AIMC Topic: Phylogeny
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EMBER: multi-label prediction of kinase-substrate phosphorylation events through deep learning.
Bioinformatics (Oxford, England)
Apr 12, 2022
MOTIVATION: Kinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. Although on the order of 105 p...
Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.
Translational vision science & technology
Apr 1, 2022
PURPOSE: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learni...
AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.
Briefings in bioinformatics
Nov 5, 2021
With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But c...
The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis.
Briefings in bioinformatics
Nov 5, 2021
Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. ...
Using graph convolutional neural networks to learn a representation for glycans.
Cell reports
Jun 15, 2021
As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes-from protein folding to viral cell entry-yet are still ...
A novel reassortant avian influenza H4N6 virus isolated from an environmental sample during a surveillance in Maharashtra, India.
The Indian journal of medical research
Jun 1, 2021
BACKGROUND & OBJECTIVES: Low pathogenic avian influenza (LPAI) viruses cause mild clinical illness in domestic birds. Migratory birds are a known reservoir for all subtypes of avian influenza (AI) viruses. The objective of the study was to characteri...
A novel deep learning method for predictive modeling of microbiome data.
Briefings in bioinformatics
May 20, 2021
With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapid expanding research field, which provides an unprecedented opportunity in various clinical applications such as d...
Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2021
Escherichia coli is a species of bacteria that can be present in a wide variety of mammalian hosts and potentially soil environments. E. coli has an open genome and can show considerable diversity in gene content between isolates. It is a reasonable ...
Supervised learning on phylogenetically distributed data.
Bioinformatics (Oxford, England)
Dec 30, 2020
MOTIVATION: The ability to develop robust machine-learning (ML) models is considered imperative to the adoption of ML techniques in biology and medicine fields. This challenge is particularly acute when data available for training is not independent ...
Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks.
Molecular biology and evolution
Dec 16, 2020
Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that...
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