AIMC Topic: Gene Expression Profiling

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Cell type prioritization in single-cell data.

Nature biotechnology Jul 20, 2020
We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensio...
Computational Biology Sequence Analysis, RNA Rats Walking Machine Learning Gene Expression Profiling Databases, Genetic Nerve Net Transcriptome Animals Chromatin Single-Cell Analysis Mice
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LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.

BMC bioinformatics Jul 20, 2020
BACKGROUND: Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the d...
Genes, Regulator Computational Biology Models, Genetic Escherichia coli Fuzzy Logic Computer Simulation Gene Expression Profiling Algorithms Gene Regulatory Networks
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Classification of Microarray Gene Expression Data Using an Infiltration Tactics Optimization (ITO) Algorithm.

Genes Jul 18, 2020
A number of different feature selection and classification techniques have been proposed in literature including parameter-free and parameter-based algorithms. The former are quick but may result in local maxima while the latter use dataset-specific ...
Oligonucleotide Array Sequence Analysis Humans Machine Learning Gene Expression Profiling
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Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example.

Pharmacological research Jul 17, 2020
'Polypharmacology' is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the 'multi-components, multi-targets and multi-pathways' principle...
Proteomics Proteome Signal Transduction Medicine, Chinese Traditional Gene Regulatory Networks Systems Biology Duodenum Molecular Structure Drugs, Chinese Herbal Polypharmacology Dyspepsia Rats, Sprague-Dawley Cluster Analysis Unsupervised Machine Learning Structure-Activity Relationship Humans Animals Male Gene Expression Profiling Transcriptome Protein Interaction Maps
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Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BMC molecular and cell biology Jul 8, 2020
BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design ...
Gene Expression Regulation, Neoplastic Adenocarcinoma Databases, Protein Drug Repositioning Molecular Docking Simulation Humans Gene Expression Profiling Machine Learning Cell Line, Tumor Fatty Acid-Binding Proteins Algorithms Neoplasm Staging Gastrointestinal Hormones Prognosis Colonic Neoplasms Epistasis, Genetic Survival Analysis Aminopyridines Benzimidazoles
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Machine learning for precision dermatology: Advances, opportunities, and outlook.

Journal of the American Academy of Dermatology Jul 6, 2020
Skin Gene Expression Profiling Skin Diseases Humans Dermatology Big Data Machine Learning Photography Image Processing, Computer-Assisted Precision Medicine Genomics
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Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis.

Neuron Jun 29, 2020
A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neu...
Mice Neurons Aging Alzheimer Disease Humans Gene Expression Profiling Transcriptome Machine Learning Gene Regulatory Networks Animals
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Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.

Journal of integrative bioinformatics Jun 29, 2020
To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key i...
Molecular Sequence Annotation Gene Ontology Machine Learning Sequence Alignment Gene Expression Profiling Subcellular Fractions Sequence Analysis, Protein Bacterial Proteins Computational Biology Gram-Negative Bacteria Databases, Protein Gram-Positive Bacteria
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Integrating spatial gene expression and breast tumour morphology via deep learning.

Nature biomedical engineering Jun 22, 2020
Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the develop...
Transcriptome Reproducibility of Results Deep Learning Breast Neoplasms Image Processing, Computer-Assisted Biomarkers, Tumor Humans Gene Expression Profiling Algorithms Female
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Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

Scientific reports Jun 12, 2020
During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alt...
Models, Theoretical Drug-Related Side Effects and Adverse Reactions Quality Control Uncertainty Humans Machine Learning Animals Gene Expression Profiling
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