Biomedical data harmonization is essential for enabling exploratory analyses and meta-studies, but the process of schema matching-identifying semantic correspondences between elements of disparate datasets (schemas)-remains a labor-intensive and error-prone task. Even state-of-the-art automated methods often yield low accuracy when applied to biomedical schemas due to the large number of attributes and nuanced semantic differences between them. We present BDIViz, a novel visual analytics system designed to streamline the schema matching process for biomedical data. Through formative studies with domain experts, we identified key requirements for an effective solution and developed interactive visualization techniques that address both scalability challenges and semantic ambiguity. BDIViz employs an ensemble approach that combines multiple matching methods with LLM-based validation, summarizes matches through interactive heatmaps, and provides coordinated views that enable users to quickly compare attributes and their values. Our method-agnostic design allows the system to integrate various schema matching algorithms and adapt to application-specific needs. Through two biomedical case studies and a within-subject user study with domain experts, we demonstrate that BDIViz significantly improves matching accuracy while reducing cognitive load and curation time compared to baseline approaches.

BACKGROUND: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. RESULTS: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. CONCLUSIONS: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

Breast cancer is the second most common cancer type and is the leading cause of cancer-related deaths worldwide. Since it is a heterogeneous disease, subtyping breast cancer plays an important role in performing a specific treatment. Gene expression data is a viable alternative to be employed on cancer subtype classification, as they represent the state of a cell at the molecular level, but generally has a relatively small number of samples compared to a large number of genes. Gene selection is a promising approach that addresses this uneven high-dimensional matrix of genes versus samples and plays an important role in the development of efficient cancer subtype classification. In this work, an innovative outlier-based gene selection (OGS) method is proposed to select relevant genes for efficiently and effectively classify breast cancer subtypes. Experiments show that our strategy presents an$F_{1}$score of 1.0 for basal and 0.86 for her 2, the two subtypes with the worst prognoses, respectively. Compared to other methods, our proposed method outperforms in the$F_{1}$score using 80% less genes. In general, our method selects only a few highly relevant genes, speeding up the classification, and significantly improving the classifier’s performance.

MOTIVATION: Retrotransposition is an important force in shaping the human genome and is involved in prenatal development, disease and aging. Current genome browsers are not optimized for visualizing the experimental evidence for retrotransposon insertions. RESULTS: We have developed a specialized browser to visualize the evidence for retrotransposon insertions for both targeted and whole-genome sequencing data. AVAILABILITY AND IMPLEMENTATION: TranspoScope's source code, as well as installation instructions, are available at https://github.com/FenyoLab/transposcope.

MOTIVATION: LINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE-1 elements are repetitive, it is difficult to quantify LINE-1 RNA at specific loci and to separate transcripts with protein coding capability from other sources of LINE-1 RNA. RESULTS: We provide a tool, L1EM that uses the expectation maximization algorithm to quantify LINE-1 RNA at each genomic locus, separating transcripts that are capable of generating retrotransposition from those that are not. We show the accuracy of L1EM on simulated data and against long read sequencing from HEK cells. AVAILABILITY AND IMPLEMENTATION: L1EM is written in python. The source code along with the necessary annotations are available at https://github.com/FenyoLab/L1EM and distributed under GPLv3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: The Global Proteome Machine and Database (GPMDB) representational state transfer (REST) service was designed to provide simplified access to the proteomics information in GPMDB using a stable set of methods and parameters. Version 1 of this interface gives access to 25 methods for retrieving experimental information about protein post-translational modifications, amino acid variants, alternate splicing variants and protein cleavage patterns. AVAILABILITY AND IMPLEMENTATION: GPMDB data and database tables are freely available for commercial and non-commercial use. All software is also freely available, under the Artistic License. http://rest.thegpm.org/1 (GPMDB REST Service), http://wiki.thegpm.org/wiki/GPMDB_REST (Service description and help), and http://www.thegpm.org (GPM main project description and documentation). The code for the interface and an example REST client is available at ftp://ftp.thegpm.org/repos/gpmdb_rest

SUMMARY: An XML derived from a data model designed to be a hierarchical representation of an organism has been specified and a browser to use this language has been developed. AVAILABILITY: The language definition is available in HTML form at http://www.proteometrics.com/BIOML/. The BioML browser is available on request from the author.

David Fenyö; A software tool for the analysis of mass spectrometric disulfide mapping experiments, Bioinformatics, Volume 13, Issue 6, 1 December 1997, Pages 61