AIMC Topic: Evolution, Molecular

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Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning.

Nature communications
A neutral network connects all genotypes with equivalent phenotypes in a fitness landscape and plays an important role in the mutational robustness and evolvability of biomolecules. In contrast to earlier theoretical works, evidence of large neutral ...

Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography.

BMC genomics
BACKGROUND: We combined an unsupervised learning methodology for analyzing mitogenome sequences with maximum likelihood (ML) phylogenetics to make detailed inferences about the evolution and diversification of mitochondrial DNA (mtDNA) haplogroup U5,...

Re-evaluating Deep Neural Networks for Phylogeny Estimation: The Issue of Taxon Sampling.

Journal of computational biology : a journal of computational molecular cell biology
Deep neural networks (DNNs) have been recently proposed for quartet tree phylogeny estimation. Here, we present a study evaluating recently trained DNNs in comparison to a collection of standard phylogeny estimation methods on a heterogeneous collect...

Computed structures of core eukaryotic protein complexes.

Science (New York, N.Y.)
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coe...

Co-evolution based machine-learning for predicting functional interactions between human genes.

Nature communications
Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary question...

TwinCons: Conservation score for uncovering deep sequence similarity and divergence.

PLoS computational biology
We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a 'cost' of transforming one ...

Disease variant prediction with deep generative models of evolutionary data.

Nature
Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational...

Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.

Scientific reports
The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of protei...

Revisiting the out of Africa event with a deep-learning approach.

American journal of human genetics
Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, s...

ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

Nature communications
Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited....