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Ensemble learning for classifying single-cell data and projection across reference atlases

ABSTRACT: Single-cell data are being generated at an accelerating pace.How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types.

PROVIDER: EGAS00001004283 | EGA |

REPOSITORIES: EGA

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Ensemble learning for classifying single-cell data and projection across reference atlases

Project description:Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms.

2020-03-01 | GSE141982 | GEO

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers

Project description:We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope® Diagnostic System (MMDx). Ensembles generated diagnoses that were both more accurate than the best individual classifiers, and nearly as stable as the best, consistent with expectations from the machine learning literature. Human experts had ~93% agreement (balanced accuracy) signing out the reports, and random forest-based automated sign-outs showed similar levels of agreement with the human experts (92% and 94% for predicting the expert MMDx sign-outs for T cell-mediated (TCMR) and antibody-mediated rejection (ABMR) respectively). In most cases disagreements, whether between experts or between experts and automated sign-outs, were in biopsies near diagnostic thresholds. Considerable disagreement with histology persisted. The balanced accuracies of MMDx sign-outs for histology diagnoses of TCMR and ABMR were 73% and 78% respectively. Disagreement with histology is largely due to the known noise in histology assessments (ClinicalTrials.gov NCT01299168).

2019-11-07 | GSE124203 | GEO

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

Design of an unbiased machine learning workflow to predict Multiple Sclerosis staging from blood transcriptome

Project description:Construction of gene expression-based classifiers to predict the different Multiple Sclerosis stages from peripheral blood mononuclear cells (PBMC) transcriptome of MS patients and controls.

2020-09-01 | GSE136411 | GEO

nasopharyngeal carcinoma versus nasopharyngeal phlogistic tissue reference

Project description:Nasopharyngeal carcinoma (NPC) remains a majoy health problem worldwide, specially in Southeast China. In order to find the new candidate genes and molecular markers that are associated with nasopharyngeal carcinoma (NPC), this study focused on the screening NPC relative genes by gene expression profile. Keywords: disease state analysis

2009-04-21 | GSE11219 | GEO

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

Patterson2022 - Tumour mutation data driven Random Forest model to predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Project description:A Random Forest model is developed to incorporate tumor mutation data within the context of the biological process known as leukocyte proliferation regulation. This model aims to predict a patient's response to anti-PD1 treatment. The authors conducted experiments using four different types of classifiers: Random Forest, Gradient Boosting, Feed Forward Neural Network, and Long Short-Term Memory (LSTM) recurrent neural network. Among these classifiers, the Random Forest algorithm yielded the best predictive performance when modeling gene mutation data associated with the 'leukocyte proliferation regulation' biological process. Hence, this curated version of the model focuses on the Random Forest model trained specifically on the 'Leukocyte Proliferation Regulation' process. In this model, a value of '0' is assigned to NonResponders, while a value of '1' is assigned to Responders. Please note that to obtain predictions, users should provide mutation data containing only the genes corresponding to the 'GO_REGULATION_OF_LEUKOCYTE_PROLIFERATION' process keyword, as specified in the 'GO_test_genes_dict_intersection' dictionary.

2023-07-03 | BIOMD0000001073 | BioModels

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

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

| PRJNA553570 | ENA

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

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

| PRJNA553574 | ENA

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy

Project description:Top-down mass spectrometry (MS) is a powerful tool for identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. While the technique is powerful, it suffered from the complex dataset generated from top-down MS experiments, which requires sequential data processing steps for data interpretation. Deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes is a critical step in the data processing process. Multiple algorithms are currently available to deconvolute top-down mass spectra; however, each algorithm generates different deconvoluted peak lists with varied accuracy comparing to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. The random forest model outperformed the single best algorithm. This machine learning strategy could enhance the accuracy and confidence in protein identification during database search by accelerating detection of true positive peaks while filtering out false positive peaks. Thus, this method showed promises in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.

2020-05-06 | PXD018043 | Pride

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