Gene expression and cancer classification

A statistical nonparametric method for identifying consistently important features across samples

This paper introduces a statistical nonparametric method to identify consistently important features across samples and demonstrates its application to RNA sequencing data.

iSeqQC: A Tool for Expression-Based Quality Control in RNA Sequencing

iSeqQC is an expression-based quality control tool that detects outliers caused by batch effects or phenotypic dissimilarity using statistical methods.

M3C: A Monte Carlo reference-based consensus clustering algorithm

M3C improves consensus clustering by simulating null distributions to reduce bias and false positives in determining the number of clusters.

Systematic Normalization with Multiple Housekeeping Genes for the Discovery of Genetic Dependencies in Cancer

The paper normalizes gene expression data using multiple housekeeping genes to identify consistent genetic correlations in breast cancer, finding that multiple gene normalization improves correlation consistency compared to single gene normalization.

A graph-based algorithm for RNA-seq data normalization

This paper introduces a graph-based normalization algorithm for RNA-seq data that improves reference transcript recovery and normalization quality without relying on prior assumptions or reference transcripts.

All of gene expression (AOE): an integrated index for public gene expression databases

AOE integrates gene expression data from GEO, AE, and GEA, providing a unified search tool accessible via the GEA website.

A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

The paper develops a Bayesian method to improve gene expression signal detection in LINCS L1000 data, showing better reliability and informativeness than existing approaches.

Robust Classification of Immune Subtypes in Cancer

The paper introduces an ensemble classifier using XGBoost to robustly classify tumor samples into six immune subtypes based on gene-level features.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma using Large-scale Transcriptomics Data

This study identifies a three-gene biomarker panel for hepatocellular carcinoma using large-scale transcriptomics data, showing high accuracy and prognostic potential across multiple platforms.

Constructing early diagnosis model of colorectal cancer based on expression profile

The paper constructs a colorectal cancer diagnosis model using expression profiles and a BPNN algorithm, achieving high accuracy and AUC values.

Naught all zeros in sequence count data are the same

The paper evaluates zero-handling models in genomic and microbiome data, showing significant disagreement in identifying differentially expressed sequences and finding simple count models sufficient across processes.

Granular Transcriptomic Signatures Derived from Independent Component Analysis of Bulk Nervous Tissue for Studying Labile Brain Physiologies

The paper uses independent component analysis on bulk tissue microarrays to derive gene expression signatures that reveal biologically relevant transcriptional modules in Drosophila brain tissues.

Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach

This paper introduces a deep learning-based clustering method for gene expression data that integrates biological information, outperforming existing methods on yeast and human datasets.

Differential Expression Gene Explorer (DrEdGE): A tool for generating interactive online data visualizations for exploration of quantitative transcript abundance datasets

DrEdGE is a web-based tool that allows genomicists to create interactive visualizations of RNA-seq datasets, enabling non-experts to explore and query data for gene and pattern discovery.

qtQDA: quantile transformed quadratic discriminant analysis for high-dimensional RNA-seq data

The paper introduces qtQDA, a classification method for RNA-seq data that uses quantile transformation and regularized QDA, showing good performance on real datasets.

consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction

consensusDE identifies consistent differential expression across RNA-seq algorithms while integrating RUV correction to improve stability.

AdaTiSS: A Novel Data-Adaptive Robust Method forQuantifying Tissue Specificity Scores

AdaTiSS provides a data-adaptive method to quantify tissue specificity scores for genes, improving comparability across tissues and genes.

Cluster analysis on high dimensional RNA-seq data with applications to cancer research- An evaluation study

This study evaluates how cluster analysis choices affect cancer subtype identification using four human cancer datasets, finding that sample size has limited impact while heterogeneity and subtype distribution significantly influence performance.

Enhanced Cancer Subtyping via Pan-Transcriptomics Data Fusion, Monte-Carlo Consensus Clustering, and Auto Classifier Creation

This paper improves cancer subtyping by fusing pan-transcriptomics data, using Monte-Carlo consensus clustering, and creating deep classifiers, identifying six novel pancreatic ductal adenocarcinoma subtypes with better reproducibility.

PROMO: An interactive tool for analyzing clinically-labeled multi-omic cancer datasets

PROMO is a tool for analyzing multi-omic cancer datasets with clinical labels, enabling subtype discovery and biomarker identification without coding.

DeepMF: Deciphering the Latent Patterns in Omics Profiles with a Deep Learning Method

DeepMF improves subtype discovery and data recovery in cancer omics data by handling noise and missing values with a deep learning-based matrix factorization model.

Revisiting Feature Selection with Data Complexity for Biomedicine

This study evaluates 11 feature selection methods across 27 biomedical datasets to determine their performance based on data complexity and provides recommendations for method selection.

Estimating mutual information under measurement error

This paper introduces a corrected estimator for mutual information that accounts for measurement error, showing improved accuracy in simulated and real gene expression data.

Multiplatform Biomarker Identification using a Data-driven Approach Enables Single-sample Classification

The paper introduces a data-driven approach to identify reliable biomarkers across platforms for single-sample classification, showing improved performance in tumor type and molecular status classification.

Effects of technical noise on bulk RNA-seq differential gene expression inference

The paper investigates how technical noise affects bulk RNA-seq differential gene expression analysis and proposes noise-adjustment methods to improve consistency across tools.

EBADIMEX: An empirical Bayes approach to detect joint differential expression and methylation and to classify samples

EBADIMEX detects joint differential expression and methylation and classifies cancer samples using empirical Bayes methods and moderated tests.

PCAGO: An interactive tool to analyze RNA-Seq data with principal component analysis

PCAGO is a web tool that uses PCA to analyze RNA-Seq data, offering normalization, filtering, visualization, and parameter selection to improve clustering and interpretation.

Prediction and analysis of skin cancer progression using genomics profiles of patients

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

Prediction of best features in heterogeneous Lung adenocarcinoma samples using Least Absolute Shrinking and Selection Operator.

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.

Predicting the prognosis of non-small cell lung cancer by integrating microarray and clinical data with deep learning

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