MOTIVATION: Disruption in normal gene expression can contribute to the development of diseases and chronic conditions. However, identifying disease-specific gene signatures can be challenging due to the presence of multiple co-occurring conditions and limited sample sizes. Unsupervised representation learning methods, such as matrix decomposition and deep learning, simplify high-dimensional data into understandable patterns, but often do not provide clear biological explanations. Incorporating prior biological knowledge directly can enhance understanding and address small sample sizes. Nevertheless, current models do not jointly consider prior knowledge of molecular interactions and sample labels. RESULTS: We present GRACKLE, a novel nonnegative matrix factorization approach that applies Graph Regularization Across Contextual KnowLedgE. GRACKLE integrates sample similarity and gene similarity matrices based on sample metadata and molecular relationships, respectively. Simulation studies show GRACKLE outperformed other NMF algorithms, especially with increased background noise. GRACKLE effectively stratified breast tumor samples and identified condition-enriched subgroups in individuals with Down syndrome. The model's latent representations aligned with known biological patterns, such as autoimmune conditions and sleep apnea in Down syndrome. GRACKLE's flexibility allows application to various data modalities, offering a robust solution for identifying context-specific molecular mechanisms in biomedical research. AVAILABILITY AND IMPLEMENTATION: GRACKLE is available at: https://github.com/lagillenwater/GRACKLE.

Motivation: Gene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate pathway-level changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment. Results: We compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets to show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis. Availability and implementation: GSEA-InContext implemented in Python, Supplementary results and the background expression compendium are available at: https://github.com/CostelloLab/GSEA-InContext.

The discovery and understanding of functional relationships amongst genes and their gene products are fundamental to our understanding of human disease. Data regarding protein-protein interactions in humans are not complete and computational techniques along with data provided from other organisms can be used to better inform relationships in humans. We demonstrate the utility of using genome scale data from Drosophila melanogaster to predict protein-protein relationships for human proteins specific to disease. To find the most likely candidates for protein-protein interaction, the predicted relationships are tested against human protein interaction and known disease data, then ranked through a support vector machine. An illustrative example related to hBrm and hSNF5 shows the validity of our approach

Recent events have made it clear that some kinds of technical texts, generated by machine and essentially meaningless, can be confused with authentic, technical texts written by humans. We identify this as a potential problem, since no existing systems for, say the web, can or do discriminate on this basis. We believe that there are subtle, short- and long-range word or even string repetitions extant in human texts, but not in many classes of computer generated texts, that can be used to discriminate based on meaning. In this paper we employ universal lossless source coding to generate features in a high-dimensional space and then apply support vector machines to discriminate between the classes of authentic and inauthentic expository texts. Compression profiles for the two kinds of text are distinct—the authentic texts being bounded by various classes of more compressible or less compressible texts that are computer generated. This in turn led to the high prediction accuracy of our models which support a conjecture that there exists a relationship between meaning and compressibility. Our results show that the learning algorithm based upon the compression profile outperformed standard term-frequency text categorization on several non-trivial classes of inauthentic texts. Availability: http://www.informatics.indiana.edu/predrag/fsi.htm.