AIMC Topic: Algorithms

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Multi-variable AUC for sifting complementary features and its biomedical application.

Briefings in bioinformatics Mar 10, 2022
Although sifting functional genes has been discussed for years, traditional selection methods tend to be ineffective in capturing potential specific genes. First, typical methods focus on finding features (genes) relevant to class while irrelevant to...
Area Under Curve Machine Learning Algorithms ROC Curve
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Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

Briefings in bioinformatics Mar 10, 2022
Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. H...
Lysine Software Algorithms Protein Processing, Post-Translational Neural Networks, Computer Phosphorylation Deep Learning ROC Curve Databases, Factual User-Computer Interface Computational Biology Reproducibility of Results
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Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Briefings in bioinformatics Mar 10, 2022
Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins th...
Machine Learning Amino Acids Algorithms Membrane Proteins
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Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

Briefings in bioinformatics Mar 10, 2022
The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in...
Workflow Antimicrobial Peptides ROC Curve Antifungal Agents Drug Discovery Deep Learning Software Algorithms Databases, Factual Reproducibility of Results Neural Networks, Computer Artificial Intelligence Humans
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Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction.

Briefings in bioinformatics Mar 10, 2022
MOTIVATION: Identifying new therapeutic effects for the approved drugs is beneficial for effectively reducing the drug development cost and time. Most of the recent computational methods concentrate on exploiting multiple kinds of information about d...
Drug Development Neural Networks, Computer Algorithms
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ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

Briefings in bioinformatics Mar 10, 2022
MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds...
Algorithms Drug Development Neural Networks, Computer Proteins Drug Interactions
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A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data.

Briefings in bioinformatics Mar 10, 2022
Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the ...
Neural Networks, Computer Algorithms Mice Deep Learning Gene Regulatory Networks Animals Transcriptome
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An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Briefings in bioinformatics Mar 10, 2022
Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force f...
Proteins Molecular Dynamics Simulation Machine Learning Algorithms Neural Networks, Computer
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A robust and scalable graph neural network for accurate single-cell classification.

Briefings in bioinformatics Mar 10, 2022
Single-cell RNA sequencing (scRNA-seq) techniques provide high-resolution data on cellular heterogeneity in diverse tissues, and a critical step for the data analysis is cell type identification. Traditional methods usually cluster the cells and manu...
Neural Networks, Computer Algorithms Sequence Analysis, RNA Learning Exome Sequencing Single-Cell Analysis
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Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation.

Physical review letters Mar 4, 2022
The theory of open quantum systems lays the foundation for a substantial part of modern research in quantum science and engineering. Rooted in the dimensionality of their extended Hilbert spaces, the high computational complexity of simulating open q...
Physics Markov Chains Neural Networks, Computer Monte Carlo Method Algorithms
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