Gonçalves lab
@goncalveslab.bsky.social
Gonçalves Lab @TUDelft (CS @EEMCS_TUD). Research on Computational Molecular Biomedicine, Machine Learning, Oncology by @joanagoncalves.bsky.social et al. Website: https://goncalveslab.tudelft.nl
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Looking back on a most important event: Yasin Tepeli masterfully defended his PhD, 16 Jun 2025, on effective strategies for therapeutic target prediction & selection bias mitigation in ML edu.nl/hv4xt. Congratulations Dr. Tepeli!
Looking for his next move, snatch him.
Looking for his next move, snatch him.
Looking back on a most important event: Yasin Tepeli masterfully defended his PhD, 16 Jun 2025, on effective strategies for therapeutic target prediction & selection bias mitigation in ML edu.nl/hv4xt. Congratulations Dr. Tepeli!
Looking for his next move, snatch him.
Looking for his next move, snatch him.
October 3, 2025 at 2:55 PM
Looking back on a most important event: Yasin Tepeli masterfully defended his PhD, 16 Jun 2025, on effective strategies for therapeutic target prediction & selection bias mitigation in ML edu.nl/hv4xt. Congratulations Dr. Tepeli!
Looking for his next move, snatch him.
Looking for his next move, snatch him.
Paper by our Sander Goossens! Shows we can learn tumor-relevant mutational signatures of DDR deficiency from gene perturbation screens with mutation profiling after DNA damage. Check out SNMF (supervised NMF)! doi.org/10.1101/2024... #DDR #DNARepair #MachineLearning
SNMF: Integrated Learning of Mutational Signatures and Prediction of DNA Repair Deficiencies
Motivation Many tumours show deficiencies in DNA damage response (DDR), which influence tumorigenesis and progression, but also expose vulnerabilities with therapeutic potential. Assessing which patie...
doi.org
February 1, 2025 at 5:35 PM
Paper by our Sander Goossens! Shows we can learn tumor-relevant mutational signatures of DDR deficiency from gene perturbation screens with mutation profiling after DNA damage. Check out SNMF (supervised NMF)! doi.org/10.1101/2024... #DDR #DNARepair #MachineLearning
Paper by our Yasin Tepeli: How to train fairer ML models from data affected by selection bias? Diversity! DCAST learns from diverse vs. most confident samples to avoid confirmation bias via semi-supervised learning. #MachineLearning #FairnessML arxiv.org/abs/2409.20126
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning
Fairness in machine learning seeks to mitigate model bias against individuals based on sensitive features such as sex or age, often caused by an uneven representation of the population in the training...
arxiv.org
December 1, 2024 at 2:54 PM
Paper by our Yasin Tepeli: How to train fairer ML models from data affected by selection bias? Diversity! DCAST learns from diverse vs. most confident samples to avoid confirmation bias via semi-supervised learning. #MachineLearning #FairnessML arxiv.org/abs/2409.20126