Gene expression and cancer classification

A new local covariance matrix estimation for the classification of gene expression profiles in RNA-Seq data

This paper introduces a local covariance matrix estimation method for RNA-Seq data classification, showing improved performance over traditional methods.

Correcting for experiment-specific variability in expression compendia can remove underlying signals

The paper simulates gene expression compendia to show that technical variability masks biological signals, but correction methods help when few experiments are combined, while more experiments make detection easier without correction.

A multivariate method to correct for batch effects in microbiome data

This paper introduces a multivariate batch correction method for microbiome data, showing it effectively removes batch effects while preserving treatment variation.

ECMarker: Interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages

Improving the sensitivity of differential-expression analyses for under-powered RNA-seq experiments

This paper improves sensitivity in differential expression analysis for under-powered RNA-seq experiments by learning a decision boundary to control FDR and combining it with elastic-net logistic regression.

Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

The paper identifies gene signatures predictive of asthma severity using a denoising autoencoder and random forest classifier applied to sputum gene expression data.

Amalgams: data-driven amalgamation for the reference-free dimensionality reduction of zero-laden compositional data

This paper introduces a data-driven method for dimensionality reduction of compositional data using amalgamation, achieving performance comparable to state-of-the-art methods while producing interpretable features.

Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer

The paper shows that integrating multiple omics data sources improves predictive models for cancer outcomes by preserving both joint and individual variation patterns.

Stable gene expression for normalisation and single-sample scoring

The paper introduces stingscore, a method that uses stably-expressed genes to score transcriptomic signatures from individual samples, improving normalization and enabling clinical applications.

A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes

This paper introduces SampleLASSO, a method for imputing unmeasured gene expression in microarray data, showing it outperforms existing methods across multiple tasks.

DeepInsight-FS: Selecting features for non-image data using convolutional neural network

DeepInsight-FS uses a convolutional neural network to select clinically relevant gene subsets from non-image data by converting samples into image-form.

Adversarial Deconfounding Autoencoder for Learning Robust Gene Expression Embeddings

The paper introduces AD-AE, an adversarial deconfounding autoencoder that generates gene expression embeddings free of confounders and capable of generalizing across domains.

Effects of underlying gene-regulation network structure on prediction accuracy in high-dimensional regression

This study investigates how gene-regulation network structure affects prediction accuracy in high-dimensional regression using Monte Carlo simulations.

CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training

CancerSiamese improves cancer type prediction using one-shot learning with gene expression data, achieving 8% and 4% accuracy gains over 1-NN for primary and metastatic tumors.

ComBat-Seq: batch effect adjustment for RNA-Seq count data

ComBat-Seq adjusts RNA-Seq count data for batch effects using negative binomial regression, improving statistical power and false positive control in differential expression analyses.

Machine Learning Based Refined Differential Gene Expression Analysis of Pediatric Sepsis

This study refines differential gene expression analysis using machine learning to identify a 10-gene signature for predicting pediatric sepsis mortality with an AUC of 0.89.

EmpiReS: Differential Analysis of Gene Expression and Alternative Splicing

EmpiReS improves detection of differentially expressed and alternatively spliced genes using fold change analysis with empirical error estimation.

Universal nature of drug treatment responses in drug-tissue-wide model-animal experiments using tensordecomposition-based unsupervised featureextraction

The paper applies tensor decomposition-based unsupervised feature extraction to gene expression data from drug-treated mouse tissues, identifying common drug effects across tissue groups.

DataRemix: a universal data transformation for optimal inference from gene expression datasets

DataRemix is a normalization method that enhances biological signal detection in gene expression data by tuning hidden factors, outperforming existing methods without external knowledge.

The optimal discovery procedure for significance analysis of general gene expression studies

The paper extends the optimal discovery procedure (ODP) to handle complex study designs and RNA sequencing, showing improved performance in detecting differentially expressed genes.

An approach for normalization and quality control for NanoString RNA expression data

This paper develops a normalization and quality control approach for NanoString RNA data, showing improved handling of technical variation without loss of biological variation.

Data Integration with SUMO Detects Latent Relationships Between Patients in Lower-Grade Gliomas

The paper integrates multi-omic data to identify a prognostic subtype in lower-grade gliomas with genomic features similar to Grade IV gliomas.

A clinically validated human capillary blood transcriptome test for global systems biology studies

The paper develops a clinically validated transcriptome test using capillary blood for global systems biology studies, enabling affordable and accessible gene expression analysis.

Probability-based methods for outlier detection in replicated high-throughput biological data

The paper develops probability-based methods for detecting outliers in replicated high-throughput biological data and demonstrates their application using experimental and simulated data.

Extended Application of Genomic Selection to Screen Multi-Omics Data for Prognostic Signatures of Prostate Cancer

The paper applies genomic selection methods to multi-omics data to develop prognostic signatures for prostate cancer, showing improved predictability and computational efficiency.

A Hierarchical Bayesian Mixture Model forInferring the Expression State of Genes inTranscriptomes

This paper introduces a hierarchical Bayesian mixture model to infer gene expression states from transcriptomic data, showing high accuracy in both simulated and empirical datasets.

dream: Powerful differential expression analysis for repeated measures designs

dream improves differential expression analysis for repeated measures designs by reducing false positives and increasing power across multiple datasets.

Recognition Genes Which Are Relative With Cancer By Deep Learning Algorithm

This paper proposes a deep learning method to identify cancer-related genes using semi-supervised and unsupervised algorithms, showing improved performance over traditional statistical methods.

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures

The paper introduces a new information-theoretic distance measure, TSD, to assess transcriptomic similarity between tissue samples using RNA-seq data and curated gene signatures.

Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis

This paper applies style transfer methods to RNA-Seq data harmonization and treatment outcome prediction, showing improved style and semantics prediction accuracy compared to existing approaches.