no code implementations • 13 Sep 2023 • Yeachan Kim, Bonggun Shin
In this work, we carefully analyze the existing methods in heterogeneous environments.
no code implementations • 20 Feb 2023 • Daeseok Lee, Jeunghyun Byun, Bonggun Shin
Since it is not always easy to find such binding sites based on domain knowledge or traditional methods, different deep learning methods that predict binding sites out of protein structures have been developed in recent years.
no code implementations • 12 Jan 2023 • Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin
Comprehensive results show that PhaseAT significantly improves the convergence for high-frequency information.
no code implementations • 14 Dec 2022 • Dongpin Oh, Dae Lee, Jeunghyun Byun, Bonggun Shin
In the END framework, we first train the \textit{identification model} to obtain the SCF samples from a training set using its predictive uncertainty.
no code implementations • 10 Jun 2022 • Yeachan Kim, Bonggun Shin
The strategy is to estimate the density of the unlabeled samples and select diverse samples mainly from sparse regions.
1 code implementation • 17 Dec 2021 • Dongpin Oh, Bonggun Shin
In the MTL, we define the Lipschitz modified mean squared error (MSE) loss function as another loss and add it to the existing NLL loss.
no code implementations • 17 Sep 2021 • Yeachan Kim, Bonggun Shin
In silico prediction of drug-target interactions (DTI) is significant for drug discovery because it can largely reduce timelines and costs in the drug development process.
1 code implementation • 26 Oct 2020 • Bonggun Shin, Sungsoo Park, JinYeong Bak, Joyce C. Ho
Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process.
no code implementations • 15 Aug 2019 • Bonggun Shin, Sungsoo Park, Keunsoo Kang, Joyce C. Ho
Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost.
no code implementations • 31 May 2019 • Bonggun Shin, Julien Hogan, Andrew B. Adams, Raymond J. Lynch, Rachel E. Patzer, Jinho D. Choi
One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature.
1 code implementation • 31 May 2019 • Bonggun Shin, Hao Yang, Jinho D. Choi
Recent advances in deep learning have facilitated the demand of neural models for real applications.
Ranked #2 on Sentiment Analysis on MPQA
no code implementations • 22 Aug 2017 • Bonggun Shin, Falgun H. Chokshi, Timothy Lee, Jinho D. Choi
The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value.
no code implementations • WS 2017 • Bonggun Shin, Timothy Lee, Jinho D. Choi
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting.