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Regular version of the site

Laboratory on AI for Computational Biology

Publications
Article
SpheroidPicker for automated 3D cell culture manipulation using deep learning

Grexa I., Diosdi A., Harmati M. et al.

Scientific Reports. 2021. Vol. 11.

Article
Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics

Sulimov P., Voronkova A. V., Kertesz-Farkas A.

Bioinformatics. 2020. Vol. 36. No. 12. P. 3781-3787.

Article
Test-time augmentation for deep learning-based cell segmentation on microscopy images

Moshkov N., Mathe B., Kertesz-Farkas A. et al.

Scientific Reports. 2020. Vol. 10. P. 5068.

About the Laboratory

Welcome to the AIC Lab. We create cutting-edge deep learning technologies for biomolecular medical data analysis to be used in life sciences and biomedical applications. We are an international, English-speaking, multidisciplinary group with a background in computer science, math, and molecular biology.

Join the English-speaking AIC Lab and gain hands on experience with high-performance computing technologies, statistical testing methods, and deep learning methods applied on interesting real-life applications. Research projects are available at any levels (BSC, MSc, PhD, Post-doc, summer practice, and summer internships). We are also hiring a C++ programmer! For more info contact Attila Kertesz-Farkas by email.


Illustration for news: Attila Kertesz-Farkas about New Lab and Research

Attila Kertesz-Farkas about New Lab and Research

Laboratory on AI for Computational Biology has opened at the Faculty not so long ago. We talked with its head, Attila Kertesz-Farkas, about the lab, research and his way in science.

Illustration for news: Seminar: "Introduction to computational mass spectrometry"

Seminar: "Introduction to computational mass spectrometry"