AIMC Topic: Computational Biology
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DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.
Journal of computational biology : a journal of computational molecular cell biology
Dec 6, 2024
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
Dec 6, 2024
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
Dec 6, 2024
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...
[PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies.
Bulletin of mathematical biology
Dec 6, 2024
The prion phenotype in yeast manifests as a white, pink, or red color pigment. Experimental manipulations destabilize prion phenotypes, and allow colonies to exhibit (red) sectored phenotypes within otherwise completely white colonies. Further inve...
Synergic Integration of the miRNome, Machine Learning and Bioinformatics for the Identification of Potential Disease-Modifying Agents in Obstructive Sleep Apnea.
Archivos de bronconeumologia
Dec 5, 2024
INTRODUCTION: Understanding the diverse pathogenetic pathways in obstructive sleep apnea (OSA) is crucial for improving outcomes. microRNA (miRNA) profiling is a promising strategy for elucidating these mechanisms.
Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings.
Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology
Dec 5, 2024
This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and integer encoding. Aimed at bioinformatics and computational biology students, our learning interven...
AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.
International journal of molecular sciences
Dec 5, 2024
MicroRNAs (miRNAs) play a crucial role in gene regulation and are strongly linked to various diseases, including cancer. This study presents AEmiGAP, an advanced deep learning model that integrates autoencoders with long short-term memory (LSTM) netw...
AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.
Scientific reports
Dec 5, 2024
AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these...
Geometric Molecular Graph Representation Learning Model for Drug-Drug Interactions Prediction.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Therefore, it is important to predict potential drug interactions since it can provide combination strategies of drugs...
Multi-View Multiattention Graph Learning With Stack Deep Matrix Factorization for circRNA-Drug Sensitivity Association Identification.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Identifying circular RNA (circRNA)-drug sensitivity association (CDsA) is crucial for advancing drug development. As conducting traditional wet experiments for determining CDsA is costly and inefficient, calculation methods have already proven to be ...
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