Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Mukund, Sudarshan"'
Autor:
Boyang Fu, Ali Pazokitoroudi, Mukund Sudarshan, Zhengtong Liu, Lakshminarayanan Subramanian, Sriram Sankararaman
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-8 (2023)
Abstract Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of
Externí odkaz:
https://doaj.org/article/8c935b330da34872b12393717d129cb6
Autor:
Narges Razavian, Vincent J. Major, Mukund Sudarshan, Jesse Burk-Rafel, Peter Stella, Hardev Randhawa, Seda Bilaloglu, Ji Chen, Vuthy Nguy, Walter Wang, Hao Zhang, Ilan Reinstein, David Kudlowitz, Cameron Zenger, Meng Cao, Ruina Zhang, Siddhant Dogra, Keerthi B. Harish, Brian Bosworth, Fritz Francois, Leora I. Horwitz, Rajesh Ranganath, Jonathan Austrian, Yindalon Aphinyanaphongs
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-13 (2020)
Abstract The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 ret
Externí odkaz:
https://doaj.org/article/24a1e6844b2b4deb962b0c15b73995db
SummaryMachine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12a7c7f70e9617d3a69835ce6ea65070
https://doi.org/10.1101/2022.10.01.510472
https://doi.org/10.1101/2022.10.01.510472
Autor:
Boyang Fu, Ali Pazokitoroudi, Mukund Sudarshan, Lakshminarayanan Subramanian, Sriram Sankararaman
Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a powerful approach to test for nonlinear relationships between sets of genetic var
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a36ef4b045e5d01dd88a491f14deb2e0
https://doi.org/10.1101/2022.04.13.488214
https://doi.org/10.1101/2022.04.13.488214
Autor:
David Kudlowitz, Cameron Zenger, Meng Cao, Ruina Zhang, Fritz Francois, Ilan Reinstein, Brian P. Bosworth, Mukund Sudarshan, Walter Wang, Vincent J. Major, Hao Zhang, Siddhant Dogra, Ji Chen, Jesse Burk-Rafel, Narges Razavian, Yindalon Aphinyanaphongs, Hardev Randhawa, Leora I. Horwitz, Jonathan S. Austrian, Vuthy Nguy, Rajesh Ranganath, Seda Bilaloglu, Peter Stella, Keerthi B. Harish
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-13 (2020)
NPJ Digital Medicine
npj Digital Medicine
NPJ Digital Medicine
npj Digital Medicine
The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8f2863845c258709070c3de3dc1baa4
https://doaj.org/article/24a1e6844b2b4deb962b0c15b73995db
https://doaj.org/article/24a1e6844b2b4deb962b0c15b73995db
Autor:
Mukund Sudarshan, Suraj Sarvode Mothi, Brett A. Clementz, Neeraj Tandon, Godfrey D. Pearlson, Carol A. Tamminga, John A. Sweeney, Matcheri S. Keshavan
Publikováno v:
Schizophrenia Research. 214:60-69
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba2d9e29c65cbd671ee1085a6a9760b7
https://doi.org/10.1016/j.schres.2018.04.037
https://doi.org/10.1016/j.schres.2018.04.037
Publikováno v:
Proc Mach Learn Res
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce the cost of providing interpretations by learning a global selector mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::3e91316c6ee4d769565fefc49f5b6464
https://pubmed.ncbi.nlm.nih.gov/33954293
https://pubmed.ncbi.nlm.nih.gov/33954293
Autor:
Kruti Modi, Mukund Sudarshan, Somavally Dalvi, Prasanna Venkatraman, Sorab N. Dalal, Prafful Nadkarni, Suchismita Dey, Arunabha Bose, Tapas K. Kundu
Publikováno v:
Genes to cells : devoted to molecularcellular mechanismsREFERENCES. 26(6)
14-3-3 proteins bind to ligands via phospho-serine containing consensus motifs. However, the molecular mechanisms underlying complex formation and dissociation between 14-3-3 proteins and their ligands remain unclear. We identified two conserved acid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::476e8bef31aaa386060a8364bed38a38
https://pubmed.ncbi.nlm.nih.gov/33813791
https://pubmed.ncbi.nlm.nih.gov/33813791
Autor:
Matcheri S. Keshavan, Mukund Sudarshan
Publikováno v:
Schizophrenia Research. 188:178-181
Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machine learning approaches can potentially help us understand how the brain normally interprets sensory
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ceb9c64e4452f4febaa531eba4b4344
https://doi.org/10.1016/j.schres.2017.01.020
https://doi.org/10.1016/j.schres.2017.01.020
Autor:
Arunabha Bose, Kruti Modi, Sorab N. Dalal, Mukund Sudarshan, Tapas K. Kundu, Somavally Dalvi, Prasanna Venkatraman, Prafful Nadkarni, Suchismita Dey
14-3-3 proteins bind to ligands via phospho-Serine containing consensus motifs. However, the molecular mechanisms underlying complex formation and dissociation between 14-3-3 proteins and their ligands remain unclear. We identified two conserved acid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73dfd3a20988d3953ce0e4315a7ed50b
https://doi.org/10.1101/2019.12.19.883264
https://doi.org/10.1101/2019.12.19.883264