Gene expression and cancer classification

This study predicts melanoma progression using genomic features from TCGA SKCM data, identifying key genes and miRNAs that distinguish primary from metastatic tumors.

This study uses LASSO to predict key features in LUAD subtypes based on RNA-Seq data from TCGA, showing improved survival prediction through unsupervised clustering and feature selection.

This paper develops a deep learning model integrating microarray and clinical data to predict NSCLC prognosis with high accuracy.

The paper uses LASSO modeling on RNASeq data from TCGA to identify subtype-specific genes in lung squamous cell carcinoma based on tumor heterogeneity.

This paper introduces a feature selection method using hidden Markov models for gene expression time series data, reducing feature space while maintaining biological relevance and robustness.

This study examines how tissue composition affects gene co-expression estimates and shows how deconvolution methods can improve co-expression analysis.

The paper curates 11 merged microarray datasets for cancer research, showing they align with TCGA data and support machine learning models for cancer classification.

This paper introduces DeepType, a deep learning framework for breast cancer subtype classification, which outperforms existing methods by identifying more robust subtypes with fewer genes.

The paper evaluates multiple compression methods and dimensionalities on gene expression data, finding no single best approach but showing benefits from ensemble methods across different dimensions.

DNetPRO identifies low-dimensional signatures from high-throughput data by combining discriminant analysis with network processing, outperforming existing methods with fewer variables.

The paper proposes a feature selection method to identify key miRNAs for cancer classification, finds a signature of 100 miRNAs that accurately classify 29 cancer types, and discovers 15 new potential biomarkers through literature meta-analysis.

This paper introduces TGCN, a method for analyzing temporal transcriptomic data using a low-rank plus sparse framework, improving covariance estimation and identifying robust gene modules.

The paper develops models to classify early and late stage Liver Hepatocellular Carcinoma using genomic and epigenomic data, achieving high accuracy and AUROC scores.

This paper introduces an R package for multiple kernel learning, showing improved classification by combining multiple data sources in ovarian cancer.

The paper benchmarks machine learning models for genetic data analysis using FRESA.CAD Binary Classification Benchmarking, showing quantitative and qualitative advantages over existing tools.

The paper estimates subject- and cell-type-specific gene expression from tissue using an empirical Bayes method, enabling downstream analyses like eQTL identification.

The paper evaluates cell lines as models of primary tumors across 22 types using transcriptomic data, finding cell-cycle pathways are consistently upregulated in cell lines.

This study predicts genomic alterations from gene expression in multiple cancers, showing gene aggregation improves prediction accuracy in noisy single-cell data.

The paper introduces a nonparametric method for RNA-seq differential expression analysis that accounts for inferential uncertainty, showing improved false discovery rate control compared to existing methods.

BALLI improves differential expression analysis in RNA-seq by addressing small sample bias and providing better power and precision than existing methods.

This paper introduces an anomaly detection approach to classify cancer using only healthy tissue transcriptomes, showing improved accuracy with more normal samples.

The paper evaluates single-subject methods for differential gene expression analysis and finds ensemble methods perform best in identifying DEGs without replicates.

The paper develops a web tool to quantify and visualize gene signature expression in blood cell types using RNA data.

This study compares gene expression profiles of leukocytes from different isolation methods, finding that RBC lysis reduces globin mRNA and improves transcriptome quality.

This paper introduces a multimodal framework to detect gene-gene interactions from large expression data, showing improved performance over existing methods in simulations and biological applications.

MetaGxData provides standardized cancer transcriptomic and clinical data for cross-tumour analyses, identifying common prognostic genes and creating a multi-cancer gene signature.

pcaExplorer provides an interactive R/Bioconductor tool for RNA-seq PCA analysis, enabling reproducible exploration and visualization of principal components.

TuBA identifies clinically relevant tumor transcriptional profiles in breast cancer by detecting biclusters with significant gene set agreement and clinical implications across multiple datasets.

ABioTrans is an R-based web tool for transcriptomics analysis that provides statistical methods and visualization for RNA-Seq data from GEO.

The paper develops a Bayesian method to identify differentially expressed transcripts from RNA-seq data, showing improved performance for lowly expressed transcripts in breast cancer analysis.