Payal Chandak
Columbia University
9 Papers
Payal Chandak is an academic researcher from Columbia University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 1, co-authored 1 publications.
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Papers
Scientific discovery in the age of artificial intelligence
Hanchen Wang,Tianfan Fu,Yuanqi Du,Wenhao Gao,Kexin Huang,Ziming Liu,Payal Chandak,Shengchao Liu,Peter Van Katwyk,A Deac,Animashree Anandkumar,Karianne J. Bergen,Carla Gomes,Shirley Ho,Pushmeet Kohli,L. Lasenby,Jure Leskovec,Tie-Yan Liu,Arjun K. Manrai,Debora Marks,Bharath Ramsundar,Le Song,Jimeng Sun,Jian Tang,Petar Veličković,Max Welling,Linfeng Zhang,Connor W. Coley,Yoshua Bengio,Marinka Zitnik +29 more
TL;DR: This work examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deeplearning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
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Building a knowledge graph to enable precision medicine
TL;DR: For example, PrimeKG as mentioned in this paper integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs.
Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women.
Payal Chandak,Nicholas P. Tatonetti +1 more
- 22 Sep 2020
TL;DR: A pharmacovigilance algorithm that leverages advances in machine learning to predict sex risks and mitigates biases and quantifies the differential risk of a drug causing an adverse event in either men or women is presented.
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Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
TL;DR: This paper presents how the extended nested model motivates and informs the design of DrugExplorer, an XAI tool for drug repurposing, and offers a novel visualization design called MetaMatrix with a set of interactions to help domain users organize and compare explanation paths at different levels of granularity to generate domain-meaningful insights.
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Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Kaimei Huang,Payal Chandak,Q. Wang,Shreya Havaldar,Akhil Vaid,Jure Leskovec,G. Nadkarni,Benjamin S. Glicksberg,Nils Gehlenborg,Marinka Zitnik +9 more
TL;DR: XGNN as mentioned in this paper is a graph neural network pre-trained on a comprehensive knowledge graph of 17,080 clinically-recognized diseases and 7,957 therapeutic candidates, which can process various therapeutic tasks, such as indication and contraindication prediction, in a unified formulation.