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Computational Biology

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Prediction of Inter-Residue Multiple Distances and Exploration of Protein Multiple Conformations by Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
AlphaFold2 has achieved a major breakthrough in end-to-end prediction for static protein structures. However, protein conformational change is considered to be a key factor in protein biological function. Inter-residue multiple distances prediction i...

Transcription Factor Binding Site Prediction Using CnNet Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Controlling the gene expression is the most important development in a living organism, which makes it easier to find different kinds of diseases and their causes. It's very difficult to know what factors control the gene expression. Transcription Fa...

RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the mechanisms of biological processes, developing PPI networks, and detecting protein functions. Currently, most computational methods primarily concentr...

A Multi-Task Deep Feature Selection Method for Brain Imaging Genetics.

IEEE/ACM transactions on computational biology and bioinformatics
Using brain imaging quantitative traits (QTs) for identifying genetic risk factors is an important research topic in brain imaging genetics. Many efforts have been made for this task via building linear models between imaging QTs and genetic factors ...

ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks.

Computers in biology and medicine
Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding si...

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Immunogenetics
Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study a...

Simple and effective embedding model for single-cell biology built from ChatGPT.

Nature biomedical engineering
Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatG...

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.

Journal of computational biology : a journal of computational molecular cell biology
Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have be...

Protein-protein interaction detection using deep learning: A survey, comparative analysis, and experimental evaluation.

Computers in biology and medicine
This survey paper provides a comprehensive analysis of various Deep Learning (DL) techniques and algorithms for detecting protein-protein interactions (PPIs). It examines the scalability, interpretability, accuracy, and efficiency of each technique, ...

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Computers in biology and medicine
Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable...