This study introduces Pro-Judice, a benchmark to assess how large language models align with procedural fairness in judicial contexts. Using criminal case data from China and US legal traditions, it reveals that models’ procedural fairness perceptions vary by legal context of test dataset, model architecture and version differences, and dataset-model interaction.

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

SUMMARY: Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/prada/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: An important question that has emerged from the recent success of genome-wide association studies (GWAS) is how to detect genetic signals beyond single markers/genes in order to explore their combined effects on mediating complex diseases and traits. Integrative testing of GWAS association data with that from prior-knowledge databases and proteome studies has recently gained attention. These methodologies may hold promise for comprehensively examining the interactions between genes underlying the pathogenesis of complex diseases. METHODS: Here, we present a dense module searching (DMS) method to identify candidate subnetworks or genes for complex diseases by integrating the association signal from GWAS datasets into the human protein-protein interaction (PPI) network. The DMS method extensively searches for subnetworks enriched with low P-value genes in GWAS datasets. Compared with pathway-based approaches, this method introduces flexibility in defining a gene set and can effectively utilize local PPI information. RESULTS: We implemented the DMS method in an R package, which can also evaluate and graphically represent the results. We demonstrated DMS in two GWAS datasets for complex diseases, i.e. breast cancer and pancreatic cancer. For each disease, the DMS method successfully identified a set of significant modules and candidate genes, including some well-studied genes not detected in the single-marker analysis of GWA studies. Functional enrichment analysis and comparison with previously published methods showed that the genes we identified by DMS have higher association signal. AVAILABILITY: dmGWAS package and documents are available at http://bioinfo.mc.vanderbilt.edu/dmGWAS.html.

Differentially expressed genes (DEGs) were identified as genes with up or down regulation fold change ≥ 2 and student t test P value ≤ 0.01. †Hub interaction number refers to the total number of interactions involving hub genes. ‡Hub genes were defined to have at least 5 interactions in each network.

Background DNA microarrays have been widely applied in cancer research for better diagnosis and prediction of the disease states. Traditionally, most microarray studies aim to identify differentially expressed genes (DEGs) by comparing the average gene expression levels between two groups (e.g., the treated vs. control or disease vs. non-disease) based on statistical analysis such as t-test and Significance Analysis of Microarrays (SAM) [1,2]. Materials and methods In this study, we defined the gene expression profile (GEP) of a gene as the distribution of the log2 values of its normalized expression signal intensities across the samples in the similarly studied microarrays. We hypothesized that the biomarker genes that distinguish disease samples from normal samples might form distinct GEPs between comparison groups. We applied Pearson Correlation Coefficient (PCC) and Kolmogorov-Smirnov Distance (KSD) metrics to identify disease-specific biomarkers by comparing GEPs between normal and disease states and then applied this technology to disease (e.g., cancer) related studies in order to discover some disease genes as biomarker candidates. These biomarkers’ gene profiles in normal and disease samples might be used to diagnose or monitor patient’s disease state via regular gene expression analysis.

BACKGROUND: Genes show different sensitivities in expression corresponding to various biological conditions. Systematical study of this concept is required because of its important implications in microarray analysis etc. J.H. Ohn et al. first studied this gene property with yeast transcriptional profiling data. RESULTS: Here we propose a calculation framework for gene expression sensitivity analysis. We also compared the functions, centralities and transcriptional regulations of the sensitive and robust genes. We found that the robust genes tended to be involved in essential cellular processes. Oppositely, the sensitive genes perform their functions diversely. Moreover while genes from both groups show similar geometric centrality by coupling them onto integrated protein networks, the robust genes have higher vertex degree and betweenness than that of the sensitive genes. An interesting fact was also found that, not alike the sensitive genes, the robust genes shared less transcription factors as their regulators. CONCLUSION: Our study reveals different propensities of gene expression to external perturbations, demonstrates different roles of sensitive genes and robust genes in the cell and proposes the necessity of combining the gene expression sensitivity in the microarray analysis.

BACKGROUND: In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches. RESULTS: In our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis - GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value. CONCLUSION: This approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at: http://omics.biosino.org:14000/kweb/workflow.jsp?id=00020.

Abstract Summary: MPSQ (multi-protein-states query) is a web-based tool for the discovery of protein states (e.g. biological interactions, covalent modifications, cellular localizations). In particular, large sets of genes can be used to search for enriched state transition network maps (NMs) and features facilitating the interpretation of genomic-scale experiments such as microarrays. One NM collects all the catalogued states of a protein as well as the mutual transitions between the states. For the returned NM, graph visualization is provided for easy understanding and to guide further analysis. Availability: MPSQ is freely available via the web at http://mpsq.biosino.org/. Contact: [email protected]; [email protected]; [email protected]

BACKGROUND: In the adaptive immune system, variable regions of immunoglobulin (IG) are encoded by random recombination of variable (V), diversity (D), and joining (J) gene segments in the germline. Partitioning the functional antibody sequences to their sourcing germline gene segments is vital not only for understanding antibody maturation but also for promoting the potential engineering of the therapeutic antibodies. To date, several tools have been developed to perform such "trace-back" calculations. Yet, the predicting ability and processing volume of those tools vary significantly for different sets of data. Moreover, none of them give a confidence for immunoglobulin heavy diversity (IGHD) identification. Developing fast, efficient and enhanced tools is always needed with the booming of immunological data. RESULTS: Here, a program named Ab-origin is presented. It is designed by batch query against germline databases based on empirical knowledge, optimized scoring scheme and appropriate parameters. Special efforts have been paid to improve the identification accuracy of the short and volatile region, IGHD. In particular, a threshold score for certain sensitivity and specificity is provided to give the confidence level of the IGHD identification. CONCLUSION: When evaluated using different sets of both simulated data and experimental data, Ab-origin outperformed all the other five popular tools in terms of prediction accuracy. The features of batch query and confidence indication of IGHD identification would provide extra help to users. The program is freely available at http://mpsq.biosino.org/ab-origin/supplementary.html.