no code implementations • 12 Nov 2023 • Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin
To efficiently evaluate new models on the benchmark, we develop a specified scorer capable of scoring LLMs across multiple dimensions, achieving an accuracy of 77. 4%.
no code implementations • 10 Nov 2023 • Yixu Wang, Yan Teng, Kexin Huang, Chengqi Lyu, Songyang Zhang, Wenwei Zhang, Xingjun Ma, Yu-Gang Jiang, Yu Qiao, Yingchun Wang
To address this, we introduce the Fake alIgNment Evaluation (FINE) framework and two novel metrics--Consistency Score (CS) and Consistent Safety Score (CSS), which jointly assess two complementary forms of evaluation to quantify fake alignment and obtain corrected performance estimates.
1 code implementation • 10 Jul 2023 • Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang, Xiangnan He
Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction.
no code implementations • 7 Jun 2023 • Camilo Ruiz, Hongyu Ren, Kexin Huang, Jure Leskovec
However, for tabular datasets with extremely high $d$-dimensional features but limited $n$ samples (i. e. $d \gg n$), machine learning models struggle to achieve strong performance due to the risk of overfitting.
no code implementations • 7 Dec 2022 • Yukun Cao, Xike Xie, Kexin Huang
The process of data exploration can be viewed as the process of training a classifier, which determines whether a database tuple is interesting to a user.
no code implementations • 26 Oct 2022 • Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang, Karthik Subbian, Kenji Kawaguchi, Jure Leskovec
Extensive evaluation of TuneUp on five diverse GNN architectures, three types of prediction tasks, and both transductive and inductive settings shows that TuneUp significantly improves the performance of the base GNN on tail nodes, while often even improving the performance on head nodes.
no code implementations • NeurIPS Workshop AI4Scien 2021 • Kexin Huang, Vishnu Sresht, Brajesh Rai, Mykola Bordyuh
Machine learning models have recently shown promise in predicting molecular quantum chemical properties.
no code implementations • 3 May 2021 • Kexin Huang, Cao Xiao, Lucas M. Glass, Cathy W. Critchlow, Greg Gibson, Jimeng Sun
Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks.
no code implementations • 11 Apr 2021 • Michelle M. Li, Kexin Huang, Marinka Zitnik
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge.
2 code implementations • 18 Feb 2021 • Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
1 code implementation • 8 Feb 2021 • Tianfan Fu, Kexin Huang, Cao Xiao, Lucas M. Glass, Jimeng Sun
Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web.
no code implementations • 12 Nov 2020 • Kexin Huang, Sankeerth Garapati, Alexander S. Rich
Unstructured clinical text in EHRs contains crucial information for applications including decision support, trial matching, and retrospective research.
1 code implementation • 5 Oct 2020 • Kexin Huang, Tianfan Fu, Dawood Khan, Ali Abid, Ali Abdalla, Abubakar Abid, Lucas M. Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
1 code implementation • 4 Oct 2020 • Yue Yu, Kexin Huang, Chao Zhang, Lucas M. Glass, Jimeng Sun, Cao Xiao
Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task.
1 code implementation • NeurIPS 2020 • Kexin Huang, Marinka Zitnik
G-Meta learns how to quickly adapt to a new task using only a handful of nodes or edges in the new task and does so by learning from data points in other graphs or related, albeit disjoint label sets.
1 code implementation • 30 Apr 2020 • Kexin Huang, Cao Xiao, Lucas Glass, Marinka Zitnik, Jimeng Sun
Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions.
1 code implementation • 23 Apr 2020 • Kexin Huang, Cao Xiao, Lucas Glass, Jimeng Sun
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
2 code implementations • 19 Apr 2020 • Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Ranked #2 on
Drug Discovery
on KIBA
3 code implementations • EMNLP (ClinicalNLP) 2020 • Kexin Huang, Abhishek Singh, Sitong Chen, Edward T. Moseley, Chih-ying Deng, Naomi George, Charlotta Lindvall
Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data.
2 code implementations • 15 Nov 2019 • Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas M. Glass, Jimeng Sun
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
2 code implementations • 10 Apr 2019 • Kexin Huang, Jaan Altosaar, Rajesh Ranganath
Clinical notes contain information about patients that goes beyond structured data like lab values and medications.
no code implementations • 24 Sep 2018 • Kexin Huang, Rodrigo Nogueira
Epistasis (gene-gene interaction) is crucial to predicting genetic disease.