Search Results for author:
Found 6 papers, 2 papers with code
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI).
Ranked #2 on
Drug Discovery
on BindingDB IC50
Network-principled deep generative models for designing drug combinations as graph sets
We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer.
Illegible Text to Readable Text: An Image-to-Image Transformation using Conditional Sliced Wasserstein Adversarial Networks
We tackle this problem by developing a handwritten-to-machine-print conditional Generative Adversarial network (HW2MP-GAN) model that formulates handwritten recognition as a text-Image-to-text-Image translation problem where a given image, typically in an illegible form, is converted into another image, close to its machine-print form.
Generative Adversarial Network
Image-to-Image Translation
+1
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
DeepRelations shows superior interpretability to the state-of-the-art: without compromising affinity prediction, it boosts the AUPRC of contact prediction 9. 5, 16. 9, 19. 3 and 5. 7-fold for the test, compound-unique, protein-unique, and both-unique sets, respectively.
Directionally Dependent Multi-View Clustering Using Copula Model
Motivated by this, we propose a copula-based multi-view clustering model where a copula enables the model to accommodate the directional dependence existing in the datasets.
Deploying self-supervised learning in the wild for hybrid automatic speech recognition
Self-supervised learning (SSL) methods have proven to be very successful in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
