Kalin Nonchev, MSc
"From error to error, one discovers the entire truth." - Sigmund Freud
PhD Student
- kalin.nonchev@inf.ethz.ch
- Address
-
ETH Zurich
Biomedical Informatics Group
Department of Computer Science
Universitätstrasse 6 - Room
- CAB F 51.1
- @nonchevk
Committed to advancing machine learning research in biomedicine to improve clinical decision-making.
My research focuses on understanding the gap between genotype and phenotype in human diseases. Machine learning algorithms are key tools for uncovering biological patterns and interpreting medical datasets. I believe this is the key to more accurate disease diagnoses for patients, and, more importantly, the well-proven rationale for their therapies.
I studied bioinformatics at the Technical University of Munich and Ludwig-Maximilian University of Munich, followed by a Master's at ETH Zurich. During my Bachelor's, I worked with Prof. Julien Gagneur on rare disease genomics. In my Master's, I contributed to transcriptomics projects at the Functional Genomics Center Zurich and Roche Diagnostics. Currently, I am a doctoral candidate under Prof. Gunnar Rätsch's supervision. Meanwhile, I'm grateful to work with exceptional people who continually push the boundaries in biomedical informatics.
I am very open to research collaborations and mentoring BSc and MSc students. Please reach out if interested. Find out more on my homepage https://kalinnonchev.github.io.
Latest Publications
Abstract Spatial transcriptomics enables in-depth molecular characterization of samples on a morphology and RNA level while preserving spatial location. Integrating the resulting multi-modal data is an unsolved problem, and developing new solutions in precision medicine depends on improved methodologies. Here, we introduce AESTETIK, a convolutional deep learning model that jointly integrates spatial, transcriptomics, and morphology information to learn accurate spot representations. AESTETIK yielded substantially improved cluster assignments on widely adopted technology platforms (e.g., 10x Genomics™, NanoString™) across multiple datasets. We achieved performance enhancement on structured tissues (e.g., brain) with a 21% increase in median ARI over previous state-of-the-art methods. Notably, AESTETIK also demonstrated superior performance on cancer tissues with heterogeneous cell populations, showing a two-fold increase in breast cancer, 79% in melanoma, and 21% in liver cancer. We expect that these advances will enable a multi-modal understanding of key biological processes.
Authors Kalin Nonchev, Sonali Andani, Joanna Ficek-Pascual, Marta Nowak, Bettina Sobottka, Tumor Profiler Consortium, Viktor Hendrik Koelzer, and Gunnar Rätsch
Submitted MedRxiv