Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Diego2021 - Improved Prediction of ICB Efficacy Across Multiple Cancer Types using Random Forests

ABSTRACT: In this study, the authors had developed a machine learning model to predict immune checkpoint blockade (ICB) response by integrating genomic, molecular, demographic and clinical data from a curated cohort (MSK-IMPACT) with 1479 patients treated with ICB across 16 different types of cancer. This model significantly outperformed the predictions based on Tumor Mutational Burden (TCB). This model uses two types of random forests, one uses 16 features and the other uses 11 features. These features are selected based on their permutation importance score. The model was deployed on docker to reproduce the results and the data was shared to promote FAIReR sharing of machine learning models.

SUBMITTER: Ganishk D

PROVIDER: MODEL2407210002 | BioModels | 2024-07-23

REPOSITORIES: BioModels

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	MODEL2407210002?filename=41587_2021_1070_MOESM3_ESM.xlsx	Xlsx
	MODEL2407210002?filename=README.md	Other
	MODEL2407210002?filename=docker_test.zip	Other
	MODEL2407210002?filename=docker_train.zip	Other
	MODEL2407210002?filename=figures.zip	Other

Items per page:

1 - 5 of 9

Similar Datasets

Chowell2022 - Random Forest model to predict efficacy of immune checkpoint blockade across multiple cancer patient cohorts

Project description:This is a Random Forest algorithm-based machine learning model called RF16, which incorporates a total of 16 genomic, molecular, demographic, and clinical features to predict the immunotherapy response for a patient. The model assigns a value of 0 for NonResponder and 1 for Responder. Please be aware that the column names in the GitHub code and the downloaded dataset from the publication may vary. Users are advised to make minor adjustments to either the code or the dataset to ensure compatibility. The curated version of the model has modified the column names in the training code to align with the dataset. GitHub repository: https://github.com/CCF-ChanLab/MSK-IMPACT-IO

2023-05-09 | BIOMD0000001066 | BioModels

Comprehensive RNA-Seq Dataset for Pan-Cancer Tumor Classification

Project description:The following consists of an RNA-Seq cohort comprised of samples from patients diagnosed with various tumor types across the pan-cancer spectrum. Collected at diagnosis, these samples serve as a resource for tumor classification using machine learning classifiers. Process count tables are provided. This cohort can assist researchers in exploring molecular signatures and facilitate accurate classification across diverse malignancies.

2024-06-30 | E-MTAB-14038 | biostudies-arrayexpress

A hybrid signal/sequence model to detect RNA polymerase activity in ATAC-seq

Project description:We explore different uses of machine learning classifiers, including neural networks, to combine the “signal” from ATAC-seq with its underlying genome sequence in order to classify ATAC-seq peaks on the presence or absence of transcription. We show how a hybrid signal/sequence representation, classified using recurrent neural networks (RNNs), yields the best performance across different cell types.

2020-04-30 | GSE130205 | GEO

Small RNA-seq of undiseased human brain

Project description:The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to classify six different types of RNA transcripts with 79~80% accuracy in cross-validation experiments, and with 71~73% accuracy when CoRAL uses one tissue type for training and the other as validation. Analysis by CoRAL revealed that long intergenic ncRNAs, small cytoplasmic RNAs, and small nuclear RNAs show more tissue specificity, while microRNAs, small nucleolar, and transposon-derived RNAs are highly discernible and consistent across the two tissue types. The ability to consistently annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using smRNA-seq data in less characterized organisms. Four samples were sequenced, each one coming from frozen brain tissue (frontal cortex) of a deceased female human patient with no remarkable pathology.

2013-07-07 | E-GEOD-43335 | biostudies-arrayexpress

DNA-Methylome based Tumor Hypoxia Classifier Identifies HPV-negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence After Primary Radiochemotherapy

Project description:We used machine-learning algorithms to identify a hypoxia-associated methylation signature in patients with HPV negative HNSCC in the TCGA-HNSCC cohort. This current submission forms the basis of the independent validation cohort used to test the Hypoxia-M classifier in our study.

2023-04-05 | E-MTAB-12431 | biostudies-arrayexpress

Machine Learning Analysis of Circulating Alternative Spliced RNAs Reveal Segregation of Retained Intron Species with New-onset Type 1 Diabetes

Project description:Distinct patterns of RNA splicing differentiated participants with T1D from healthy unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects.

2024-09-18 | GSE277133 | 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

Nano Random Forests to mine proteomics data for protein complexes and their relationships in quantitative proteomics data

Project description:This project has integrated SILAC proteomics data of wild type and knockout mitotic chromosomes, and used machine learning technique random forests to identify chromosomal complexes with high precision, and small training sets.

2018-10-26 | PXD003588 | Pride

Small RNA-seq of undiseased human brain

2013-07-07 | GSE43335 | GEO

Machine-learning model based on glycosyltransferase expression predicts multiple cancer types, subtypes and survival probability

Project description:Machine-learning model based on glycosyltransferase expression predicts multiple cancer types, subtypes and survival probability

| PRJNA1070610 | ENA