1 code implementation • 30 Nov 2023 • Emanuele Cavalleri, Alberto Cabri, Mauricio Soto-Gomez, Sara Bonfitto, Paolo Perlasca, Jessica Gliozzo, Tiffany J. Callahan, Justin Reese, Peter N Robinson, Elena Casiraghi, Giorgio Valentini, Marco Mesiti
The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.
1 code implementation • 11 Jul 2023 • Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski, Luca Cappelletti, Sanya B. Taneja, Jordan M. Wyrwa, Elena Casiraghi, Nicolas A. Matentzoglu, Justin Reese, Jonathan C. Silverstein, Charles Tapley Hoyt, Richard D. Boyce, Scott A. Malec, Deepak R. Unni, Marcin P. Joachimiak, Peter N. Robinson, Christopher J. Mungall, Emanuele Cavalleri, Tommaso Fontana, Giorgio Valentini, Marco Mesiti, Lucas A. Gillenwater, Brook Santangelo, Nicole A. Vasilevsky, Robert Hoehndorf, Tellen D. Bennett, Patrick B. Ryan, George Hripcsak, Michael G. Kahn, Michael Bada, William A. Baumgartner Jr, Lawrence E. Hunter
Translational research requires data at multiple scales of biological organization.
1 code implementation • 24 Sep 2022 • Sanya B. Taneja, Tiffany J. Callahan, Mary F. Paine, Sandra L. Kane-Gill, Halil Kilicoglu, Marcin P. Joachimiak, Richard D. Boyce
NP-KG is a heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature, constructed with the Phenotype Knowledge Translator framework and the semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler.
1 code implementation • 10 Sep 2022 • Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa, Chenjie Zeng, Anna Ostropolets, Juan M. Banda, William A. Baumgartner Jr., Richard D. Boyce, Elena Casiraghi, Ben D. Coleman, Janine H. Collins, Sara J. Deakyne-Davies, James A. Feinstein, Melissa A. Haendel, Asiyah Y. Lin, Blake Martin, Nicolas A. Matentzoglu, Daniella Meeker, Justin Reese, Jessica Sinclair, Sanya B. Taneja, Katy E. Trinkley, Nicole A. Vasilevsky, Andrew Williams, Xingman A. Zhang, Joshua C. Denny, Peter N. Robinson, Patrick Ryan, George Hripcsak, Tellen D. Bennett, Lawrence E. Hunter, Michael G. Kahn
Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping.
no code implementations • 28 Jul 2022 • Tiffany J. Callahan, Adrianne L. Stefanski, Jin-Dong Kim, William A. Baumgartner Jr., Jordan M. Wyrwa, Lawrence E. Hunter
Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease.
no code implementations • 13 Jun 2022 • Elena Casiraghi, Rachel Wong, Margaret Hall, Ben Coleman, Marco Notaro, Michael D. Evans, Jena S. Tronieri, Hannah Blau, Bryan Laraway, Tiffany J. Callahan, Lauren E. Chan, Carolyn T. Bramante, John B. Buse, Richard A. Moffitt, Til Sturmer, Steven G. Johnson, Yu Raymond Shao, Justin Reese, Peter N. Robinson, Alberto Paccanaro, Giorgio Valentini, Jared D. Huling, Kenneth Wilkins, :, Tell Bennet, Christopher Chute, Peter DeWitt, Kenneth Gersing, Andrew Girvin, Melissa Haendel, Jeremy Harper, Janos Hajagos, Stephanie Hong, Emily Pfaff, Jane Reusch, Corneliu Antoniescu, Kimberly Robaski
In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques.
2 code implementations • 12 Oct 2021 • Luca Cappelletti, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr, Tiffany J. Callahan, Carlos Cano, Marcin P. Joachimiak, Christopher J. Mungall, Peter N. Robinson, Justin Reese, Giorgio Valentini
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs.
1 code implementation • 27 Jul 2021 • Luis Alberto Robles Hernandez, Tiffany J. Callahan, Juan M. Banda
As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes.
no code implementations • 8 Oct 2019 • Tiffany J. Callahan, Harrison Pielke-Lombardo, Ignacio J. Tripodi, Lawrence E. Hunter
Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine.