AIMC Topic: Transcriptome

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DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism.

Nucleic acids research May 7, 2021
Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular events. Computa...
Neural Networks, Computer Transcriptome Computational Biology RNA, Messenger Proteomics Gene Ontology Humans Ribosomes Cytosol Subcellular Fractions Databases, Genetic Endoplasmic Reticulum Databases, Protein Exosomes Cell Nucleus Cell Membrane
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Modeling multi-species RNA modification through multi-task curriculum learning.

Nucleic acids research Apr 19, 2021
N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, ...
Datasets as Topic Gene Expression Regulation Adenosine Humans Transcriptome Machine Learning RNA-Binding Proteins Software Sequence Analysis, RNA Animals Databases, Genetic RNA
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Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

Cell reports Apr 13, 2021
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-fre...
Humans Precision Medicine Female Adult Asthma Adolescent Child, Preschool Child Feces Proteomics Male Middle Aged Gene Expression Profiling Transcriptome Case-Control Studies Nasal Cavity Allergens Epigenome Datasets as Topic Cluster Analysis Gene Expression Regulation Unsupervised Machine Learning Atlases as Topic Benchmarking Microbiota
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Deep learning in systems medicine.

Briefings in bioinformatics Mar 22, 2021
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features nee...
Proteomics Metabolomics Disease Systems Analysis Humans Neural Networks, Computer Algorithms Biomarkers Precision Medicine Electronic Health Records Transcriptome Deep Learning Genomics
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Integration and transfer learning of single-cell transcriptomes via cFIT.

Proceedings of the National Academy of Sciences of the United States of America Mar 9, 2021
Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods...
Transcriptome Single-Cell Analysis RNA-Seq Mice Exome Sequencing Humans Machine Learning Software Animals
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iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

Nucleic acids research Jan 8, 2021
Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their ass...
Bacillus subtilis Databases, Genetic Bacterial Proteins Internet Knowledge Bases Gene Expression Regulation, Bacterial Transcription, Genetic Transcriptome Gene Regulatory Networks Transcription Factors Escherichia coli Staphylococcus aureus Machine Learning Software Gene Expression Profiling
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m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.

Nucleic acids research Jan 8, 2021
N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and disease pathogenesis. We present here a comprehensive knowledgebase, m6A-Atlas, for unraveling the ...
Humans Animals Zebrafish Rats Knowledge Bases Swine Herpesvirus 8, Human MicroRNAs Datasets as Topic Transcriptome Adenosine RNA, Long Noncoding Saccharomyces cerevisiae Mice Atlases as Topic Drosophila melanogaster Dengue Virus RNA, Messenger Arabidopsis HIV Pan troglodytes
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Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.

Nucleic acids research Nov 18, 2020
High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used ...
Animals Myeloid Progenitor Cells Male Gene Expression Profiling Mice, Inbred C57BL Transcriptome Flow Cytometry Humans Single-Cell Analysis High-Throughput Nucleotide Sequencing Image Processing, Computer-Assisted Neural Networks, Computer Gene Expression Regulation Mice Machine Learning Fetal Blood Computational Biology Female Monocytes Databases, Genetic
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Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI-based approach.

Chemical biology & drug design Sep 1, 2020
The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction o...
Humans Machine Learning Survival Rate Antineoplastic Agents Neoplasms Transcriptome Cohort Studies Polymorphism, Single Nucleotide Mutation
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Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.

Journal of the American Medical Informatics Association : JAMIA May 1, 2020
OBJECTIVE: Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been sys...
Transcriptome Humans Neural Networks, Computer ROC Curve Machine Learning Lung Neoplasms Carcinoma, Squamous Cell Carcinoma, Non-Small-Cell Lung Adenocarcinoma of Lung
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