Gene expression and cancer classification

Gene prediction in heterogeneous cancer tissues and establishment of Least Absolute Shrinking and Selection Operator model of lung squamous cell carcinoma.

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

A feature selection strategy for gene expression time series experiments with hidden Markov models

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.

The effect of tissue composition on gene co-expression

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

Compendiums of Cancer Transcriptome for Machine Learning Applications

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

Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data

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.

Sequential compression of gene expression across dimensionalities and methods reveals no single best method or dimensionality

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: A network approach for low-dimensional signatures from high-throughput data

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

Ensemble Feature Selection and Meta-Analysis of Cancer miRNA Biomarkers

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.

Temporal Gene Coexpression Network Analysis Using A Low-rank plus Sparse Framework

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.

Classification of early and late stage Liver Hepatocellular Carcinoma patients from their genomics and epigenomics profiles.

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

Multiple-kernel learning for genomic data mining and prediction

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

Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking

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

Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

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

Comprehensive transcriptomic analysis of cell lines as models of primary tumor samples across 22 tumor types

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.

Pan-Cancer and Single-Cell modelling of genomic alterations through gene expression

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

Nonparametric expression analysis using inferential replicate counts

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: Bartlett-Adjusted Likelihood-based LInear Model Approach for Identifying Differentially Expressed Gene with RNA-seq Data

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

Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data

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

Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine

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

Uncovering the cell type specificity of blood sample derived gene signatures using RNA expression data

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

Assessment of the gene expression profiles of leukocytes from different isolations

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

A multimodal framework for detecting direct and indirect gene-gene interactions from large expression compendium

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: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature

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

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

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

TuBA: Tunable Biclustering Algorithm Reveals Clinically Relevant Tumor Transcriptional Profiles in Breast Cancer

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: A Biostatistical tool for Transcriptomics Analysis

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

Bayesian inference of differentially expressed transcripts and their abundance from multi-condition RNA-seq data

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.

Determining sufficient sequencing depth in RNA-Seq differential expression studies

The paper introduces superSeq, a method to determine optimal sequencing depth for RNA-Seq studies, finding most studies are undersequenced and saturation depends on statistical methods.

A new normalization for the Nanostring nCounter gene expression assay

The paper introduces a new normalization method, RUV-III, for Nanostring nCounter gene expression data and evaluates its effectiveness across multiple datasets.

NormExpression: an R package to normalize gene expression data using evaluated methods

NormExpression evaluates normalization methods using AUCVC and mSCC metrics, showing consistency across bulk and scRNA-seq data, and provides an R package for method comparison.