Search Results for author:
Found 6 papers, 2 papers with code
Progress and Opportunities of Foundation Models in Bioinformatics
Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs.
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines.
A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data
We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data.
E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction
When E2Efold-3D is coupled with the experimental techniques, the RNA structure prediction field can be greatly advanced.
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations.
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward.
