AIMC Topic: Genome, Human

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On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn't.

Genes Apr 5, 2021
In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transfor...
Humans Artificial Intelligence Selection, Genetic Adaptation, Physiological Genetics, Population Genetic Drift Machine Learning Algorithms Evolution, Molecular Genome, Human
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DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Mar 8, 2021
Approximately 15% of human cancers are estimated to be attributed to viruses. Virus sequences can be integrated into the host genome, leading to genomic instability and carcinogenesis. Here, a new deep convolutional neural network (CNN) model is deve...
Humans Genomic Instability Hepatitis B virus Reproducibility of Results Virus Integration Herpesvirus 4, Human Herpesvirus 1, Human Neoplasms Cluster Analysis Oncogenic Viruses Deep Learning Genome, Human
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Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning.

Nature communications Mar 8, 2021
Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate into...
Genome, Human DNA, Intergenic Sequence Analysis, DNA Whole Genome Sequencing Genetic Variation Humans Deep Learning Genomics
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Creating artificial human genomes using generative neural networks.

PLoS genetics Feb 4, 2021
Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create re...
Databases, Factual Computer Simulation Deep Learning Polymorphism, Single Nucleotide Neural Networks, Computer Machine Learning Algorithms Population Chromosomes, Human, Pair 15 HapMap Project Databases, Genetic Alleles Markov Chains Genome, Human Humans
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Systematic analysis of binding of transcription factors to noncoding variants.

Nature Jan 27, 2021
Many sequence variants have been linked to complex human traits and diseases, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human trans...
Support Vector Machine Ligands Genome, Human Disease Binding Sites SELEX Aptamer Technique Humans Protein Binding Polymorphism, Single Nucleotide Transcription Factors
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A Revised Model of Anatomically Modern Human Expansions Out of Africa through a Machine Learning Approximate Bayesian Computation Approach.

Genes Dec 16, 2020
There is a wide consensus in considering Africa as the birthplace of anatomically modern humans (AMH), but the dispersal pattern and the main routes followed by our ancestors to colonize the world are still matters of debate. It is still an open ques...
Human Migration Hominidae Neanderthals Europe Bayes Theorem Computer Simulation Genetic Variation Animals Humans Genome, Human Africa Machine Learning Racial Groups Models, Theoretical Asia Biological Evolution History, Ancient
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SVFX: a machine learning framework to quantify the pathogenicity of structural variants.

Genome biology Nov 9, 2020
There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity score...
Humans Machine Learning Genomics Epigenomics Virulence Genome, Human Oncogenes Genomic Structural Variation
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Predicting 3D genome folding from DNA sequence with Akita.

Nature methods Oct 12, 2020
In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key regulators; perturbing the levels of either greatly disrupts genome-wide foldin...
Cohesins Cell Cycle Proteins Humans CCCTC-Binding Factor Neural Networks, Computer Chromosomal Proteins, Non-Histone Models, Genetic Sequence Analysis, DNA DNA-Binding Proteins Gene Expression Regulation Genome, Human
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DeepC: predicting 3D genome folding using megabase-scale transfer learning.

Nature methods Oct 12, 2020
Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from...
Genome, Human Chromatin CCCTC-Binding Factor Humans Genomic Structural Variation Genomics Neural Networks, Computer Models, Genetic Computer Simulation Base Sequence
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Sequence-to-function deep learning frameworks for engineered riboregulators.

Nature communications Oct 7, 2020
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules....
Mutagenesis Biotechnology Genetic Engineering Riboswitch Base Sequence Structure-Activity Relationship Genome, Human Natural Language Processing Genome, Viral Datasets as Topic Synthetic Biology Humans Computer Simulation Deep Learning Models, Genetic
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