AIMC Topic: Transcriptome

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Potential applications of deep learning in single-cell RNA sequencing analysis for cell therapy and regenerative medicine.

Stem cells (Dayton, Ohio) Feb 15, 2021
When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardle...
Humans RNA-Seq Regenerative Medicine Cell- and Tissue-Based Therapy Transcriptome Deep Learning Single-Cell Analysis
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CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgra...
Humans DNA Copy Number Variations Neoplasms Machine Learning Transcriptome Computational Biology
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A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are...
Saccharomyces cerevisiae Proteins Intracellular Space Protein Interaction Maps Computational Biology Protein Interaction Mapping Transcriptome Deep Learning
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Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.

BMC cardiovascular disorders Jan 28, 2021
BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learnin...
Databases, Genetic Gene Expression Profiling Bayes Theorem Heart Atria Transcriptome Female Protein Interaction Maps Humans Atrial Fibrillation Heart Valve Diseases Case-Control Studies Proteomics Gene Regulatory Networks Male Machine Learning Oligonucleotide Array Sequence Analysis Middle Aged Signal Transduction Genetic Association Studies
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Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals.

International journal of molecular sciences Jan 22, 2021
One of the important questions in aging research is how differences in transcriptomics are associated with the longevity of various species. Unfortunately, at the level of individual genes, the links between expression in different organs and maximum...
RNA-Seq Computational Biology Models, Genetic Liver Regression Analysis Humans Bayes Theorem Machine Learning Aging Algorithms Tissue Distribution Gene Expression Brain Gene Expression Profiling Longevity Mammals Animals Transcriptome Linear Models
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Mining influential genes based on deep learning.

BMC bioinformatics Jan 22, 2021
BACKGROUND: Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression prof...
Transcriptome Deep Learning Gene Expression Profiling Genome Genomics
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On transformative adaptive activation functions in neural networks for gene expression inference.

PloS one Jan 14, 2021
Gene expression profiling was made more cost-effective by the NIH LINCS program that profiles only ∼1, 000 selected landmark genes and uses them to reconstruct the whole profile. The D-GEX method employs neural networks to infer the entire profile. H...
Humans Neural Networks, Computer Gene Expression Profiling Transcriptome Gene Regulatory Networks Computational Biology
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Prediction of future gene expression profile by analyzing its past variation pattern.

Gene expression patterns : GEP Jan 12, 2021
A number of initial Hematopoietic Stem Cells (HSC) are considered in a container that are able to divide into HSCs or differentiate into various types of descendant cells. In this paper, a method is designed to predict an approximate gene expression ...
Hematopoiesis Gene Expression Regulation, Developmental Hematopoietic Stem Cells Gene Expression Profiling Transcriptome Humans Neural Networks, Computer
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Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction.

Biology direct Jan 9, 2021
MOTIVATION: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be pre...
Risk Assessment Drug Discovery Transcriptome Humans Machine Learning Algorithms Cell Line Chemical and Drug Induced Liver Injury
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Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform.

BMC bioinformatics Jan 7, 2021
BACKGROUND: Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression d...
Software Transcriptome Computational Biology Artificial Intelligence Pharmacology Databases, Genetic Pharmaceutical Preparations
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