel four-dimensional dynamic model describing the infection of MERS-CoV is given, and then global stability of the equilibria of the model is discussed. Our results show that the spread of MERS-CoV can also be controlled by decreasing the expression rate of DPP4.
      PubDate: Tue, 15 Aug 2017 00:00:00 +000
       
  • Computational and Mathematical Methods to Estimate the Basic Reproduction
           Number and Final Size for Single-Stage and Multistage Progression Disease
           Models for Zika with Preventative Measures

    • Abstract: We present new mathematical models that include the impact of using selected preventative measures such as insecticide treated nets (ITN) in controlling or ameliorating the spread of the Zika virus. For these models, we derive the basic reproduction number and sharp estimates for the final size relation. We first present a single-stage model which is later extended to a new multistage model for Zika that incorporates more realistic incubation stages for both the humans and vectors. For each of these models, we derive a basic reproduction number and a final size relation estimate. We observe that the basic reproduction number for the multistage model converges to expected values for a standard Zika epidemic model with fixed incubation periods in both hosts and vectors. Finally, we also perform several computational experiments to validate the theoretical results obtained in this work and study the influence of various parameters on the models.
      PubDate: Tue, 15 Aug 2017 00:00:00 +000
       
  • Effect of Rainfall for the Dynamical Transmission Model of the Dengue
           Disease in Thailand

    • Abstract: The SEIR (Susceptible-Exposed-Infected-Recovered) model is used to describe the transmission of dengue virus. The main contribution is determining the role of the rainfall in Thailand in the model. The transmission of dengue disease is assumed to depend on the nature of the rainfall in Thailand. We analyze the dynamic transmission of dengue disease. The stability of the solution of the model is analyzed. It is investigated by using the Routh-Hurwitz criteria. We find two equilibrium states: a disease-free state and an endemic equilibrium state. The basic reproductive number () is obtained, which indicates the stability of each equilibrium state. Numerical results taking into account the rainfall are obtained and they are seen to correspond to the analytical results.
      PubDate: Tue, 08 Aug 2017 08:16:12 +000
       
  • Segmentation of Left and Right Ventricles in Cardiac MRI Using Active
           Contours

    • Abstract: Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI.
      PubDate: Tue, 08 Aug 2017 06:26:31 +000
       
  • An Update on Statistical Boosting in Biomedicine

    • Abstract: Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.
      PubDate: Wed, 02 Aug 2017 07:57:33 +000
       
  • A Multicriteria Approach to Find Predictive and Sparse Models with Stable
           Feature Selection for High-Dimensional Data

    • Abstract: Finding a good predictive model for a high-dimensional data set can be challenging. For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable. This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial. We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features. As it is unclear which measure is best for evaluating the stability, we first compare a variety of stability measures. We conclude that the Pearson correlation has the best theoretical and empirical properties. Also, we find that for the stability assessment behaviour it is most important that a measure contains a correction for chance or large numbers of chosen features. Then, we analyse Pareto fronts and conclude that it is possible to find models with a stable selection of few features without losing much predictive accuracy.
      PubDate: Tue, 01 Aug 2017 08:01:06 +000
       
  • Several Indicators of Critical Transitions for Complex Diseases Based on
           Stochastic Analysis

    • Abstract: Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules. Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson’s correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases. To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state. The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.
      PubDate: Tue, 01 Aug 2017 07:04:13 +000
       
  • Probing for Sparse and Fast Variable Selection with Model-Based Boosting

    • Abstract: We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slightly altered data (e.g., cross-validation or bootstrap) to find the optimal number of boosting iterations and prevent overfitting. In our proposed approach, we augment the data set with randomly permuted versions of the true variables, so-called shadow variables, and stop the stepwise fitting as soon as such a variable would be added to the model. This allows variable selection in a single fit of the model without requiring further parameter tuning. We show that our probing approach can compete with state-of-the-art selection methods like stability selection in a high-dimensional classification benchmark and apply it on three gene expression data sets.
      PubDate: Mon, 31 Jul 2017 06:43:12 +000
       
  • Integration of Multiple Genomic Data Sources in a Bayesian Cox Model for
           Variable Selection and Prediction

    • Abstract: Bayesian variable selection becomes more and more important in statistical analyses, in particular when performing variable selection in high dimensions. For survival time models and in the presence of genomic data, the state of the art is still quite unexploited. One of the more recent approaches suggests a Bayesian semiparametric proportional hazards model for right censored time-to-event data. We extend this model to directly include variable selection, based on a stochastic search procedure within a Markov chain Monte Carlo sampler for inference. This equips us with an intuitive and flexible approach and provides a way for integrating additional data sources and further extensions. We make use of the possibility of implementing parallel tempering to help improve the mixing of the Markov chains. In our examples, we use this Bayesian approach to integrate copy number variation data into a gene-expression-based survival prediction model. This is achieved by formulating an informed prior based on copy number variation. We perform a simulation study to investigate the model’s behavior and prediction performance in different situations before applying it to a dataset of glioblastoma patients and evaluating the biological relevance of the findings.
      PubDate: Sun, 30 Jul 2017 09:24:19 +000
       
  • Convolutional Neural Network for the Detection of End-Diastole and
           End-Systole Frames in Free-Breathing Cardiac Magnetic Resonance Imaging

    • Abstract: Free-breathing cardiac magnetic resonance (CMR) imaging has short examination time with high reproducibility. Detection of the end-diastole and the end-systole frames of the free-breathing cardiac magnetic resonance, supplemented by visual identification, is time consuming and laborious. We propose a novel method for automatic identification of both the end-diastole and the end-systole frames, in the free-breathing CMR imaging. The proposed technique utilizes the convolutional neural network to locate the left ventricle and to obtain the end-diastole and the end-systole frames from the respiratory motion signal. The proposed procedure works successfully on our free-breathing CMR data, and the results demonstrate a high degree of accuracy and stability. Convolutional neural network improves the postprocessing efficiency greatly and facilitates the clinical application of the free-breathing CMR imaging.
      PubDate: Wed, 26 Jul 2017 09:25:29 +000
       
  • Kalman Filtering for Genetic Regulatory Networks with Missing Values

    • Abstract: The filter problem with missing value for genetic regulation networks (GRNs) is addressed, in which the noises exist in both the state dynamics and measurement equations; furthermore, the correlation between process noise and measurement noise is also taken into consideration. In order to deal with the filter problem, a class of discrete-time GRNs with missing value, noise correlation, and time delays is established. Then a new observation model is proposed to decrease the adverse effect caused by the missing value and to decouple the correlation between process noise and measurement noise in theory. Finally, a Kalman filtering is used to estimate the states of GRNs. Meanwhile, a typical example is provided to verify the effectiveness of the proposed method, and it turns out to be the case that the concentrations of mRNA and protein could be estimated accurately.
      PubDate: Wed, 26 Jul 2017 08:58:39 +000
       
  • Computational Paradigms for Mental Health

    • PubDate: Wed, 26 Jul 2017 08:54:28 +000
       
  • NSiteMatch: Prediction of Binding Sites of Nucleotides by Identifying the
           Structure Similarity of Local Surface Patches

    • Abstract: Nucleotides play a central role in life-form metabolism, by interacting with proteins and mediating the function of proteins. It is estimated that nucleotides constitute about 15% of the biologically relevant ligands included in PDB. Prediction of binding sites of nucleotides is useful in understanding the function of proteins and can facilitate the in silico design of drugs. In this study, we propose a nucleotide-binding site predictor, namely, NSiteMatch. The NSiteMatch algorithm integrates three different strategies: geometrical analysis, energy calculation, and template comparison. Unlike a traditional template-based predictor, which identifies global similarity between target structure and template, NSiteMatch concerns the local similarity between a surface patch of the target protein and the binding sites of template. To this end, NSiteMatch identifies more templates than traditional template-based predictors. The NSiteMatch predictor is compared with three representative methods, Findsite, Q-SiteFinder, and MetaPocket. An extensive evaluation demonstrates that NSiteMatch achieves higher success rates than Findsite, Q-SiteFinder, and MetaPocket, in prediction of binding sites of ATP, ADP, and AMP.
      PubDate: Tue, 25 Jul 2017 00:00:00 +000
       
  • A Multiscale Model for the World’s First Parasitic Disease Targeted for
           Eradication: Guinea Worm Disease

    • Abstract: Guinea worm disease (GWD) is both a neglected tropical disease and an environmentally driven infectious disease. Environmentally driven infectious diseases remain one of the biggest health threats for human welfare in developing countries and the threat is increased by the looming danger of climate change. In this paper we present a multiscale model of GWD that integrates the within-host scale and the between-host scale. The model is used to concurrently examine the interactions between the three organisms that are implicated in natural cases of GWD transmission, the copepod vector, the human host, and the protozoan worm parasite (Dracunculus medinensis), and identify their epidemiological roles. The results of the study (through sensitivity analysis of ) show that the most efficient elimination strategy for GWD at between-host scale is to give highest priority to copepod vector control by killing the copepods in drinking water (the intermediate host) by applying chemical treatments (e.g., temephos, an organophosphate). This strategy should be complemented by health education to ensure that greater numbers of individuals and communities adopt behavioural practices such as voluntary reporting of GWD cases, prevention of GWD patients from entering drinking water bodies, regular use of water from safe water sources, and, in the absence of such water sources, filtering or boiling water before drinking. Taking into account the fact that there is no drug or vaccine for GWD (interventions which operate at within-host scale), the results of our study show that the development of a drug that kills female worms at within-host scale would have the highest impact at this scale domain with possible population level benefits that include prevention of morbidity and prevention of transmission.
      PubDate: Thu, 20 Jul 2017 07:56:54 +000
       
  • Improved - PCA Algorithm Using Artificial Sparsity in Dynamic MRI

    • Abstract: The - principal component analysis (- PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse - PCA that combines the - PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional - PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled - space from the original reconstructed - space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard - PCA algorithm, the sparse - PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.
      PubDate: Tue, 18 Jul 2017 00:00:00 +000
       
  • Altered Brain Functional Connectivity in Small-Cell Lung Cancer Patients
           after Chemotherapy Treatment: A Resting-State fMRI Study

    • Abstract: Previous studies in small-cell lung cancer (SCLC) patients have mainly focused on exploring neurocognitive deficits associated with prophylactic cranial irradiation (PCI). Little is known about functional brain alterations that might occur due to chemotherapy treatment in this population before PCI is administered. For this reason, we used resting-state functional Magnetic Resonance Imaging (fMRI) to examine potential functional connectivity disruptions in brain networks, including the Default Mode Network (DMN), the Sensorimotor Network, and the Task-Positive Network (TPN). Nineteen SCLC patients after platinum-based chemotherapy treatment and thirteen controls were recruited in the current study. ROI-to-ROI and Seed-to-Voxel analyses were carried out and revealed functional connectivity deficits in patients within all the networks investigated demonstrating the possible negative effect of chemotherapy in cognitive functions in SCLC populations.
      PubDate: Mon, 17 Jul 2017 10:55:07 +000
       
  • Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association
           Studies

    • Abstract: The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.
      PubDate: Thu, 13 Jul 2017 09:08:48 +000
       
  • A Monte Carlo Study of the Photon Spectrum due to the Different Materials
           Used in the Construction of Flattening Filters of LINAC

    • Abstract: Different types the spectrum of photons were studied; they were emitted from the flattening filter of a LINAC Varian 2100 C/D that operates at 15 MV. The simplified geometry of the LINAC head was calculated using the MCNPX code based on the studies of the materials of the flattening filter, namely, SST, W, Pb, Fe, Ta, Al, and Cu. These materials were replaced in the flattening filter to calculate the photon spectra at the output of this device to obtain the spectrum that makes an impact with the patient. The different spectra obtained were analyzed and compared to the emission from the original spectra configuration of the LINAC, which uses material W. In the study, different combinations of materials were considered in order to establish differences between the use of different materials and the original material, with the objective of establishing advantages and disadvantages from a clinical standpoint.
      PubDate: Mon, 10 Jul 2017 00:00:00 +000
       
  • Mathematical Modeling for Inherited Diseases

    • Abstract: We introduced a new nonassociative algebra, namely, left almost algebra, and discussed some of its genetic properties. We discussed the relation of this algebra with flexible algebra, Jordan algebra, and generalized Jordan algebra.
      PubDate: Sun, 09 Jul 2017 07:03:25 +000
       
  • Threshold Dynamics of a Stochastic Model with Vertical Transmission and
           Vaccination

    • Abstract: A stochastic model with vertical transmission and vaccination is proposed and investigated in this paper. The threshold dynamics are explored when the noise is small. The conditions for the extinction or persistence of infectious diseases are deduced. Our results show that large noise can lead to the extinction of infectious diseases which is conducive to epidemic diseases control.
      PubDate: Thu, 06 Jul 2017 00:00:00 +000
       
  • Optimization of Classification Strategies of Acetowhite Temporal Patterns
           towards Improving Diagnostic Performance of Colposcopy

    • Abstract: Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4.5), in addition to different data models that take full advantage of this information and improve the diagnostic performance of colposcopy based on acetowhite temporal patterns. Based on the ROC and PRC area scores, the k-Nearest Neighbors and discrete PLA representation performed better than other methods. The values of sensitivity, specificity, and accuracy reached using this method were 60% (95% CI 50–70), 79% (95% CI 71–86), and 70% (95% CI 60–80), respectively. The acetowhitening phenomenon is not exclusive to high-grade lesions, and we have found acetowhite temporal patterns of epithelial changes that are not precancerous lesions but that are similar to positive ones. These findings need to be considered when developing more robust computing systems in the future.
      PubDate: Tue, 04 Jul 2017 06:11:23 +000
       
  • Corrigendum to “A Clinical Decision Support System for the Diagnosis,
           Fracture Risks and Treatment of Osteoporosis”

    • PubDate: Thu, 29 Jun 2017 00:00:00 +000
       
  • A Selective Ensemble Classification Method Combining Mammography Images
           with Ultrasound Images for Breast Cancer Diagnosis

    • Abstract: Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator presents a new way to choose the base classifier for ensemble learning.
      PubDate: Tue, 27 Jun 2017 09:10:50 +000
       
  • Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse
           Extreme Learning Machine Classification

    • Abstract: An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.
      PubDate: Mon, 19 Jun 2017 08:02:59 +000
       
  • Mathematical Simulation of Transport Kinetics of Tumor-Imaging
           Radiopharmaceutical 99mTc-MIBI

    • Abstract: The proposed model describes in a quality way the process of tumor-imaging radiopharmaceutical -MIBI distribution with taking into account radiopharmaceutical accumulation, elimination, and radioactive decay. The dependencies of concentration versus the time are analyzed. The model can be easily tested by the concentration data of the radioactive pharmaceuticals in the blood measured at early time point and late time point of the scanning, and the obtained data can be used for determination of the washout rate coefficient which is one of the existing oncology diagnostics methods.
      PubDate: Thu, 15 Jun 2017 00:00:00 +000
       
  • An Exercise Health Simulation Method Based on Integrated Human
           Thermophysiological Model

    • Abstract: Research of healthy exercise has garnered a keen research for the past few years. It is known that participation in a regular exercise program can help improve various aspects of cardiovascular function and reduce the risk of suffering from illness. But some exercise accidents like dehydration, exertional heatstroke, and even sudden death need to be brought to attention. If these exercise accidents can be analyzed and predicted before they happened, it will be beneficial to alleviate or avoid disease or mortality. To achieve this objective, an exercise health simulation approach is proposed, in which an integrated human thermophysiological model consisting of human thermal regulation model and a nonlinear heart rate regulation model is reported. The human thermoregulatory mechanism as well as the heart rate response mechanism during exercise can be simulated. On the basis of the simulated physiological indicators, a fuzzy finite state machine is constructed to obtain the possible health transition sequence and predict the exercise health status. The experiment results show that our integrated exercise thermophysiological model can numerically simulate the thermal and physiological processes of the human body during exercise and the predicted exercise health transition sequence from finite state machine can be used in healthcare.
      PubDate: Thu, 15 Jun 2017 00:00:00 +000
       
  • SIFT Based Vein Recognition Models: Analysis and Improvement

    • Abstract: Scale-Invariant Feature Transform (SIFT) is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE), compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence that the number of extracted keypoints resulting by gradient based detectors increases greatly with different CE methods, while on the other hand the matching result of extracted invariant descriptors is negatively influenced in terms of Precision-Recall (PR) and Equal Error Rate (EER). Rigorous experiments with state-of-the-art and other CE adopted in published SIFT based hand vein recognition system demonstrate the influence. What is more, an improved SIFT model by importing the kernel of RootSIFT and Mirror Match Strategy into a unified framework is proposed to make use of the positive keypoints change and make up for the negative influence brought by CE.
      PubDate: Wed, 07 Jun 2017 10:11:02 +000
       
 
 
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