Gene expression and cancer classification

PDXGEM: Patient-Derived Tumor Xenograft based Gene Expression Model for Predicting Clinical Response to Anticancer Therapy in Cancer Patients

PDXGEM builds a gene expression model to predict cancer patient responses to anticancer therapies using data from patient-derived xenograft models.

Imputing missing RNA-seq data from DNA methylation by using transfer learning based-deep neural network

The paper introduces TDimpute, a transfer learning-based deep neural network for imputing missing RNA-seq data using DNA methylation, showing improved accuracy across 16 cancer datasets.

CODC: A copula based model to identify differential coexpression.

The paper introduces CODC, a copula-based model to identify differential coexpression between gene pairs across two conditions, showing improved performance on TCGA RNA-Seq data.

EBSeq: improving mixing computations for multi-group differential expression analysis

EBSeq improves computational efficiency for multi-group differential expression analysis by updating algorithms and pruning techniques.

New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data

The paper introduces a two-stage framework using mixed mutual information and scalable Bayesian networks to analyze multimodal cancer data, identifying 11 candidate genes with 6 validated in literature.

RNA-seq analyses: Benchmarking differential expression analyses tools reveals the effect of higher number of replicates on performance.

This paper benchmarks differential expression analysis tools for RNA-seq, showing how replicate numbers affect performance and recommending specific tools for different replicate ranges.

Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data

The paper introduces a density-based clustering method using percolation theory to identify functionally related gene modules from noisy, high-dimensional RNA sequencing data across different organisms.

Collapsing the list of myocardial infarction-related differential expressed genes into a diagnostic signature

The paper identifies a diagnostic signature for myocardial infarction using differential expressed genes and validates it on independent datasets.

ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets

ClusterMine integrates biological knowledge to cluster samples based on gene set expression profiles, improving interpretability of clusters.

Differential Transcript Usage Analysis Incorporating Quantification Uncertainty Via Compositional Measurement Error Regression Modeling

The paper introduces CompDTU, a method for differential transcript usage analysis that addresses scalability and quantification uncertainty, improving sensitivity and reducing false positives.

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles

CCLA authenticates cancer cell lines using gene expression profiles with high accuracy and speed, offering a web server for public use.

Sparse input neural networks to differentiate 32 primary cancer types based on somatic point mutations

This paper introduces a sparse-input neural network (spinn) to classify 32 cancer types using somatic point mutations, achieving 73% accuracy on independent test data.

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

This paper introduces a robust hierarchical co-clustering algorithm to identify toxicogenomic biomarkers and their regulatory doses of chemical compounds, showing improved performance over traditional methods in the presence of outliers.

Gaining Accuracy for Gene Expression Data by Parsimonious Models

The paper introduces parsimonious models to improve gene expression data accuracy by selectively recovering signal and discarding noise, achieving significant accuracy gains with minimal computational cost.

A novel method for classification of tabular data using convolutional neural networks

The paper introduces TAC, a method using CNNs to classify tabular data by converting it to images, and shows it performs as well as existing non-CNN classifiers on gene expression data.

Negative Binomial Additive Model for RNA-Seq Data Analysis

The paper introduces NBAMSeq, a statistical model for RNA-Seq data that improves detection of nonlinear effects while maintaining linear effect performance.

Robust gene expression-based classification of cancers without normalization

This paper introduces a self-training method for gene expression-based cancer classification that works across different data types and normalization methods.

K-means Based Unsupervised Feature Selection to Prioritize Biomarkers of Different Disease Clinical Phases

This study uses a k-means based unsupervised feature selection method to identify biomarkers associated with different clinical phases of Huntington's disease.

The covariance shift (C-SHIFT) algorithm for normalizing biological data

The paper introduces the C-SHIFT normalization method to recover the covariance matrix from gene expression data by minimizing noise and improving accuracy over existing techniques.

Blind estimation and correction of microarray batch effect

This paper introduces BESC, a method to correct microarray batch effects without requiring known batch identities or removing true biological differences, using signatures from a reference set.

Identification of the associated expression patterns as potential predictive markers for cancer prognosis

The paper identifies gene expression correlation patterns (AEPs) associated with cancer prognosis across 22 cancer types using transcriptomic data from 5,001 patients.

Pitfalls in re-analysis of observational omics studies: a post-mortem of the human pathology atlas.

The paper identifies issues in re-analyzing observational omics studies, highlighting problems with false positives, method limitations, and lack of confounders in public metadata.

CAncer bioMarker Prediction Pipeline (CAMPP) - A standardised and user-friendly framework for the analysis of quantitative biological data.

CAMPP is an open-source R pipeline that simplifies high-throughput data analysis for non-experts, providing tools for normalization, clustering, differential expression, and biomarker prediction.

Filtering Variables for Supervised Sparse Network Analysis

The paper introduces a gene filtering method to improve supervised network analysis by removing irrelevant features, enhancing scalability and accuracy of downstream analyses like sparse canonical correlation analysis.

Exploring High-Dimensional Biological Data with Sparse Contrastive Principal Component Analysis

This paper introduces sparse contrastive principal component analysis to extract stable and relevant biological signals from high-dimensional data and evaluates it on multiple datasets.

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

The paper develops machine learning models to classify early and late stages of invasive ductal carcinoma using RNA-seq data from 610 patients, and provides a web server for prediction.

A Bayesian inference tool for identifying artifactual calls from differential transcript abundance analyses

The paper introduces ppcseq, a Bayesian tool to detect artifactual calls in differential transcript abundance analyses by identifying outliers in negative binomial models.

A Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

This paper introduces a Bayesian framework to detect gene expression outliers in single samples by creating a consensus background distribution without requiring a manual comparison set.

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional cancer data

The paper introduces gene selection methods to identify key biomarkers in cancer data, showing improved classification and pathway detection compared to existing methods.

SPECS: A non-parameteric method to identify tissue-specific molecular features for unbalanced sample groups

The paper introduces a non-parametric specificity score to identify tissue-specific genes from unbalanced datasets and applies it to GTEx data.