Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Ranadip, Pal"'
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based re
Externí odkaz:
https://doaj.org/article/a724ecd9514e46e1ad4599c548481f0d
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Convolutional Neural Networks (CNN) are often unsuitable for predictive modeling involving nonimage based biological features. Here, the authors present a mapping termed REFINED to represent high dimensional vectors as compact images with spatial cor
Externí odkaz:
https://doaj.org/article/6277e3dea67843cfa33738444ad4f63f
Publikováno v:
BMC Genomics, Vol 21, Iss S9, Pp 1-3 (2020)
Externí odkaz:
https://doaj.org/article/e98519482e10464291296fe7eb7f6a4d
Autor:
Noah E. Berlow, Rishi Rikhi, Mathew Geltzeiler, Jinu Abraham, Matthew N. Svalina, Lara E. Davis, Erin Wise, Maria Mancini, Jonathan Noujaim, Atiya Mansoor, Michael J. Quist, Kevin L. Matlock, Martin W. Goros, Brian S. Hernandez, Yee C. Doung, Khin Thway, Tomohide Tsukahara, Jun Nishio, Elaine T. Huang, Susan Airhart, Carol J. Bult, Regina Gandour-Edwards, Robert G. Maki, Robin L. Jones, Joel E. Michalek, Milan Milovancev, Souparno Ghosh, Ranadip Pal, Charles Keller
Publikováno v:
BMC Cancer, Vol 19, Iss 1, Pp 1-23 (2019)
Abstract Background Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitabl
Externí odkaz:
https://doaj.org/article/6013cd28c33a417d9d673beb4af0154c
Publikováno v:
BMC Bioinformatics, Vol 20, Iss S12, Pp 1-12 (2019)
Abstract Background Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. O
Externí odkaz:
https://doaj.org/article/d306180542bc4ad085609339d68b757e
Autor:
Narendra Bharathy, Noah E. Berlow, Eric Wang, Jinu Abraham, Teagan P. Settelmeyer, Jody E. Hooper, Matthew N. Svalina, Zia Bajwa, Martin W. Goros, Brian S. Hernandez, Johannes E. Wolff, Ranadip Pal, Angela M. Davies, Arya Ashok, Darnell Bushby, Maria Mancini, Christopher Noakes, Neal C. Goodwin, Peter Ordentlich, James Keck, Douglas S. Hawkins, Erin R. Rudzinski, Atiya Mansoor, Theodore J. Perkins, Christopher R. Vakoc, Joel E. Michalek, Charles Keller
Publikováno v:
Skeletal Muscle, Vol 9, Iss 1, Pp 1-10 (2019)
Abstract Background Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in the pediatric cancer population. Survival among metastatic RMS patients has remained dismal yet unimproved for years. We previously identified the class I-specific h
Externí odkaz:
https://doaj.org/article/61f97051a5764d4b94672ae99d9ce9fa
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S17, Pp 51-63 (2018)
Abstract Background In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the iss
Externí odkaz:
https://doaj.org/article/888f0f9be1464466a7dbb5545c5afd1d
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S3, Pp 21-33 (2018)
Abstract Background A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic
Externí odkaz:
https://doaj.org/article/e8cab06084ba461b955716abbb926ef4
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S3, Pp 1-4 (2018)
Externí odkaz:
https://doaj.org/article/6f4d3596a7a446e1ad83fce6da571234
Publikováno v:
BMC Bioinformatics, Vol 20, Iss S12, Pp 1-3 (2019)
Externí odkaz:
https://doaj.org/article/2879f5903b004134a90a9870eef01a0c