DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology
Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME Registry (URL: registry.dome-ml.org), a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, promoting transparency and reproducibility of ML in the life sciences.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Experiences with the ISOcat Data Category Registry
The ISOcat Data Category Registry has been a joint project of both ISO TC 37 and the European CLARIN infrastructure. In this paper the experiences of using ISOcat in CLARIN are described and evaluated. This evaluation cl…
DOME: Recommendations for supervised machine learning validation in biology
Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we…
BIG-bench Machine LearningOn the Ethical Limits of Natural Language Processing on Legal Text
Natural language processing (NLP) methods for analyzing legal text offer legal scholars and practitioners a range of tools allowing to empirically analyze law on a large scale. However, researchers seem to struggle when …
DiversityDesigning and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials
Personalized adaptive interventions offer the opportunity to increase patient benefits, however, there are challenges in their planning and implementation. Once implemented, it is an important question whether personaliz…
reinforcement-learningReinforcement LearningRELcat: a Relation Registry for ISOcat data categories
The ISOcat Data Category Registry contains basically a flat and easily extensible list of data category specifications. To foster reuse and standardization only very shallow relationships among data categories are stored…
Relation