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 …
some risks such as patient mortality, adverse events, and enrollment failure that waste …
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Clinical trials are indispensable for medical research and the development of new
treatments. However, clinical trials often involve thousands of participants and can span …
treatments. However, clinical trials often involve thousands of participants and can span …
Drugclip: Contrastive drug-disease interaction for drug repurposing
Y Lu, Y Hu, C Li - arXiv preprint arXiv:2407.02265, 2024 - arxiv.org
Bringing a novel drug from the original idea to market typically requires more than ten years
and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the approved …
and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the approved …
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 …
(ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy …
TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the
statistical power of the treatment (eg, a new drug) in curing a certain disease. Clinical trial …
statistical power of the treatment (eg, a new drug) in curing a certain disease. Clinical trial …
BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba
The advancement of natural language processing (NLP) in biology hinges on models' ability
to interpret intricate biomedical literature. Traditional models often struggle with the complex …
to interpret intricate biomedical literature. Traditional models often struggle with the complex …
Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study
This study explores the integration of real-world machine learning (ML) projects using
human-computer interfaces (HCI) datasets in college-level courses to enhance both …
human-computer interfaces (HCI) datasets in college-level courses to enhance both …
CT-Agent: Clinical Trial Multi-Agent with Large Language Model-based Reasoning
Large Language Models (LLMs) and multi-agent systems have shown impressive
capabilities in natural language tasks but face challenges in clinical trial applications …
capabilities in natural language tasks but face challenges in clinical trial applications …
Quantum-machine-assisted Drug Discovery: Survey and Perspective
Drug discovery and development is a highly complex and costly endeavor, typically
requiring over a decade and substantial financial investment to bring a new drug to market …
requiring over a decade and substantial financial investment to bring a new drug to market …
DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning
Drug repurposing offers a promising avenue for accelerating drug development by
identifying new therapeutic potentials of existing drugs. In this paper, we propose a multi …
identifying new therapeutic potentials of existing drugs. In this paper, we propose a multi …