AIMC Topic: Transcriptome
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Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.
EBioMedicine
Nov 10, 2020
BACKGROUND: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subcla...
Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.
BMC bioinformatics
Nov 7, 2020
BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the cur...
Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients.
Aging
Nov 5, 2020
Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and ran...
Prognosis
Clinical Decision-Making
Female
Reproducibility of Results
Risk Factors
Humans
Machine Learning
Decision Support Techniques
Transcriptome
Adolescent
Child, Preschool
Child
Predictive Value of Tests
Cerebellar Neoplasms
Middle Aged
Medulloblastoma
Risk Assessment
Young Adult
Infant
Nomograms
Adult
Gene Expression Profiling
Databases, Genetic
Biomarkers, Tumor
Male
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,...
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