Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prediction of tissue-of-origin of early-stage cancers using serum miRNomes

ABSTRACT: Large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system.

ORGANISM(S): Homo sapiens

PROVIDER: GSE211692 | GEO | 2022/11/12

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Prediction of tissue-of-origin of early-stage cancers using serum miRNomes

Project description:Prediction of tissue-of-origin of early-stage cancers using serum miRNomes

| PRJNA871766 | ENA

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

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

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

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

2019-07-18 | GSE134052 | GEO

Zeng2022 - Prediction of HIV Growth Inhibition using ImageMol

Project description:ImageMol is a Representation Learning Framework that utilizes molecule images for encoding molecular inputs as machine readable vectors for downstream tasks such as bio-activity prediction, drug metabolism analysis, or drug toxicity prediction. The approach utilizes transfer learning, that is, pre-training the model on massive unlabeled datasets to help it in generalizing feature extraction and then fine tuning on specific tasks. This model is fine tuned on 13 assays concerned with a number of target categories ranging from viral entry to toxicity in humans. These interactions are formulated as binary classification tasks. Model Type: Predictive machine learning model. Model Relevance: Probability of HIV inhibition. Model Encoded by: Dhanshree Arora (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos6hy3

2024-05-13 | MODEL2405130001 | BioModels

Urinary Proteomic Spectra Analysis Based on Machine Learning for Classification of Kidney Diseases

Project description:To extract urinary proteome spectral features based on advanced mass spectrometer and machine learning algorithms, it could get more accurate reporting results for disease classification. We tried to establish a novel diagnosis model of kidney diseases by combining machine learning XGBoost algorithm with complete urinary proteomic information.

2022-09-13 | PXD018996 | Pride

Machine learning-based prediction of the activity and specificity of Cas9 variants in gene editing

Project description:Machine learning-based prediction of the activity and specificity of Cas9 variants in gene editing

2024-08-22 | GSE231840 | GEO

Zeng2022 - Prediction of molecular properties and drug targets using a self-supervised learning framework

Project description:ImageMol is a Representation Learning Framework that utilizes molecule images for encoding molecular inputs as machine readable vectors for downstream tasks such as bio-activity prediction, drug metabolism analysis, or drug toxicity prediction. The approach utilizes transfer learning, that is, pre-training the model on massive unlabeled datasets to help it in generalizing feature extraction and then fine tuning on specific tasks. This model is fine tuned on 13 assays concerned with a number of target categories ranging from viral entry to toxicity in humans. These interactions are formulated as binary classification tasks. Model Type: Predictive machine learning model. Model Relevance: SARS-CoV-2 Anti viral screening. Model Encoded by: Dhanshree Arora (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos4cxk

2024-05-08 | MODEL2405080005 | BioModels

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

2013-01-01 | GSE29210 | GEO