Machine learning for synthetic data generation: a review
Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …
data-related issues. These include data of poor quality, insufficient data points leading to …
Machine learning-aided generative molecular design
Abstract Machine learning has provided a means to accelerate early-stage drug discovery
by combining molecule generation and filtering steps in a single architecture that leverages …
by combining molecule generation and filtering steps in a single architecture that leverages …
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 …
Semignn-ppi: Self-ensembling multi-graph neural network for efficient and generalizable protein-protein interaction prediction
Protein-protein interactions (PPIs) are crucial in various biological processes and their study
has significant implications for drug development and disease diagnosis. Existing deep …
has significant implications for drug development and disease diagnosis. Existing deep …
Uncertainty quantification on clinical trial outcome prediction
The importance of uncertainty quantification is increasingly recognized in the diverse field of
machine learning. Accurately assessing model prediction uncertainty can help provide …
machine learning. Accurately assessing model prediction uncertainty can help provide …
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 …
Uncertainty quantification and interpretability for clinical trial approval prediction
Background: Clinical trial is a crucial step in the development of a new therapy (eg,
medication) and is remarkably expensive and time-consuming. Forecasting the approval of …
medication) and is remarkably expensive and time-consuming. Forecasting the approval of …
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 …
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …