AIMC Topic: Computational Biology

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Prediction of Essential Genes in Comparison States Using Machine Learning.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Identifying essential genes in comparison states (EGS) is vital to understanding cell differentiation, performing drug discovery, and identifying disease causes. Here, we present a machine learning method termed Prediction of Essential Genes in Compa...
Genetic Markers Gene Regulatory Networks Computational Biology Genes, Essential Machine Learning Transcriptome
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Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery ...
Binding Sites Sequence Analysis, Protein Chromatin Immunoprecipitation Sequencing Algorithms Transcription Factors Deep Learning Protein Binding DNA-Binding Proteins Computational Biology
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Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) ...
Chromatin Immunoprecipitation Sequencing Algorithms DNA Computational Biology Binding Sites Transcription Factors Humans Machine Learning
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DMFMDA: Prediction of Microbe-Disease Associations Based on Deep Matrix Factorization Using Bayesian Personalized Ranking.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. Firs...
Inflammatory Bowel Diseases Asthma Computational Biology Bayes Theorem Deep Learning Humans Colonic Neoplasms Neural Networks, Computer Host-Pathogen Interactions
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A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to whi...
Bacteriophages Computational Biology DNA, Bacterial Bacteria Algorithms Drug Resistance, Bacterial Sequence Analysis, DNA DNA, Viral Deep Learning Microbial Interactions
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MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
In the past few years, the prediction models have shown remarkable performance in most biological correlation prediction tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. These models often encounte...
Transcriptome Genetic Predisposition to Disease MicroRNAs Computational Biology Machine Learning Algorithms Neoplasms Humans
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AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid seque...
Drug Discovery Computational Biology Models, Statistical Algorithms Peptides Amino Acid Sequence Sequence Analysis, Protein Anti-Inflammatory Agents Machine Learning
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Protein Fold Recognition by Combining Support Vector Machines and Pairwise Sequence Similarity Scores.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Protein fold recognition is one of the most essential steps for protein structure prediction, aiming to classify proteins into known protein folds. There are two main computational approaches: one is the template-based method based on the alignment s...
Algorithms Computational Biology Support Vector Machine Proteins Pattern Recognition, Automated Protein Folding Sequence Analysis, Protein
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iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Identification of lncRNA-disease associations is not only important for exploring the disease mechanism, but will also facilitate the molecular targeting drug discovery. Fusing multiple biological information is able to generate a more comprehensive ...
Computational Biology RNA, Long Noncoding Neural Networks, Computer Algorithms Deep Learning Genetic Predisposition to Disease Neoplasms Humans
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Exploring Molecular Descriptors and Fingerprints to Predict mTOR Kinase Inhibitors using Machine Learning Techniques.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Mammalian Target of Rapamycin (mTOR) is a Ser/Thr protein kinase, and its role is integral to the autophagy pathway in cancer. Targeting mTOR for therapeutic interventions in cancer through autophagy pathway is challenging due to the dual roles of au...
Humans Machine Learning Algorithms Autophagy TOR Serine-Threonine Kinases Drug Discovery Neoplasms Protein Kinase Inhibitors Computational Biology
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