Search Results for author:
Found 14 papers, 6 papers with code
Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design
To address this limitation, we propose a novel approach called Gradient Genetic Algorithm (Gradient GA), which incorporates gradient information from the objective function into genetic algorithms.
Political-LLM: Large Language Models in Political Science
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection.
Protein-Mamba: Biological Mamba Models for Protein Function Prediction
Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics.
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy.
BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba
BioMamba builds upon the Mamba architecture and is pre-trained on an extensive corpus of biomedical literature.
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments.
DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing
Bringing a novel drug from the original idea to market typically requires more than ten years and billions of dollars.
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets
Clinical trials are pivotal for developing new medical treatments, yet they typically pose some risks such as patient mortality, adverse events, and enrollment failure that waste immense efforts spanning over a decade.
Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?
Currently, the field of structure-based drug design is dominated by three main types of algorithms: search-based algorithms, deep generative models, and reinforcement learning.
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Clinical trials are indispensable for medical research and the development of new treatments.
Uncertainty Quantification on Clinical Trial Outcome Prediction
Selective classification, encompassing a spectrum of methods for uncertainty quantification, empowers the model to withhold decision-making in the face of samples marked by ambiguity or low confidence, thereby amplifying the accuracy of predictions for the instances it chooses to classify.
GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualization
With GenoCraft, researchers and data scientists have access to an array of cutting-edge bioinformatics tools under a user-friendly interface, making it a valuable resource for managing and analyzing large-scale omics data.
Machine Learning for Synthetic Data Generation: A Review
In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate.
Deep Learning based Multi-Label Image Classification of Protest Activities
With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication.
