AIMC Topic: Phylogeny
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Harnessing machine learning to guide phylogenetic-tree search algorithms.
Nature communications
Mar 31, 2021
Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems i...
MSA-Regularized Protein Sequence Transformer toward Predicting Genome-Wide Chemical-Protein Interactions: Application to GPCRome Deorphanization.
Journal of chemical information and modeling
Mar 23, 2021
Small molecules play a critical role in modulating biological systems. Knowledge of chemical-protein interactions helps address fundamental and practical questions in biology and medicine. However, with the rapid emergence of newly sequenced genes, t...
Using neural networks to mine text and predict metabolic traits for thousands of microbes.
PLoS computational biology
Mar 2, 2021
Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. I...
Analysis of protein determinants of host-specific infection properties of polyomaviruses using machine learning.
Genes & genomics
Mar 1, 2021
BACKGROUND: The large tumor antigen (LT-Ag) and major capsid protein VP1 are known to play important roles in determining the host-specific infection properties of polyomaviruses (PyVs).
Universal probabilistic programming offers a powerful approach to statistical phylogenetics.
Communications biology
Feb 24, 2021
Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabi...
Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning.
Food chemistry
Jan 15, 2021
Identification of honey origin based on specific chemical markers is important for honey authentication. This study is aimed to differentiate Malaysian stingless bee honey from different entomological origins (Heterotrigona bakeri, Geniotrigona thora...
Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.
Trends in microbiology
Jan 14, 2021
The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity w...
Deep learning for HGT insertion sites recognition.
BMC genomics
Dec 29, 2020
BACKGROUND: Horizontal Gene Transfer (HGT) refers to the sharing of genetic materials between distant species that are not in a parent-offspring relationship. The HGT insertion sites are important to understand the HGT mechanisms. Recent studies in m...
Review of the genus Shilovia Makarchenko (Diptera: Chironomidae: Diamesinae: Boreoheptagyiini) from the mountains of Central Asia, with morphological description and DNA barcoding of known species.
Zootaxa
Dec 15, 2020
Chironomids of the genus Shilovia Makarchenko (Diamesinae, Boreoheptagyiini) from the mountains of Central Asia are revised using both morphological characters and molecular data. Illustrated descriptions of the adult male Shilovia xinhuawangi sp. no...
Ensembling of Gene Clusters Utilizing Deep Learning and Protein-Protein Interaction Information.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2020
Cluster ensemble techniques aim to combine the outputs of multiple clustering algorithms to obtain a single consensus partitioning. The current paper reports about the development of a cluster ensemble based technique combining the concepts of multio...
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