AIMC Topic: Computational Biology
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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks.
Computers in biology and medicine
Dec 8, 2024
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
Dec 7, 2024
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
Dec 6, 2024
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
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...
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