AIMC Topic: Gene Regulatory Networks

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SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

Briefings in bioinformatics Jan 17, 2022
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using ...
Algorithms Gene Expression Profiling Deep Learning Single-Cell Analysis Humans Neural Networks, Computer RNA-Seq Gene Regulatory Networks Sequence Analysis, RNA Transcription Factors Cluster Analysis Gene Expression Regulation
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A deep learning model to identify gene expression level using cobinding transcription factor signals.

Briefings in bioinformatics Jan 17, 2022
Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the...
Gene Regulatory Networks Transcription Factors Gene Expression Gene Expression Regulation Deep Learning
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scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species.

Nucleic acids research Jan 7, 2022
Previous studies on enhancers and their target genes were largely based on bulk samples that represent 'average' regulatory activities from a large population of millions of cells, masking the heterogeneity and important effects from the sub-populati...
Internet Genetic Heterogeneity Gene Expression Regulation Promoter Regions, Genetic Enhancer Elements, Genetic Gene Regulatory Networks Organ Specificity Chromatin Mice Drosophila melanogaster Databases, Genetic Humans Molecular Sequence Annotation Consensus Sequence Unsupervised Machine Learning Software Cell Lineage Eukaryotic Cells Animals Single-Cell Analysis
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Journal of biosciences Jan 1, 2022
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational biomodelling approaches with different hybri...
Gene Regulatory Networks Computational Biology Artificial Intelligence Machine Learning Algorithms Computer Simulation
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GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology Jan 1, 2022
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive abil...
Computational Biology Escherichia coli Bias Supervised Machine Learning Single-Cell Analysis RNA-Seq Gene Regulatory Networks Mice Saccharomyces cerevisiae Databases, Nucleic Acid Humans Neural Networks, Computer Algorithms Computer Simulation Animals Deep Learning
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Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

Bioscience reports Dec 22, 2021
BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patien...
Signal Transduction Databases, Genetic Predictive Value of Tests Case-Control Studies Breast Neoplasms Humans Protein Interaction Maps Biomarkers, Tumor Machine Learning Gene Regulatory Networks Gene Expression Profiling Prognosis Gene Expression Regulation, Neoplastic Transcriptome Female
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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.

Briefings in bioinformatics Nov 5, 2021
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This 'black box' problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we pr...
Cluster Analysis Area Under Curve Reproducibility of Results Humans Biomarkers, Tumor Male Neoplasms Machine Learning Algorithms Female Gene Expression Profiling Genomics Deep Learning Computational Biology ROC Curve Signal Transduction Gene Regulatory Networks Databases, Genetic
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Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

Briefings in bioinformatics Nov 5, 2021
MOTIVATION: Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic m...
Gene Regulatory Networks Models, Theoretical MicroRNAs Machine Learning Female Breast Neoplasms Disease Progression Humans
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Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Briefings in bioinformatics Nov 5, 2021
Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of...
Humans Machine Learning Female Gene Expression Profiling Gene Regulatory Networks Gene Expression Regulation, Neoplastic Ovarian Neoplasms Drug Resistance, Neoplasm Databases, Nucleic Acid
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Deep learning of gene relationships from single cell time-course expression data.

Briefings in bioinformatics Sep 2, 2021
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offe...
Single-Cell Analysis Computational Biology RNA-Seq Animals Models, Genetic Humans Gene Regulatory Networks Gene Expression Profiling Epistasis, Genetic Algorithms Mice Deep Learning Cells, Cultured Time Factors
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