AIMC Topic: Computational Biology

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Epistasis Analysis: Classification Through Machine Learning Methods.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for ...
Datasets as Topic Epistasis, Genetic Multifactor Dimensionality Reduction Humans Multifactorial Inheritance Polymorphism, Single Nucleotide Machine Learning Software Computational Biology Models, Genetic
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Protocol for Epistasis Detection with Machine Learning Using GenEpi Package.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
To develop medical treatments and prevention, the association between disease and genetic variants needs to be identified. The main goal of genome-wide association study (GWAS) is to discover the underlying reason for vulnerability to disease and uti...
Machine Learning Humans Software Databases, Genetic Genome, Human Phenotype Epistasis, Genetic Genotype Genome-Wide Association Study Genetic Association Studies Polymorphism, Single Nucleotide Computational Biology
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Brief Survey on Machine Learning in Epistasis.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interacti...
Data Mining Alzheimer Disease Genome, Human Inheritance Patterns Epistasis, Genetic Quantitative Trait, Heritable Crohn Disease Plants Macular Degeneration Genome-Wide Association Study Humans Polymorphism, Single Nucleotide Machine Learning Computational Biology Phenotype Neoplasms
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Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2021
Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to...
Humans Neural Networks, Computer Computational Biology
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CheXclusion: Fairness gaps in deep chest X-ray classifiers.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2021
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-art deep learning classifiers t...
X-Rays Humans Neural Networks, Computer Machine Learning Computational Biology
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Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2021
Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure and even death. Current detection of acute infection as well as assessment of a patient's severity of illness are imperfect. Characterization of a patient's im...
Humans Machine Learning Computational Biology Bayes Theorem Hospital Mortality Genomics
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Ten computer codes that transformed science.

Nature Jan 1, 2021
Physics National Institutes of Health (U.S.) Preprints as Topic History, 20th Century Databases, Nucleic Acid Fourier Analysis United States Databases, Protein Weather History, 21st Century National Library of Medicine (U.S.) Programming Languages Datasets as Topic Deep Learning Time Factors Computational Biology Neural Networks, Computer Algorithms Databases, Factual
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DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder.

Protein and peptide letters Jan 1, 2021
BACKGROUND: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcription...
Protein Processing, Post-Translational Proteins Databases, Protein Sulfenic Acids Machine Learning Deep Learning Computational Biology
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Network Analysis of Integrin Adhesion Complexes.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Cell-surface adhesion receptors mediate interactions with the extracellular matrix (ECM) to control many fundamental aspects of cell behavior, including cell migration, survival, and proliferation. Integrin adhesion receptors recruit structural and s...
Multiprotein Complexes Proteome Cell Movement Extracellular Matrix Actin Cytoskeleton Animals Integrins Protein Binding Cell Adhesion Humans Computational Biology Protein Interaction Mapping Neural Networks, Computer Software
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Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. However, the number of possible drug combinations is enormous, and it is infeasible to experimentally...
Databases, Factual Computer Simulation Neoplasms Antineoplastic Combined Chemotherapy Protocols Gene Expression Profiling Deep Learning Databases, Genetic Drug Combinations Humans Cell Line, Tumor Drug Synergism Neural Networks, Computer Computational Biology
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