Gene expression and cancer classification

Assessing the basal gene expression of cancer cell lines for in vitro transcriptomic toxicology screening

The paper assesses the basal gene expression of cancer cell lines to determine their relevance to specific human tissues for in vitro toxicology screening.

ABDS: tool suite for analyzing biologically diverse samples

ABDS provides tools for analyzing biologically diverse samples by addressing missing value imputation, signature gene detection, and expression pattern visualization.

Development of a CMS Classifier for Clinical CRC Samples

The paper introduces NanoClassifier, a NanoString-based CMS classifier for CRC samples, showing strong accuracy in predicting CMS subtypes from FFPE and FF tumors.

Sagittarius: Extrapolating Heterogeneous Time-Series Gene Expression Data

Sagittarius simulates gene expression at unmeasured timepoints using a transformer model, improving understanding of temporal dynamics in biology.

MTM: a multi-task learning framework to predict individualized tissue gene expression profiles

MTM predicts individualized tissue gene expression profiles from peripheral samples using a multi-task learning framework, improving prediction accuracy and preserving biological variation.

Gene Expression and Physiological traits in Mice

The paper analyzes gene expression and clinical traits in mice to find gene-trait associations using ANOVA, finding statistically significant genes linked to clinical outcomes.

Pattern-centric transformation of omics-data sources grounded on multi-wise gene associations aids predictive tasks in TCGA while ensuring interpretability.

The paper transforms omics data from gene-centric to pattern-centric to improve predictive performance and interpretability in TCGA data.

Utilizing tumor microenvironment microbial profiles and host gene expressions for survival subtyping in diverse types of cancers

This study develops ASD-cancer, a deep learning framework that identifies survival subtypes in cancers by integrating tumor microbiome and transcriptome data, revealing microbial-gene interactions linked to patient outcomes.

kimma: flexible linear mixed effects modeling with kinship for RNA-seq data

kimma is an R package for linear mixed effects modeling of RNA-seq data, offering improved sensitivity and model complexity compared to existing pipelines.

Plasma-free samples for transcriptomic analysis: a potential alternative to whole blood samples

This study compares transcriptomic profiles of whole blood, plasma-free blood, and serum-free blood samples, finding that plasma-free blood is more similar to whole blood in gene expression and immune characterization.

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

CONNECTOR analyzes longitudinal tumor growth data to identify clusters and reveal new molecular associations with therapy response.

RoPE: a robust profile likelihood method for differential gene expression analysis

RoPE improves differential gene expression analysis by using a data-driven adjustment and robust profile likelihood ratio test, showing better performance with larger sample sizes and reliable error control.

LOCC: a novel visualization and scoring of cutoffs for continuous variables

LOCC is a tool for visualizing and scoring cutoffs in continuous variables for dichotomous outcomes, showing better performance than ROC curves in prognostic modeling.

E2EGraph: An End-to-end Graph Learning Model for Interpretable Prediction of Pathlogical Stages in Prostate Cancer

E2EGraph predicts prostate cancer pathological stages using gene expression data and graph neural networks, achieving state-of-the-art performance with interpretable marker gene identification.

Patterns of unwanted biological and technical expression variation across 49 human tissues

This study identifies biological and technical sources of expression variation across 49 human tissues using GTEx data, revealing 522 variable transcript clusters with varying confidence in their explanations.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The paper evaluates methods to detect genes with differential expression dispersion in cancer datasets, identifying autophagy-related functions associated with tumor progression.

A field-wide assessment of differential high throughput sequencing reveals widespread bias

This study evaluates differential expression analysis in HT-Seq by analyzing p-value distributions across datasets, finding widespread bias and inconsistent results.

Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously

This study evaluates normalization methods for combining microarray and RNA-seq data, finding that quantile and Training Distribution Matching normalization work best for machine learning tasks.

Identification of Significant Gene Expression Changes in Multiple Perturbation Experiments using Knockoffs

The paper introduces a method to identify significant gene expression changes in multiple perturbation experiments using knockoffs and applies it to data from the LINCS program.

Gene-signatures for early detection of cancers

This paper introduces a new algorithm, NCT, to identify gene signatures for early cancer detection using RNA-seq data from TCGA and GTEx, showing improved classification performance.

Using Biological Constraints to Improve Prediction in Precision Oncology

The paper develops mechanistic classifiers using biological constraints to improve the robustness and interpretability of gene signatures in cancer prediction.

Uncovering the Consequences of Batch-Effect Associated Missing Values in Omics Data Analysis

This paper investigates how batch-effect associated missing values (BEAMs) impact missing value imputation performance and highlights the need for improved strategies.

The Vacc-SeqQC Project: Benchmarking RNA-Seq for Clinical Vaccine Studies

This study benchmarks RNA-Seq technical parameters in a clinical vaccine trial to establish guidelines for future studies.

Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning

The paper uses machine learning on public transcriptome data to predict age and sex from gene expression and identify molecular signatures associated with age and sex differences.

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration

NMFClustering provides GPU-accelerated non-negative matrix factorization for efficient clustering of large RNA-Seq and scRNA-seq datasets.

Percolate: an exponential family JIVE model to design DNA-based predictors of drug response

Percolate combines gene expression with other genomic data to create robust DNA-based predictors of drug response, outperforming individual data types.

CAT PETR: A Graphical User Interface for Differential Analysis of Phosphorylation Data

CAT PETR is a web application for differential analysis of phosphorylation data from antibody microarrays, offering improved data input and visualization compared to existing tools.

Quantifying Intratumor Heterogeneity by Key Genes Selected Using Concrete Autoencoder

The paper develops a deep learning framework using Concrete Autoencoder to identify key genes for quantifying intratumor heterogeneity, showing that a subset of genes suffices for accurate ITH scoring.

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer specific driver

SCOPE identifies stable core genes and pathways across cancers, revealing shared genome instability pathways and a kidney cancer driver.

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

This study identifies a loss of cell identity in ageing and cancer using transcriptomic data, showing distinct patterns in gene expression associated with patient survival.