AIMC Topic: Evolution, Molecular

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Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

Proceedings of the National Academy of Sciences of the United States of America Mar 6, 2019
Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological ...
Deep Learning Models, Theoretical Gene Expression Regulation Base Sequence Transcription, Genetic Sequence Analysis, DNA Evolution, Molecular DNA
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Robust optimization through neuroevolution.

PloS one Mar 1, 2019
We propose a method for evolving neural network controllers robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The method speci...
Neural Networks, Computer Models, Theoretical Evolution, Molecular Gene-Environment Interaction
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Levels and building blocks-toward a domain granularity framework for the life sciences.

Journal of biomedical semantics Jan 28, 2019
BACKGROUND: With the emergence of high-throughput technologies, Big Data and eScience, the use of online data repositories and the establishment of new data standards that require data to be computer-parsable become increasingly important. As a conse...
Biological Science Disciplines Epithelium Biological Ontologies Cell Membrane Evolution, Molecular
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iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Proteins Dec 3, 2018
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using...
Evolution, Molecular Proteins Models, Molecular Machine Learning Protein Binding Computational Biology Proto-Oncogene Proteins c-mdm2 Tumor Suppressor Protein p53 Mutation Thermodynamics Protein Domains Binding, Competitive
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Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.

PLoS genetics Apr 23, 2018
Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is cru...
Software Computer Simulation Animals Models, Genetic Hybridization, Genetic Genetic Speciation Haplotypes Evolution, Molecular Drosophila simulans Genetics, Population Supervised Machine Learning Species Specificity Gene Flow Genome, Insect Genetic Variation Drosophila
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A machine learning based framework to identify and classify long terminal repeat retrotransposons.

PLoS computational biology Apr 23, 2018
Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. A...
Genome, Insect Retroelements Terminal Repeat Sequences Arabidopsis Proteins Arabidopsis Evolution, Molecular Drosophila Proteins Drosophila melanogaster Genome, Plant Conserved Sequence DNA, Plant Animals Computational Biology Decision Trees Machine Learning Software
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Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations.

Scientific reports Mar 23, 2018
Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis - the ontolo...
Molecular Sequence Annotation Gene Ontology Models, Genetic Databases, Genetic Humans Evolution, Molecular Reproducibility of Results Computational Biology
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Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains.

Journal of theoretical biology Feb 22, 2018
The receptor-associated protein (RAP) is an inhibitor of endocytic receptors that belong to the lipoprotein receptor gene family. In this study, a computational approach was tried to find the evolutionarily related fold of the RAP proteins. Through t...
Computational Biology Support Vector Machine Carrier Proteins Evolution, Molecular Models, Molecular Databases, Protein Protein Conformation Structure-Activity Relationship
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An Improved Binary Differential Evolution Algorithm for Feature Selection in Molecular Signatures.

Molecular informatics Nov 6, 2017
The discovery of biomarkers from high-dimensional data is a very challenging task in cancer diagnoses. On the one hand, biomarker discovery is the so-called high-dimensional small-sample problem. On the other hand, these data are redundant and noisy....
Support Vector Machine Humans Evolution, Molecular Biomarkers, Tumor Computational Biology
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iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features.

Journal of theoretical biology Sep 21, 2017
Bacteriophage proteins are viruses that can significantly impact on the functioning of bacteria and can be used in phage based therapy. The functioning of Bacteriophage in the host bacteria depends on its location in those host cells. It is very impo...
Viral Proteins Intracellular Space Cell Compartmentation Models, Biological Host-Pathogen Interactions Bacteriophages Support Vector Machine Evolution, Molecular
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