AIMC Topic: Models, Genetic

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Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.

Briefings in bioinformatics May 20, 2021
DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understandi...
Machine Learning Rosaceae Oryza Base Sequence Species Specificity DNA, Plant Arabidopsis Adenosine Genome, Plant Models, Genetic Support Vector Machine Internet Algorithms Computational Biology Epigenesis, Genetic
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Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning.

Briefings in bioinformatics May 20, 2021
MOTIVATION: Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed unde...
Models, Genetic MicroRNAs RNA, Long Noncoding Predictive Value of Tests Disease Humans Machine Learning
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MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs.

Briefings in bioinformatics May 20, 2021
The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNA...
Computational Biology Models, Genetic RNA, Long Noncoding Disease Humans Deep Learning
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Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data.

Molecular biology and evolution Mar 9, 2021
Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for function...
Neural Networks, Computer Software Gene Expression Gene Duplication Drosophila Animals Models, Genetic Evolution, Molecular
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Categorization of birth weight phenotypes for inclusion in genetic evaluations using a deep neural network.

Journal of animal science Mar 1, 2021
Birth weight (BW) serves as a valuable indicator of the economically relevant trait of calving ease (CE), and erroneous data collection for BW could impact genetic evaluations for CE. The objective of the current study was to evaluate the use of deep...
Data Collection Goals Animals Models, Genetic Birth Weight Neural Networks, Computer Phenotype
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Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Nucleic acids research Jan 11, 2021
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise ...
Binding Sites Linkage Disequilibrium Genetic Diseases, Inborn Chromatin Immunoprecipitation Histone Code Datasets as Topic Organ Specificity Molecular Sequence Annotation Transcription Factors Causality Epigenome Humans Deep Learning Epigenomics Models, Genetic Genome-Wide Association Study Polymorphism, Single Nucleotide
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Epistasis Analysis: Classification Through Machine Learning Methods.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for ...
Datasets as Topic Polymorphism, Single Nucleotide Humans Multifactorial Inheritance Computational Biology Epistasis, Genetic Machine Learning Models, Genetic Multifactor Dimensionality Reduction Software
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Epistasis Detection Based on Epi-GTBN.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Epistasis detection is a hot topic in bioinformatics due to its relevance to the detection of specific phenotypic traits and gene-gene interactions. Here, we present a step-by-step protocol to apply Epi-GTBN, a machine learning-based method based on ...
Genotype Models, Genetic Bayes Theorem Genetic Loci Epistasis, Genetic Machine Learning Data Mining Humans Phenotype Datasets as Topic Software Gene Regulatory Networks Polymorphism, Single Nucleotide
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A Belief Degree-Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Epistasis is a challenge in prediction, classification, and suspicion of human genetic diseases. Many technologies, methods, and tools have been developed for epistasis detection. Multifactor dimensionality reduction (MDR) is the method commonly used...
Epistasis, Genetic Uncertainty Multifactor Dimensionality Reduction Multifactorial Inheritance Humans Fuzzy Logic Coronary Artery Disease Software Polymorphism, Single Nucleotide Datasets as Topic Algorithms Models, Genetic Databases, Genetic
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Deep Neural Networks for Epistatic Sequence Analysis.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
We report a step-by-step protocol to use pysster, a TensorFlow-based package for building deep neural networks on a broad range of epistatic sequences such as DNA, RNA, or annotated secondary structure sequences. Pysster provides users comprehensive ...
Software Deep Learning Humans RNA Editing ROC Curve Datasets as Topic Epistasis, Genetic Models, Genetic Base Sequence Sequence Analysis RNA
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