Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency

ABSTRACT: A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency

PROVIDER: PRJNA675152 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR13001200.fastq.gz	Fastqsanger.gz
	SRR13001201.fastq.gz	Fastqsanger.gz
	SRR13001202.fastq.gz	Fastqsanger.gz
	SRR13001203.fastq.gz	Fastqsanger.gz
	SRR13001204.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 58

Similar Datasets

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (ATAC-seq)

Project description:One challenge of cancer precision medicine is the heterogeneity of genetic and non-genetic alterations that result in dysfunctional molecular pathways. As an emerging drug discovery effort, dysregulation in Hippo pathway signaling is known to drive oncogenesis across numerous cancer types but lacks recurrent mutation(s) that are often found in other canonical signaling pathways. Here, we use first principles approach to develop a machine-learning framework to identify a robust, lineage-independent gene expression signature to quantify Hippo pathway dependency in cancers. Through integrating data from multi-omics platforms, this data-driven approach has enabled identifying a proposed combination with MAPK inhibition for direct targeting of Hippo pathway dependent cancers for which we then elucidate the underlying molecular mechanism. The results underscore how a multifaceted approach, computational models combined with laboratory efforts, can accelerate precision medicine efforts toward co-targeting Hippo and MAPK pathways, an approach that can be generalized to other key cancer signaling pathways to define therapeutic strategies.

2021-01-26 | GSE161018 | GEO

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (RNA-seq)

2021-01-26 | GSE161010 | GEO

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (ATAC-seq)

Project description:A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (ATAC-seq)

| PRJNA675182 | ENA

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (RNA-seq)

Project description:A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (RNA-seq)

| PRJNA675107 | ENA

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-01-26 | GSE161019 | GEO

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Prefrontal cortical synaptoproteome profile combined with machine learning predict resilience towards chronic social isolation in rats

Project description:Chronic social isolation (CSIS), an animal model of depression, generates two stress-response phenotypes: CSIS-resilient and CSIS-susceptible. However, the molecular mechanism underlying resilience to CSIS remains unclear. We aimed to investigate the prefrontal cortical synaptoproteome profile between CSIS-resilient, CSIS-susceptible and control rats in order to identify predictive proteins for the resilience phenotype. Comparison of CSIS-resilient versus CSIS-susceptible rats revealed downregulated glycolysis intermediate Aldoc and upregulated Cltc, Cam2a, Syn and Fasn proteins involved in neuronal transmission, synaptic vesicle trafficking and fatty acid synthesis. Comparison of CSIS-resilient and control rats revealed downregulated mitochondrial proteins Atp5f1b and Cs, and upregulated Prkcg, Slc17a7, and Sv2a proteins involved in signal transduction and synaptic trafficking. These proteins make the animal groups linearly separable, and 100% validation accuracy is achieved by standard machine learning models. In addition, we identified the most significant proteins for discriminating CSIS-resilient vs. CSIS-susceptible or control rats by using Support Vector Machine with greedy forward search and Random Forest, resulting in four panels of discriminative proteins. Proteomics-data-driven machine learning algorithms can provide a platform for accessing predictive features and provide additional insight into the molecular mechanisms of synaptic neurotransmission involved in stress resilience.

2024-04-23 | PXD047159 | Pride

Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation

Project description:In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic workup for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models (random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), support vector machine (SVM)) that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF=83%, NN=88%, LOGREG=SVM=89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic workup of HNSC-CUP.

2021-12-08 | GSE171994 | GEO

Machine Learning-based Peptide ID Rescoring

Project description:Machine Learning-based Mascot Rescoring to Reduce False Positives in Tumor Immunopeptidomics using Mass Spectrometry

2024-08-25 | PXD044852 | JPOST Repository

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Transcriptomics]

Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.

2019-07-18 | GSE134056 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data