Schlessinger, Avner

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 429

Impaired Amino Acid Transport at the Blood Brain Barrier Is a Cause of Autism Spectrum Disorder

Tărlungeanu, D. C., Deliu, E., Dotter, C. P., Kara, M., Janiesch, P. C., Scalise, M., Galluccio, M., Tesulov, M., Morelli, E., Sonmez, F. M., et al. (2016) · Cell

Amino Acid Enzymes and MetabolismAutism Spectrum Disorder Research

Cited by 367

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

Greenbaum, J. A., Andersen, P. H., Blythe, M., Bui, H., Cachau, R. E., Crowe, J., Davies, M., Kolaskar, A. S., Lund, O., Morrison, S., et al. (2007) · Journal of Molecular Recognition

vaccines and immunoinformatics approachesMachine Learning in Bioinformatics

Cited by 244

Integrative structure modeling of macromolecular assemblies from proteomics data

Lasker, K., Phillips, J. L., Russel, D., Velázquez-Muriel, J., Schneidman-Duhovny, D., Tjioe, E., Webb, B., Schlessinger, A., & Sali, A. (2010) · Molecular & Cellular Proteomics

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 71

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Stein, D., Sevim Bayrak, Ç., Wu, Y., Kars, M. E., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2022) · Genome Medicine

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 53

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Stein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 53

Identification of a G-Protein-Independent Activator of GIRK Channels

Zhao, Y., Ung, P. M.-U., Zahoránszky-Kőhalmi, G., Zakharov, A. V., Martinez, N. J., Simeonov, A., Glaaser, I. W., Rai, G., Schlessinger, A., Marugan, J. J., et al. (2020) · Cell Reports

Ion channel regulation and functionNeuroscience and Neuropharmacology Research

Cited by 39

Describing inhibitor specificity for the amino acid transporter LAT1 from metainference simulations

Hutchinson, K., Silva, D. B., Bohlke, J., Clausen, C., Thomas, A. A., Bonomi, M., & Schlessinger, A. (2022) · Biophysical Journal

Computational Drug Discovery MethodsProtein Structure and Dynamics

Cited by 18

Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer

Zatorski, N., Sun, Y., Elmas, A., Dallago, C., Karl, T., Stein, D., Rost, B., Huang, K.-L., Walsh, M., & Schlessinger, A. (2024) · iScience

Protein Structure and DynamicsGene expression and cancer classification

Cited by 2

Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue

Zatorski, N., Sun, Y., Elmas, A., Dallago, C., Karl, T., Stein, D., Rost, B., Huang, K.-L., Walsh, M., & Schlessinger, A. (2023) · iScience

Protein Structure and DynamicsGene expression and cancer classification

Cited by 2