AIMC Topic: Computational Biology

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Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals Jun 3, 2024
BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted th...
Humans Machine Learning Biomarkers Support Vector Machine Osteoarthritis Ion Channels Gene Expression Profiling Gene Regulatory Networks Computational Biology
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Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BMC musculoskeletal disorders Jun 3, 2024
BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both...
Sarcopenia Computational Biology Databases, Genetic Female Aged Gene Expression Profiling Transcriptome Protein Interaction Maps Gene Regulatory Networks Humans Machine Learning Osteoporosis
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SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Communications biology Jun 3, 2024
Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. Howev...
Deep Learning Protein Binding Binding Sites Computational Biology Proteins Nucleic Acids
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Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

PLoS computational biology Jun 3, 2024
Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any sub...
Humans Neural Networks, Computer Neurons Computational Biology Cognition Brain Models, Neurological Learning
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[Not Available].

Proteomics Jun 2, 2024
RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of t...
Humans RNA Artificial Intelligence Computational Biology Phase Separation Proteins Databases, Protein
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How well do models of visual cortex generalize to out of distribution samples?

PLoS computational biology May 31, 2024
Unit activity in particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses to static images along the primate ventral visual cortex. Linear combinations of DNN unit activities are widely used to build predicti...
Models, Neurological Neurons Photic Stimulation Visual Cortex Humans Neural Networks, Computer Algorithms Animals Computational Biology
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Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.

PLoS computational biology May 31, 2024
Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linea...
Neurons Photic Stimulation Neural Networks, Computer Visual Fields Computational Biology Cats Animals Visual Cortex Models, Neurological Primary Visual Cortex
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Equivariant score-based generative diffusion framework for 3D molecules.

BMC bioinformatics May 30, 2024
BACKGROUND: Molecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing gener...
Models, Molecular Deep Learning Computational Biology Algorithms Drug Design Drug Discovery
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Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

NPJ systems biology and applications May 30, 2024
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually chang...
Gene Expression Profiling Transcriptome Pharmacogenetics Genome-Wide Association Study Computational Biology Transcription Factors Humans Machine Learning Enhancer Elements, Genetic Chromatin
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Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

Proteins May 29, 2024
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed ...
Protein Folding Sequence Analysis, Protein Computational Biology Proteins Databases, Protein Amino Acid Sequence Artificial Intelligence Intrinsically Disordered Proteins Machine Learning Fourier Analysis Algorithms
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