Methods in molecular biology (Clifton, N.J.)
38468097
We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and provide additional tools to guide scientific discovery. There are two different kinds of objectives ...
In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to ...
Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a long history, starting in 1932 with R.A. Fisher and including more re...
Methods in molecular biology (Clifton, N.J.)
38441759
Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models ...
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been publishe...
INTRODUCTION: Social robots including telepresence robots have emerged as potential support in dementia care. However, the effectiveness of these robots hinges significantly on their design and utility. These elements are often best understood by the...
Journal of chemical information and modeling
38456446
Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from t...
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of ...
Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and ...
Large language models (LLMs) may facilitate and expedite systematic reviews, although the approach to integrate LLMs in the review process is unclear. This study evaluates GPT-4 agreement with human reviewers in assessing the risk of bias using the R...