Dr. Francesco Lescai on Computational Genomics, the SIEVE Framework and Explainable AI for Discovery
Sunday, October 18, 2026
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The speaker reports using cardiovascular disorder cohorts of 2,000 to 4,000 samples in pilot studies to evaluate the SIEVE framework's behavior in detecting variant interactions.
The speaker reports that they use explainability in AI to identify which input features enable a model's decision for biological discovery, rather than explaining how the model reaches a decision as regulators might require in hospital settings.
“for example explainability is very crucial for being able to employ ai in highly regulated environments like hospitals for example but in that specific area explainable doesn't mean the same thing that it means for me in the sense that i'm not entirely interested in my work in explaining how a model reaches its decision in the sense of a regulator might be interested into my interest is on the input itself therefore the way i'm interested in how the model reached a decision is what in the input enable that decision and that is the way i use explainability for discovery”
The speaker reports that their lab avoids rare disease research due to insufficient sample sizes for training neural networks, focusing instead on common disorders with inflammatory components.
In this episode of the University of Pittsburgh’s Health and Explainable AI Podcast, host Jordan Gass-Pooré speaks with Francesco Lescai, PhD, Associate Professor of Bioinformatics and Head of the Computational Genomics Laboratory at the University of Pavia. Reflecting on his career transition from wet-lab immunology to computational genomics, Francesco discusses his work, explainable AI and the necessity of interdisciplinary training across computer science, genetics, and health informatics to solve complex human disease traits. The conversation focuses on his lab's SIEVE framework (Sparse Interpretable Exome Variant Explainer), a deep learning framework that advances explainable AI by shifting the paradigm from post-hoc model auditing to using inherently interpretable architectures as active engines for biological discovery. For students and practitioners in explainable AI, his work offers practical insights into model development, demonstrating how training "null" baseline models ensures attributions reflect true biological signals over computational noise, how annotation-ablation protocols measure a model's reliance on raw genomic architecture versus pre-existing labels, and how capping GPU memory limits power consumption while actively measuring CO2 footprints to keep XAI sustainable. Francesco also highlights the necessity of making research openly available for external critique across disciplines to catch architectural blind spots and drive healthier, more collaborative science. Aligning with the mission of the University of Pittsburgh's Health and Explainable AI (HexAI) Research Laboratory, Francesco offers insights for students and computational researchers on navigating sparse clinical datasets, developing robust machine learning architectures for rare diseases, and advancing inter-institutional sample sharing networks. Reflecting core priorities of Pitt's Computational Pathology and AI Center of Excellence (CPACE), the discussion illustrates how explainable machine learning and sensor-driven molecular profiling advance the frontiers of computational pathology and precision diagnostics. He closes the episode by encouraging students to build transparent, interpretable models for genomic medicine Host: Jordan Gass-Pooré | Producer: Brent Phillips | Guest: Francesco Lescai, PhD (Associate Professor of Bioinformatics & Head of Computational Genomics Laboratory, University of Pavia) Topics: Computational Genomics | Explainable AI (XAI) | SIEVE Framework | Variant Interactions | Data Governance & Privacy | Sustainable Computing (CO2 Footprint) Institutions: University of Pittsburgh | University of Pavia | Norwegian University of Science and Technology (NTNU) Links: [Pitt HexAI Lab] https://pitthexai.github.io/ [SIEVE Project] https://lescailab.github.io/sieve-project/
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