AIMC Topic: Datasets as Topic

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NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Briefings in bioinformatics Sep 2, 2021
Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative way...
Benchmarking Deep Learning Datasets as Topic Protein Binding RNA, Untranslated Proteins Internet Humans Software Sensitivity and Specificity
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ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxic...
Computational Biology Evolution, Molecular Databases, Protein Datasets as Topic Neural Networks, Computer Humans Machine Learning Software Peptides
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iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics Sep 2, 2021
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...
Deoxyribonuclease I Enhancer Elements, Genetic Arabidopsis Chromatin Humans Software Deep Learning Protein Binding Transcription Factors Genetic Loci DNA Datasets as Topic Internet Promoter Regions, Genetic Oryza Transcription, Genetic
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SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.

Briefings in bioinformatics Sep 2, 2021
Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biologica...
Deep Learning Gene Regulatory Networks Computational Biology Databases, Genetic Area Under Curve Myocardial Infarction Osteoarthritis Multiple Sclerosis Humans Risk Factors Neoplasms Datasets as Topic Gene Expression Regulation RNA, Circular
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Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.

Briefings in bioinformatics Sep 2, 2021
Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein inte...
Proteins Software Molecular Docking Simulation Datasets as Topic Humans Binding Sites Protein Interaction Domains and Motifs Deep Learning Protein Multimerization Protein Binding Protein Conformation, alpha-Helical Protein Conformation, beta-Strand
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Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.

Briefings in bioinformatics Sep 2, 2021
Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface marker...
RNA Sequence Analysis, RNA Datasets as Topic Cluster Analysis Erythroid Cells Immunophenotyping Lymphocytes Humans Deep Learning Single-Cell Analysis RNA-Seq
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A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics Sep 2, 2021
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...
Nucleotide Motifs Chromatin CCCTC-Binding Factor Gene Expression Regulation Genome, Human Molecular Conformation K562 Cells Humans Neural Networks, Computer Software MCF-7 Cells Datasets as Topic
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Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Briefings in bioinformatics Sep 2, 2021
Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While methods to interpret single-cell transcriptomics data are developing rapidly, challenges in most analysis ...
Humans Leukocytes, Mononuclear Transcriptome Single-Cell Analysis Mice Cells, Cultured Phenotype Brain Animals Logistic Models Benchmarking Support Vector Machine Datasets as Topic Lymphocytes, Tumor-Infiltrating Pancreas
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XGSEA: CROSS-species gene set enrichment analysis via domain adaptation.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. Howeve...
Embryo, Mammalian Datasets as Topic Computational Biology Gene Expression Regulation, Neoplastic Ovarian Neoplasms Zebrafish Machine Learning Female Melanoma Skin Neoplasms Brain Neoplasms Animals T-Lymphocytes Humans Mice
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DeepCNV: a deep learning approach for authenticating copy number variations.

Briefings in bioinformatics Sep 2, 2021
Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably hig...
Humans Deep Learning Datasets as Topic Genome, Human False Positive Reactions Disease ROC Curve DNA Copy Number Variations Area Under Curve Benchmarking
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