Research output per year
Research output per year
Personal profile
Research interests
My research focuses on computational structural biology:
- Addicted CASPer
- Deep Learning for protein structure prediction
- Deep Learning for secondary structure prediction
- The MESHI software package
- Machine learning for estimation of model accuracy
- Datamining in the PDB
- Structure based function prediction
- Collaborations with experimental groups
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Collaborations and top research areas from the last five years
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Protein Design Using Physics Informed Neural Networks
Omar, S. I., Keasar, C., Ben-Sasson, A. J. & Haber, E., 1 Mar 2023, In: Biomolecules. 13, 3, 457.Research output: Contribution to journal › Article › peer-review
Open Access -
The Peptidyl-Prolyl cis-trans isomerase, Pin1, associates with Protein Kinase C θ via a critical Phospho-Thr-Pro motif in the V3 regulatory domain
Anto, N. P., Muraleedharan, A., Nath, P. R., Sun, Z., Keasar, C., Livneh, E., Braiman, A., Altman, A., Kong, K. F. & Isakov, N., 8 Mar 2023, In: Frontiers in Immunology. 14, 2023, 1126464.Research output: Contribution to journal › Article › peer-review
Open Access -
Estimation of model accuracy by a unique set of features and tree-based regressor
Bitton, M. & Keasar, C., 1 Dec 2022, In: Scientific Reports. 12, 1, 14074.Research output: Contribution to journal › Article › peer-review
Open Access1 Scopus citations -
High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics
Gerovichev, A., Sadeh, A., Winter, V., Bar-Massada, A., Keasar, T. & Keasar, C., 21 May 2021, In: Frontiers in Ecology and Evolution. 9, 600931.Research output: Contribution to journal › Article › peer-review
Open Access12 Scopus citations -
Mimetic Neural Networks: A unified framework for Protein Design and Folding.
Eliasof, M., Boesen, T., Haber, E., Keasar, C. & Treister, E., 7 Feb 2021.Research output: Working paper/Preprint › Preprint
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Modeling SARS-CoV-2 proteins in the CASP-commons experiment
AlphaFold team & CASP-COVID participants, 1 Dec 2021, In: Proteins: Structure, Function and Bioinformatics. 89, 12, p. 1987-1996 10 p.Research output: Contribution to journal › Article › peer-review
Open Access14 Scopus citations -
Predicting mutant outcome by combining deep mutational scanning and machine learning
Sarfati, H., Naftaly, S., Papo, N. & Keasar, C., 22 Jul 2021, (E-pub ahead of print) In: Proteins: Structure, Function and Bioinformatics. 90, 1, p. 45-57 13 p.Research output: Contribution to journal › Article › peer-review
6 Scopus citations -
Unraveling the hidden role of a uORF-encoded peptide as a kinase inhibitor of PKCs
Jayaram, D. R., Frost, S., Argov, C., Liju, V. B., Anto, N. P., Muraleedharan, A., Ben-Ari, A., Sinay, R., Smoly, I., Novoplansky, O., Isakov, N., Toiber, D., Keasar, C., Elkabets, M., Yeger-Lotem, E. & Livneh, E., 5 Oct 2021, In: Proceedings of the National Academy of Sciences of the United States of America. 118, 40, e2018899118.Research output: Contribution to journal › Article › peer-review
Open Access19 Scopus citations -
Redundancy-weighting the PDB for detailed secondary structure prediction using deep-learning models
Keasar, C. & Sidi, T., 31 Mar 2020, In: Bioinformatics. 36, 12, p. 3733-3738 6 p.Research output: Contribution to journal › Article › peer-review
6 Scopus citations -
Accurate protein structure prediction by embeddings and deep learning representations
Drori, I., Thaker, D., Srivatsa, A., Jeong, D., Wang, Y., Nan, L., Wu, F., Leggas, D., Lei, J., Lu, W. & Keasar, C., 2019.Research output: Working paper/Preprint › Preprint