Article

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning

2021-04-11

Abstract excerpt

Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very f...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8bed7cbb-1993-5bba-88cd-2069e8a70430
DOI: 10.1101/2021.04.10.439266

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learningDOI 10.1101/2021.04.10.439266Read

CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancerSemantic proximity Read

CaSee: A lightning transfer-learning model directly used to discriminate cancer/normal cells from scRNA-seqSemantic proximity Read

Cell-type classification of cancer single-cell RNA-seq data using the Subsemble ensemble-based machine learning classifierSemantic proximity Read

Machine learning for pan-cancer classification based on RNA sequencing dataSemantic proximity Read

DeSide: A unified deep learning approach for cellular decomposition of bulk tumors based on limited scRNA-seq dataSemantic proximity Read

A Deep Learning method for classification of HNSCC and HPV patients using single-cell transcriptomicsSemantic proximity Read

Identifying common transcriptome signatures of cancer by interpreting deep learning modelsSemantic proximity Read

LSTM neural network for pan-cancer classification & marker gene discovery by symmetrical occlusion methodSemantic proximity Read

Multiblock LASSO Framework for Cancer Gene Selection from RNA-Seq PANCAN DataSemantic proximity Read

CancerSubminer: an integrated framework for cancer subtyping using supervised and unsupervised learning on DNA methylation profilesSemantic proximity Read

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.Semantic proximity Read

CanSig: a tool for benchmarking malignant state discovery in single-cell RNA-Seq dataSemantic proximity Read