AIMC Topic: Models, Genetic

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Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks.

Molecular biology and evolution Oct 4, 2023
Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which use...
Humans Polymorphism, Single Nucleotide Models, Genetic Genome Multifactorial Inheritance Neural Networks, Computer Computer Simulation Genomics
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Multimodal deep learning methods enhance genomic prediction of wheat breeding.

G3 (Bethesda, Md.) May 2, 2023
While several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep ...
Triticum Plant Breeding Genomics Genotype Models, Genetic Phenotype Deep Learning
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A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Molecular biology and evolution Jan 7, 2022
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method t...
Models, Genetic Selection, Genetic Genetics, Population Recombination, Genetic Deep Learning
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Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.

Bioengineered Dec 1, 2021
Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene signature for an accurate prediction and classification of osteoporosis patients. Three gene expression ...
Osteoporosis Transcriptome Diagnosis, Computer-Assisted Humans Support Vector Machine Protein Interaction Maps Databases, Genetic Computational Biology Models, Genetic
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Deep learning of gene relationships from single cell time-course expression data.

Briefings in bioinformatics Sep 2, 2021
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offe...
Time Factors Animals Models, Genetic Gene Regulatory Networks Gene Expression Profiling Single-Cell Analysis Epistasis, Genetic Mice Deep Learning RNA-Seq Computational Biology Humans Algorithms Cells, Cultured
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Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Briefings in bioinformatics Sep 2, 2021
Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discrimina...
Machine Learning Internet Algorithms Gene Expression Profiling Databases, Factual Humans Models, Genetic Computational Biology Cluster Analysis Gene Expression Regulation, Neoplastic Mutation Reproducibility of Results Animals Neoplasms
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usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme.

Briefings in bioinformatics Sep 2, 2021
Although synonymous mutations do not alter the encoded amino acids, they may impact protein function by interfering with the regulation of RNA splicing or altering transcript splicing. New progress on next-generation sequencing technologies has put t...
Algorithms Reproducibility of Results Computational Biology Models, Genetic Proteins Silent Mutation Humans Machine Learning
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Deep embedded clustering with multiple objectives on scRNA-seq data.

Briefings in bioinformatics Sep 2, 2021
In recent years, single-cell RNA sequencing (scRNA-seq) technologies have been widely adopted to interrogate gene expression of individual cells; it brings opportunities to understand the underlying processes in a high-throughput manner. Deep embedde...
RNA-Seq Computational Biology Models, Genetic Gene Ontology Cluster Analysis Reproducibility of Results Humans Mutation Gene Expression Profiling Neural Networks, Computer Single-Cell Analysis Algorithms
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A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Briefings in bioinformatics Sep 2, 2021
Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to un...
Models, Genetic Escherichia coli Databases, Genetic Bayes Theorem Gene Expression Regulation RNA-Seq Gene Regulatory Networks Computational Biology Biomarkers, Tumor Neoplasms Transcriptome Machine Learning
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SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals import...
Databases, Genetic RNA, Long Noncoding Proteins RNA, Messenger Models, Genetic Support Vector Machine Eukaryota Eukaryotic Cells Humans
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