AIMC Topic: Computational Biology

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Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology Jul 2, 2024
The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means...
Neurodegenerative Diseases Neuroimaging Computational Biology Magnetic Resonance Imaging Artificial Intelligence Machine Learning Brain Image Processing, Computer-Assisted Algorithms Humans
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Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

PLoS computational biology Jul 2, 2024
The classification of B cell lymphomas-mainly based on light microscopy evaluation by a pathologist-requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are importan...
Algorithms Leukemia, Lymphocytic, Chronic, B-Cell Computational Biology B-Lymphocytes Lymphoma, Large B-Cell, Diffuse Humans Machine Learning Receptors, Antigen, B-Cell High-Throughput Nucleotide Sequencing Lymphoma, B-Cell
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Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks.

IEEE journal of biomedical and health informatics Jul 2, 2024
Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different links, but most link prediction methods overlook distinctive no...
Humans Neural Networks, Computer Computational Biology Algorithms
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MAMLCDA: A Meta-Learning Model for Predicting circRNA-Disease Association Based on MAML Combined With CNN.

IEEE journal of biomedical and health informatics Jul 2, 2024
Circular RNAs (circRNAs) exist in vivo and are a class of noncoding RNA molecules. They have a single-stranded, closed, annular structure. Many studies have shown that circRNAs and diseases are linked. Therefore, it is critical to build a reliable an...
Humans Neural Networks, Computer Machine Learning Principal Component Analysis Algorithms RNA, Circular Computational Biology
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A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

IEEE journal of biomedical and health informatics Jul 2, 2024
Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-sp...
Protein Interaction Mapping Neural Networks, Computer Computational Biology Algorithms Deep Learning Drug Discovery Proteins Protein Interaction Maps
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Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

IEEE journal of biomedical and health informatics Jul 2, 2024
Binding affinity prediction of three-dimensional (3D) protein-ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches usually transform a 3D protein-ligand complex to a two-dimensional (2D) graph, and then ...
Neural Networks, Computer Computational Biology Proteins Ligands Algorithms Protein Binding
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Variant Effect Prediction in the Age of Machine Learning.

Cold Spring Harbor perspectives in biology Jul 1, 2024
Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus...
Polymorphism, Single Nucleotide Computational Biology Machine Learning Deep Learning Proteins Amino Acid Substitution Humans
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Bioinformatics and machine learning to support nanomaterial grouping.

Nanotoxicology Jul 1, 2024
Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and ris...
Risk Assessment Computational Biology Nanostructures Humans Machine Learning Animals
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A CNN-CBAM-BIGRU model for protein function prediction.

Statistical applications in genetics and molecular biology Jul 1, 2024
Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning as a powe...
Computational Biology Algorithms Amino Acid Sequence Humans Proteins Deep Learning Databases, Protein Neural Networks, Computer
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APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features.

Methods (San Diego, Calif.) Jun 28, 2024
Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through ...
Databases, Protein Amino Acid Sequence Software Computational Biology Support Vector Machine Machine Learning
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