AIMC Topic: RNA-Seq

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DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types.

BMC medical genomics Feb 24, 2020
BACKGROUND: Breast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an import...
Humans Female RNA-Seq Models, Biological RNA, Neoplasm Kinetochores Breast Neoplasms Biomarkers, Tumor Transcriptome Deep Learning
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Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.

BMC bioinformatics Feb 24, 2020
BACKGROUND: MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional da...
Huntingtin Protein Gene Knock-In Techniques Gene Expression Regulation Trinucleotide Repeats Huntington Disease RNA-Seq Humans Mice Proteomics Machine Learning Disease Models, Animal Brain MicroRNAs RNA, Messenger Animals Neurons
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Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.

Scientific reports Jan 27, 2020
Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To add...
Humans Machine Learning Biomarkers, Tumor RNA, Messenger Proteome DNA Methylation MicroRNAs Transcriptome Cell Line, Tumor RNA-Seq Pancreatic Neoplasms Exome Sequencing Transcription, Genetic
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Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.

IEEE journal of biomedical and health informatics Dec 23, 2019
Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligen...
Humans RNA-Seq Machine Learning Computational Biology Skin Neoplasms Biomarkers, Tumor Gene Expression Profiling Diagnosis, Computer-Assisted
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Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach.

Genomics Dec 6, 2019
Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miR...
Sequence Homology, Nucleic Acid RNA Precursors Nucleotide Motifs Genome Genomics Liver MicroRNAs Muscle, Skeletal Genetic Loci Molecular Sequence Annotation Sus scrofa Algorithms Animals Transcriptome RNA-Seq Machine Learning
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Global transcriptome analysis reveals relevant effects at environmental concentrations of cypermethrin in honey bees (Apis mellifera).

Environmental pollution (Barking, Essex : 1987) Dec 4, 2019
Cypermethrin is a frequently used insecticide in agriculture and households but its chronic and molecular effects are poorly known are . Here we describe effects of sublethal cypermethrin exposure on the global transcriptome in the brain of honey bee...
Environmental Pollutants Pyrethrins Genetic Markers Bees Animals Insecticides Gene Expression Profiling Reverse Transcriptase Polymerase Chain Reaction Transcriptome RNA-Seq RNA, Messenger Sequence Analysis, RNA
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Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data.

Genomics Nov 20, 2019
This paper presents a Grouping Genetic Algorithm (GGA) to solve a maximally diverse grouping problem. It has been applied for the classification of an unbalanced database of 801 samples of gene expression RNA-Seq data in 5 types of cancer. The sample...
Unsupervised Machine Learning Humans Neoplasms RNA-Seq Gene Expression Regulation, Neoplastic
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Compendiums of cancer transcriptomes for machine learning applications.

Scientific data Oct 8, 2019
There are massive transcriptome profiles in the form of microarray. The challenge is that they are processed using diverse platforms and preprocessing tools, requiring considerable time and informatics expertise for cross-dataset analyses. If there e...
Transcriptome RNA-Seq Oligonucleotide Array Sequence Analysis Machine Learning Algorithms Neoplasms Humans
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Pathway-Based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests.

IEEE journal of biomedical and health informatics Oct 1, 2019
Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell popul...
Algorithms Cells, Cultured Animals Single-Cell Analysis RNA-Seq Gene Regulatory Networks Mice Machine Learning Cluster Analysis Decision Trees Stem Cells
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A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell Jun 6, 2019
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over ...
Models, Genetic Humans Databases, Genetic Transcriptome Gene Expression HEK293 Cells Polyadenylation Deep Learning Synthetic Biology Base Sequence 3' Untranslated Regions RNA-Seq Mutagenesis RNA Cleavage RNA, Messenger
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