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%.
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.
Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction.
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.
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.
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.
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.
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.
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
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.
Unstructured clinical text in EHRs contains crucial information for applications including decision support, trial matching, and retrospective research.
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task.
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.
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.
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Ranked #2 on Drug Discovery on KIBA
Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data.
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
Clinical notes contain information about patients that goes beyond structured data like lab values and medications.