In biomedicine, survival analysis addresses time-to-event data to study outcomes like patient survival and treatment response, and supports biomarker discovery. Yet, teaching this analysis is often hindered by mathematical and programming barriers. We present a structured, hands-on tutorial that goes beyond a typical online guide-offering integrated video lectures, literature, quizzes, and practical exercises. Built around Orange Data Mining, an open and free no-code visual analytics platform, the tutorial covers key concepts such as censoring, Kaplan-Meier curves, group comparisons, and biomarker discovery through real-world datasets. Organized in four pedagogical units, it progresses from basic survival data analysis to gene and gene-set biomarker discovery. Designed for 2-3 hours of learning, it supports both individual study and classroom use, and was successfully tested with over 120 participants.

Disease progression is closely linked to shifts in the expression levels of specific genes within molecular pathways. While gene set enrichment analysis is a widely employed method for identifying key disease markers, it has been underutilized in survival analysis. Here, we introduce a novel computational approach that adapts gene set enrichment analysis for survival analysis. The proposed approach considers a gene set, computes a single-sample gene set enrichment score, and, based on this score, splits the samples into cohorts. It then scores the gene sets by evaluating the differences in survival rates between the resulting cohorts. We aim to find gene sets that can lead to cohorts with significantly different survival probabilities. Utilizing gene expression data from The Cancer Genome Atlas and gene sets from the Molecular Signature Database, our results demonstrate that existing empirical research consistently supports the top gene sets our approach associates with survival prognosis. The proposed method broadens gene set enrichment analysis applications to include information on survival, bridging the gap between alterations in molecular pathways and their implications on survival.

MOTIVATION: Providing students with individualized feedback through assignments is a cornerstone of education that supports their learning and development. Studies have shown that timely, high-quality feedback plays a critical role in improving learning outcomes. However, providing personalized feedback on a large scale in classes with large numbers of students is often impractical due to the significant time and effort required. Recent advances in natural language processing and large language models (LLMs) offer a promising solution by enabling the efficient delivery of personalized feedback. These technologies can reduce the workload of course staff while improving student satisfaction and learning outcomes. Their successful implementation, however, requires thorough evaluation and validation in real classrooms. RESULTS: We present the results of a practical evaluation of LLM-based graders for written assignments in the 2024/25 iteration of the Introduction to Bioinformatics course at the University of Ljubljana. Over the course of the semester, more than 100 students answered 36 text-based questions, most of which were automatically graded using LLMs. In a blind study, students received feedback from both LLMs and human teaching assistants (TAs) without knowing the source, and later rated the quality of the feedback. We conducted a systematic evaluation of six commercial and open-source LLMs and compared their grading performance with human TAs. Our results show that with well-designed prompts, LLMs can achieve grading accuracy and feedback quality comparable to human graders. Our results also suggest that open-source LLMs perform as well as commercial LLMs, allowing schools to implement their own grading systems while maintaining privacy.

MOTIVATION: We learn more effectively through experience and reflection than through passive reception of information. Bioinformatics offers an excellent opportunity for project-based learning. Molecular data are abundant and accessible in open repositories, and important concepts in biology can be rediscovered by reanalyzing the data. RESULTS: In the manuscript, we report on five hands-on assignments we designed for master's computer science students to train them in bioinformatics for genomics. These assignments are the cornerstones of our introductory bioinformatics course and are centered around the study of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). They assume no prior knowledge of molecular biology but do require programming skills. Through these assignments, students learn about genomes and genes, discover their composition and function, relate SARS-CoV-2 to other viruses, and learn about the body's response to infection. Student evaluation of the assignments confirms their usefulness and value, their appropriate mastery-level difficulty, and their interesting and motivating storyline. AVAILABILITY AND IMPLEMENTATION: The course materials are freely available on GitHub at https://github.com/IB-ULFRI.

Abstract Unveiling gene interactions is crucial for comprehending biological processes, particularly their combined impact on phenotypes. Computational methodologies for gene interaction discovery have been extensively studied, but their application to censored data has yet to be thoroughly explored. Our work introduces a data-driven approach to identifying gene interactions that profoundly influence survival rates through the use of survival analysis. Our approach calculates the restricted mean survival time (RMST) for gene pairs and compares it against their individual expressions. If the interaction’s RMST exceeds that of the individual gene expressions, it suggests a potential functional association. We focused on L1000 landmark genes using TCGA na METABRIC data sets. Our findings demonstrate numerous additive and competing interactions and a scarcity of XOR-type interactions. We substantiated our results by cross-referencing with existing interactions in STRING and BioGRID databases and using large language models to summarize complex biological data. Although many potential gene interactions were hypothesized, only a fraction have been experimentally explored. This novel approach enables biologists to initiate a further investigation based on our ranked gene pairs and the generated literature summaries, thus offering a comprehensive, data-driven approach to understanding gene interactions affecting survival rates.