Gene expression and cancer classification

Improving the value of public RNA-seq expression data by phenotype prediction

The paper predicts missing phenotype annotations for public RNA-seq data using well-annotated datasets, enabling better analysis of large-scale genomic data.

Clustering gene expression time series data using an infinite Gaussian process mixture model

This paper introduces a nonparametric model, DPGP, to cluster gene expression time series data and demonstrates its effectiveness using simulated and real datasets.

Data-driven characterization of molecular phenotypes across heterogenous sample collections

The paper develops a method for stratifying molecular profiles across datasets and applies it to hematological malignancies, revealing differential DNA methylation in a leukemia cluster.

DAtest: a framework for choosing differential abundance or expression method

DAtest is an R package that empirically compares statistical methods for differential expression or abundance directly on a user's own dataset across diverse omics types.

Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components

The paper improves PCA for biological data by automating a graphical Bayesian method to identify significant principal components with biological interpretations, applied to proteomics data from acute myeloid leukemia patients.

A risk stratification model for lung cancer based on gene coexpression network

The paper develops a deep learning-based risk stratification model for lung adenocarcinoma using gene coexpression networks and validates its prognostic value across multiple datasets.

A hypothesis-driven approach to assessing significance of differences in RNA expression levels among specific groups of genes

The paper introduces a method to assess the significance of differences in gene expression among predefined gene groups, applying it to evaluate heat stress responses in maize.

Accurate And Fast Feature Selection Workflow For High-Dimensional Omics Data

The paper develops an efficient feature selection workflow and R package for high-dimensional omics data, evaluated on proteomics and gene expression datasets.

iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks

iterativeWGCNA improves module detection in WGCNA by iteratively refining gene clusters and minimizing information loss.

MultiDCoX: Multi-factor Analysis of Differential Co-expression

MultiDCoX identifies differentially co-expressed gene sets influenced by multiple factors in transcriptomic data, demonstrating effectiveness through simulated data and breast cancer analysis.

Framework For Reanalysis Of Publicly Available Affymetrix(R) Genechip(R) Data Sets Based On Functional Regions Of Interest

The paper presents a framework for reanalyzing Affymetrix GeneChip data using functional regions of interest, improving probe mapping and detection of gene expression changes.

Wx: a neural network-based feature selection algorithm for next-generation sequencing data

Wx is a neural network-based feature selection algorithm that identifies optimal gene sets for distinguishing cancer from normal samples in NGS data, outperforming existing biomarker candidates.

Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders

This paper trains a variational autoencoder on TCGA RNA-seq data to model cancer gene expression and identify biologically relevant latent features.

sigQC: A procedural approach for standardising the evaluation of gene signatures

This paper introduces sigQC, an R package that standardizes gene signature quality control, demonstrating its application in evaluating signatures on cancer gene expression data.

Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data

This paper discusses machine learning approaches for RNA-Seq data classification and their implementation to improve disease diagnosis and biomarker research.

Mining the forest: uncovering biological mechanisms by interpreting Random Forests

The paper introduces Foresight, a method to interpret Random Forest models in biological datasets by uncovering feature groups and interactions, demonstrating its application on breast cancer gene expression data.

A novel feature selection for RNA-seq analysis

This paper introduces a novel feature selection method, NSVA, to improve RNA-seq differential expression analysis by reducing false positives and enhancing robustness.

A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

The paper introduces the L1000 platform, a high-throughput gene expression profiling method, and reports 1.3 million perturbational profiles expanding the Connectivity Map for drug mechanism discovery and functional annotation.

Composite measurements and molecular compressed sensing for highly efficient transcriptomics

This paper introduces composite measurements and molecular compressed sensing to efficiently recover gene expression profiles with fewer measurements than genes.

optSelect: using agent-based modeling and binary PSO techniques for ensemble feature selection and stability assessment

optSelect improves classification accuracy by using a two-stage ensemble feature selection framework with agent-based modeling and binary PSO techniques.

Clusternomics: Integrative Context-Dependent Clustering For Heterogeneous Datasets

Clusternomics identifies clusters across heterogeneous datasets by modeling both local and global structures, showing improved performance on simulated and real-world cancer datasets.

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

The paper develops a preprocessing and normalization pipeline for large-scale RNA-Seq data, addressing batch and tissue effects in heterogeneous datasets.

Tumor Origin Detection with Tissue-Specific miRNA and DNA methylation Markers

The paper develops classifiers using miRNA and DNA methylation data to predict tumor origin with high accuracy and provides a webserver for clinical use.

clusterSeq: methods for identifying co-expression in high-throughput sequencing data

The paper introduces clusterSeq, a method for identifying co-expression clusters in high-throughput sequencing data by incorporating structural elements between experimental conditions.

Determining the optimal number of independent components for reproducible transcriptomic data analysis

This paper introduces a method to determine the optimal number of independent components for reproducible transcriptomic data analysis using ICA stability rankings.

CancerDiscover: A configurable pipeline for cancer prediction and biomarker identification using machine learning framework

CancerDiscover is a pipeline that automates data integration, preprocessing, and classification model generation for cancer detection and biomarker identification using machine learning.

WDR88, CCDC11, and ARPP21 genes indulge profoundly in the desmoplastic retort to prostate and breast cancer metastasis.

The paper identifies genes involved in cancer metastasis using microarray data and proposes a threshold for gene expression.

Uncovering Robust Patterns of MicroRNA Co-Expression across Cancers using Bayesian Relevance Networks

This paper introduces Bayesian Relevance Networks to improve co-expression analysis of miRNA data by accounting for uncertainty in high-throughput sequencing, showing better reproducibility and lower false discovery rates.

A Next Generation Clustering Tool Enables Identification of Functional Cancer Subtypes with Associated Biological Phenotypes

The paper introduces a tool called phenMap that integrates clustering, phenotype association, and biomarker identification to define functional cancer subtypes with biological phenotypes.

A general and powerful stage-wise testing procedure for differential expression and differential transcript usage

The paper introduces a stage-wise testing procedure for differential expression and transcript usage that provides gene-level FDR control while maintaining transcript-level resolution.