AIMC Topic: Transcriptome
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Unsupervised and supervised learning with neural network for human transcriptome analysis and cancer diagnosis.
Scientific reports
Nov 5, 2020
Deep learning analysis of images and text unfolds new horizons in medicine. However, analysis of transcriptomic data, the cause of biological and pathological changes, is hampered by structural complexity distinctive from images and text. Here we con...
Prediction of cancer dependencies from expression data using deep learning.
Molecular omics
Nov 2, 2020
Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply...
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.
Nature communications
Oct 30, 2020
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...
Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting.
Clinical cancer research : an official journal of the American Association for Cancer Research
Oct 27, 2020
PURPOSE: Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cance...
Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.
Chemical biology & drug design
Oct 16, 2020
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from s...
A machine learning analysis of a "normal-like" IDH-WT diffuse glioma transcriptomic subgroup associated with prolonged survival reveals novel immune and neurotransmitter-related actionable targets.
BMC medicine
Oct 16, 2020
BACKGROUND: Classification of primary central nervous system tumors according to the World Health Organization guidelines follows the integration of histologic interpretation with molecular information and aims at providing the most precise prognosis...
Spage2vec: Unsupervised representation of localized spatial gene expression signatures.
The FEBS journal
Oct 11, 2020
Investigations of spatial cellular composition of tissue architectures revealed by multiplexed in situ RNA detection often rely on inaccurate cell segmentation or prior biological knowledge from complementary single-cell sequencing experiments. Here,...
Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data.
Methods (San Diego, Calif.)
Oct 9, 2020
Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as ...
Artificial Intelligence Applied to Gene Expression Testing (IVIGET) to Predict Trivalent Inactivated Influenza Vaccine Immunogenicity in HIV Infected Children.
Frontiers in immunology
Oct 5, 2020
The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially ...
Vaccines, Inactivated
Hemagglutination Inhibition Tests
Child
Computational Biology
Vaccination
Coinfection
Influenza, Human
Reproducibility of Results
Influenza Vaccines
Gene Expression Profiling
Influenza A Virus, H1N1 Subtype
Humans
Transcriptome
Immunogenicity, Vaccine
Artificial Intelligence
HIV Infections
Prognosis
An embedded gene selection method using knockoffs optimizing neural network.
BMC bioinformatics
Sep 22, 2020
BACKGROUND: Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural...
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