AIMC Topic: Gene Expression

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Interpretation of deep learning in genomics and epigenomics.

Briefings in bioinformatics May 20, 2021
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies tow...
Genomics Computational Biology Deep Learning Protein Binding Gene Expression RNA Chromatin Epigenesis, Genetic DNA Neural Networks, Computer
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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...
Evolution, Molecular Gene Expression Drosophila Gene Duplication Animals Models, Genetic Neural Networks, Computer Software
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Gene Classification Based on Multi-Class SVMs with Systematic Sampling and Hierarchical Clustering (SSHC) Algorithm.

Advances in experimental medicine and biology Jan 1, 2021
The support vector machines (SVMs) is one of the machine learning algorithms with high classification accuracy. However, the support vector machine algorithm has a very high training complexity. Thus, it is not very efficient with large datasets. In ...
Humans Support Vector Machine Cluster Analysis Gene Expression Machine Learning Algorithms
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Adversarial deconfounding autoencoder for learning robust gene expression embeddings.

Bioinformatics (Oxford, England) Dec 30, 2020
MOTIVATION: Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in larg...
Gene Expression Neural Networks, Computer
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Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data.

Journal of Alzheimer's disease : JAD Jan 1, 2020
BACKGROUND: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) resear...
Gene Ontology Encephalitis Computational Biology Microglia Alzheimer Disease Molecular Sequence Annotation Gene Expression Humans
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Statistical and Machine Learning Methods for eQTL Analysis.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2020
An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regula...
Chromosome Mapping Genome-Wide Association Study Computational Biology Gene Expression Quantitative Trait Loci Data Interpretation, Statistical Humans Machine Learning
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Computational functional genomics-based reduction of disease-related gene sets to their key components.

Bioinformatics (Oxford, England) Jul 15, 2019
MOTIVATION: The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction a...
Computational Biology Gene Expression Software Knowledge Bases Genomics
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Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

Bioinformatics (Oxford, England) Jul 1, 2019
MOTIVATION: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predic...
Genome Computational Biology Neural Networks, Computer Gene Expression Gene Regulatory Networks
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DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications.

Bioinformatics (Oxford, England) Sep 1, 2018
MOTIVATION: Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the functional heterogeneity of cells through influencing different...
Software Histones Humans Histone Code Protein Processing, Post-Translational Gene Expression Machine Learning
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[From symptom to syndrome using modern software support].

Der Internist Aug 1, 2018
Diagnosing rare diseases can be challenging for clinicians. This article gives an overview on novel approaches, which enable automated phenotype-driven analyses of differential diagnoses for rare diseases as well as genomic variation data of affected...
Gene Ontology Genomics Gene Expression Algorithms Humans Models, Biological Rare Diseases Computational Biology Phenotype Syndrome Software Biological Ontologies
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