AIMC Topic: Signal Transduction

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Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference.

PloS one Feb 8, 2021
In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can r...
Models, Theoretical CD4-Positive T-Lymphocytes Biomedical Research Humans Computational Biology Neural Networks, Computer Signal Transduction Protein Interaction Maps Machine Learning
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Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.

BMC cardiovascular disorders Jan 28, 2021
BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learnin...
Protein Interaction Maps Gene Regulatory Networks Genetic Association Studies Proteomics Gene Expression Profiling Humans Transcriptome Male Case-Control Studies Middle Aged Atrial Fibrillation Machine Learning Heart Atria Female Heart Valve Diseases Signal Transduction Oligonucleotide Array Sequence Analysis Databases, Genetic Bayes Theorem
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Characterization of Antiphospholipid Syndrome Atherothrombotic Risk by Unsupervised Integrated Transcriptomic Analyses.

Arteriosclerosis, thrombosis, and vascular biology Dec 24, 2020
OBJECTIVE: Our aim was to characterize distinctive clinical antiphospholipid syndrome phenotypes and identify novel microRNA (miRNA)-mRNA-intracellular signaling regulatory networks in monocytes linked to cardiovascular disease. Approach and Results:...
Gene Expression Regulation Oligonucleotide Array Sequence Analysis Antiphospholipid Syndrome MicroRNAs Unsupervised Machine Learning Middle Aged Thrombosis Risk Assessment Monocytes Gene Expression Profiling Signal Transduction Protein Interaction Maps Cluster Analysis Female Gene Regulatory Networks Risk Factors Transcriptome Humans Adult Case-Control Studies Cells, Cultured Male
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Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.

PLoS computational biology Dec 14, 2020
Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-dept...
Lipopolysaccharides Least-Squares Analysis NF-kappa B Signal Transduction Apoptosis Neural Networks, Computer Algorithms
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Neuronal differentiation strategies: insights from single-cell sequencing and machine learning.

Development (Cambridge, England) Dec 8, 2020
Neuronal replacement therapies rely on the differentiation of specific cell types from embryonic or induced pluripotent stem cells, or on the direct reprogramming of differentiated adult cells via the expression of transcription factors or signaling...
Humans Machine Learning Animals Single-Cell Analysis Neurons Induced Pluripotent Stem Cells Embryonic Stem Cells Neural Stem Cells Signal Transduction Cellular Reprogramming Cell Differentiation
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Machine-Learning and Chemicogenomics Approach Defines and Predicts Cross-Talk of Hippo and MAPK Pathways.

Cancer discovery Nov 18, 2020
Hippo pathway dysregulation occurs in multiple cancers through genetic and nongenetic alterations, resulting in translocation of YAP to the nucleus and activation of the TEAD family of transcription factors. Unlike other oncogenic pathways such as RA...
Hippo Signaling Pathway Computational Biology Humans MAP Kinase Signaling System Signal Transduction Cheminformatics Machine Learning Genomics
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Learning to encode cellular responses to systematic perturbations with deep generative models.

NPJ systems biology and applications Nov 6, 2020
Cellular signaling systems play a vital role in maintaining homeostasis when a cell is exposed to different perturbations. Components of the systems are organized as hierarchical networks, and perturbing different components often leads to transcript...
Signal Transduction Models, Biological Cells Deep Learning Systems Biology
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Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species.

PLoS computational biology Nov 5, 2020
In eukaryotes, polyadenylation (poly(A)) is an essential process during mRNA maturation. Identifying the cis-determinants of poly(A) signal (PAS) on the DNA sequence is the key to understand the mechanism of translation regulation and mRNA metabolism...
Humans Neural Networks, Computer Animals Species Specificity Poly A Deep Learning Signal Transduction Deoxyguanosine
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Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.

Frontiers in immunology Aug 25, 2020
Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents too...
Receptors, Antigen, T-Cell Protein Interaction Domains and Motifs Epitopes, T-Lymphocyte Protein Binding Humans Signal Transduction Peptides Software Deep Learning Databases, Protein T-Lymphocytes Antigens Ligands Binding Sites
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Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.

Genome biology Aug 3, 2020
BACKGROUND: Deep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the ...
Single-Cell Analysis Receptors, Antigen, T-Cell Sequence Analysis, RNA Signal Transduction Humans Deep Learning
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