AIMC Journal:
Nucleic acids research

Showing 161 to 170 of 228 articles

FireProtDB: database of manually curated protein stability data.

Nucleic acids research Jan 8, 2021
The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevate...
Datasets as Topic Molecular Sequence Annotation Point Mutation Databases, Protein Models, Molecular Internet Protein Stability Machine Learning Software Computational Biology Proteins
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The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases.

Nucleic acids research Jan 8, 2021
Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases su...
Internet Phosphotransferases Knowledge Bases Gene Expression Regulation, Enzymologic
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SilencerDB: a comprehensive database of silencers.

Nucleic acids research Jan 8, 2021
Gene regulatory elements, including promoters, enhancers, silencers, etc., control transcriptional programs in a spatiotemporal manner. Though these elements are known to be able to induce either positive or negative transcriptional control, the comm...
Rats Cell Line Internet Buffaloes Animals User-Computer Interface Mice Chickens Sus scrofa Databases, Nucleic Acid Silencer Elements, Transcriptional Organ Specificity Drosophila melanogaster Molecular Sequence Annotation Transcription, Genetic Humans Machine Learning
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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...
Machine Learning Software Bacterial Proteins Escherichia coli Transcription, Genetic Internet Gene Expression Profiling Staphylococcus aureus Transcriptome Knowledge Bases Bacillus subtilis Gene Regulatory Networks Gene Expression Regulation, Bacterial Databases, Genetic Transcription Factors
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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 ...
RNA, Long Noncoding Dengue Virus HIV Zebrafish Mice RNA, Messenger Rats MicroRNAs Herpesvirus 8, Human Atlases as Topic Knowledge Bases Animals Arabidopsis Adenosine Datasets as Topic Humans Swine Pan troglodytes Saccharomyces cerevisiae Transcriptome Drosophila melanogaster
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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 ...
Humans Neural Networks, Computer Machine Learning Computational Biology Transcriptome Monocytes Single-Cell Analysis Gene Expression Regulation Databases, Genetic Flow Cytometry Image Processing, Computer-Assisted Female Fetal Blood Mice, Inbred C57BL Mice High-Throughput Nucleotide Sequencing Animals Myeloid Progenitor Cells Male Gene Expression Profiling
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DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research Jul 27, 2020
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...
Mice Computational Biology RNA-Binding Proteins Mutation Humans Computer Simulation Animals Deep Learning Protein Binding RNA Nucleotide Motifs Base Sequence Nucleic Acid Conformation Binding Sites
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mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization.

Nucleic acids research Jul 2, 2020
Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regul...
Software Endoplasmic Reticulum Computer Simulation Support Vector Machine RNA, Messenger Mitochondria Sequence Analysis, RNA Cell Nucleus Cytoplasm
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MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization.

Nucleic acids research Jul 2, 2020
MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which res...
Proteins Models, Molecular Internet Protein Conformation Computer Graphics Software Sequence Analysis, Protein Deep Learning Protein Processing, Post-Translational
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Prediction of condition-specific regulatory genes using machine learning.

Nucleic acids research Jun 19, 2020
Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has bec...
Stress, Physiological RNA-Seq Arabidopsis Proteins Promoter Regions, Genetic Machine Learning Gene Regulatory Networks Arabidopsis Gene Expression Regulation, Plant Gene Expression Profiling Transcription Factors Genes, Regulator Genes, Plant Deep Learning Datasets as Topic Single-Cell Analysis
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