Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction
Authors: Bo Jin, Haoyu Yang, Cao Xiao, Ping Zhang, Xiaopeng Wei, Fei Wang
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,278 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,222 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluation on real world datasets is presented to demonstrate the effectiveness of the proposed approach. |
| Researcher Affiliation | Collaboration | Computer Science, Dalian University of Technology, Dalian, China 116024 *Health Analytics Research Group, IBM T.J.Watson Research Center, Yorktown Heights, NY 10598 Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, NY 10065 |
| Pseudocode | Yes | Algorithm 1 Alternating PGD algorithm; Algorithm 2 Accelerated PGD for O with fixed P and Q |
| Open Source Code | No | The paper does not provide a concrete access link or explicit statement about the availability of the source code for the methodology. |
| Open Datasets | Yes | The DDI data we used in evaluation is extracted from FDA Adverse Event Reporting System (FAERS)1. [...] Mined from FAERS, the Twosides database2 (Tatonetti et al. 2012) is a resource of polypharmacy side effects for pairs of drugs. [...] 1https://open.fda.gov/data/faers/; 2http://tatonettilab.org/resources/tatonetti-stm.html |
| Dataset Splits | Yes | To be specific, we randomly selected a fixed percentage (10%) of drugs for testing, and considered all DDIs associated with these drugs as testing set. Then we constructed the models with the remaining DDIs as the training set. The model parameters were tuned with cross validation based on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details, such as hyperparameter values or training configurations. |