DOME Registry: implementing community-wide recommendations for
reporting supervised machine learning in biology

J 2024

DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology

ATTAFI, Omar Abdelghani; Damiano CLEMENTEL; Konstantinos KYRITSIS; Emidio CAPRIOTTI; Gavin FARRELL et al.

Základní údaje

Originální název

DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology

Autoři

Vydání

GigaScience, Oxford University Press, 2024, 2047-217X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10700 1.7 Other natural sciences

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.900

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/24:00138456

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

machine learning; standards; transparency; reproducibility

Štítky

143180, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 2. 2025 16:05, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Data Optimization Model Evaluation (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, and promoting transparency and reproducibility of ML in the life sciences.

Citovat

ATTAFI, Omar Abdelghani; Damiano CLEMENTEL; Konstantinos KYRITSIS; Emidio CAPRIOTTI; Gavin FARRELL; Styliani-Christina FRAGKOULI; Leyla Jael CASTRO; Andras HATOS; Tom LENAERTS; Stanislav MAZURENKO; Soroush MOZAFFARI; Franco PRADELLI; Patrick RUCH; Castrense SAVOJARDO; Paola TURINA; Federico ZAMBELLI; Damiano PIOVESAN; Alexander Miguel MONZON; Fotis PSOMOPOULOS a Silvio C. E. TOSATTO. DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology. GigaScience. Oxford University Press, 2024, roč. 13, December 2024, s. 1-8. ISSN 2047-217X. Dostupné z: https://doi.org/10.1093/gigascience/giae094.

@article{2467859,
   author = {Attafi, Omar Abdelghani and Clementel, Damiano and Kyritsis, Konstantinos and Capriotti, Emidio and Farrell, Gavin and Fragkouli, StylianiandChristina and Castro, Leyla Jael and Hatos, Andras and Lenaerts, Tom and Mazurenko, Stanislav and Mozaffari, Soroush and Pradelli, Franco and Ruch, Patrick and Savojardo, Castrense and Turina, Paola and Zambelli, Federico and Piovesan, Damiano and Monzon, Alexander Miguel and Psomopoulos, Fotis and Tosatto, Silvio C. E.},
   article_number = {December 2024},
   doi = {https://doi.org/10.1093/gigascience/giae094},
   keywords = {machine learning; standards; transparency; reproducibility},
   language = {eng},
   issn = {2047-217X},
   journal = {GigaScience},
   title = {DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology},
   url = {https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae094/7921169?login=true},
   volume = {13},
   year = {2024}
}

TY  - JOUR
ID  - 2467859
AU  - Attafi, Omar Abdelghani - Clementel, Damiano - Kyritsis, Konstantinos - Capriotti, Emidio - Farrell, Gavin - Fragkouli, Styliani-Christina - Castro, Leyla Jael - Hatos, Andras - Lenaerts, Tom - Mazurenko, Stanislav - Mozaffari, Soroush - Pradelli, Franco - Ruch, Patrick - Savojardo, Castrense - Turina, Paola - Zambelli, Federico - Piovesan, Damiano - Monzon, Alexander Miguel - Psomopoulos, Fotis - Tosatto, Silvio C. E.
PY  - 2024
TI  - DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology
JF  - GigaScience
VL  - 13
IS  - December 2024
SP  - 1-8
EP  - 1-8
PB  - Oxford University Press
SN  - 2047217X
KW  - machine learning
KW  - standards
KW  - transparency
KW  - reproducibility
UR  - https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae094/7921169?login=true
N2  - Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Data Optimization Model Evaluation (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, and promoting transparency and reproducibility of ML in the life sciences.
ER  -

ATTAFI, Omar Abdelghani; Damiano CLEMENTEL; Konstantinos KYRITSIS; Emidio CAPRIOTTI; Gavin FARRELL; Styliani-Christina FRAGKOULI; Leyla Jael CASTRO; Andras HATOS; Tom LENAERTS; Stanislav MAZURENKO; Soroush MOZAFFARI; Franco PRADELLI; Patrick RUCH; Castrense SAVOJARDO; Paola TURINA; Federico ZAMBELLI; Damiano PIOVESAN; Alexander Miguel MONZON; Fotis PSOMOPOULOS a Silvio C. E. TOSATTO. DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology. \textit{GigaScience}. Oxford University Press, 2024, roč.~13, December 2024, s.~1-8. ISSN~2047-217X. Dostupné z: https://doi.org/10.1093/gigascience/giae094.

Přehled o publikaci