https://bioinfo.pwr.edu.pl/Recent content in About on BioMIP research group | Wrocław TechHugo -- gohugo.ioen-ushttps://bioinfo.pwr.edu.pl/people/Mon, 01 Jan 0001 00:00:00 +0000https://bioinfo.pwr.edu.pl/people/Head Witold Dyrka, PhD, DSc, Eng. Office: Building D-1, Room 118 Phone: +48 71 320 44 61 Research Staff Filip Pietluch, PhD PhD Students Krzysztof Pysz, MSc, Eng. Sosina Sewunet, MSc, Eng.https://bioinfo.pwr.edu.pl/publications/Mon, 01 Jan 0001 00:00:00 +0000https://bioinfo.pwr.edu.pl/publications/Preprints Pysz K.G., Bartczak A., Kwiecień J., Krajewski P., Dyrka W. (2026). Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC. arXiv, arXiv:2606.27579. Journal Articles & Book Chapters Pysz K.G., Gałązka J., Dyrka W. (2025). Harnessing deep learning for proteome-scale detection of amyloid signaling motifs. Bioinformatics, 41(Supplement_1), i420–i428. Pietluch F., Mackiewicz P., Sidorczuk K., Gagat P. (2025). Dating the origin and spread of plastids and chromatophores. International Journal of Molecular Sciences, 26(12), 5569.https://bioinfo.pwr.edu.pl/research/Mon, 01 Jan 0001 00:00:00 +0000https://bioinfo.pwr.edu.pl/research/Our research focuses on three core pillars at the intersection of AI, computational biology, and biomedical translation: Fungal Immunology: Integrating genomic neighborhood analysis, structural modeling, and molecular dynamics to map fungal immune systems and identify novel antifungal targets. Peptides Research: Building machine learning tools and curated databases to predict, profile, and design antimicrobial and amyloidogenic peptides. Digital Pathology: Developing diagnostic AI tools in oncology centered on histopathology slide analysis, engineered for interpretability and uncertainty quantification.https://bioinfo.pwr.edu.pl/software/Mon, 01 Jan 0001 00:00:00 +0000https://bioinfo.pwr.edu.pl/software/ ASMscan ProteinBERT — Deep learning model for proteome-scale detection of amyloid signaling motifs (docker image and execution runner) ChloroGuide — Computational tool for plastid and chromatophore sequence analysis PCFG-CM — Probabilistic context-free grammars for proteins using contact map constraints Quantiprot — Python package for quantitative analysis of protein sequences