TY - JOUR
T1 - The evolution of computational research in a data-centric world
AU - Deshpande, Dhrithi
AU - Chhugani, Karishma
AU - Ramesh, Tejasvene
AU - Pellegrini, Matteo
AU - Shifman, Sagiv
AU - Abedalthagafi, Malak S.
AU - Alqahtani, Saleh
AU - Ye, Jimmie
AU - Liu, Xiaole Shirley
AU - Leek, Jeffrey T.
AU - Brazma, Alvis
AU - Ophoff, Roel A.
AU - Rao, Gauri
AU - Butte, Atul J.
AU - Moore, Jason H.
AU - Katritch, Vsevolod
AU - Mangul, Serghei
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/8/22
Y1 - 2024/8/22
N2 - Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data. We are now able to generate vast amounts of data, and the challenge has shifted from data generation to data analysis. Here we discuss the pitfalls, challenges, and opportunities facing the field of data-centric research in biology. We discuss the evolving perception of computational data-driven research and its rise as an independent domain in biomedical research while also addressing the significant collaborative opportunities that arise from integrating computational research with experimental and translational biology. Additionally, we discuss the future of data-centric research and its applications across various areas of the biomedical field.
AB - Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data. We are now able to generate vast amounts of data, and the challenge has shifted from data generation to data analysis. Here we discuss the pitfalls, challenges, and opportunities facing the field of data-centric research in biology. We discuss the evolving perception of computational data-driven research and its rise as an independent domain in biomedical research while also addressing the significant collaborative opportunities that arise from integrating computational research with experimental and translational biology. Additionally, we discuss the future of data-centric research and its applications across various areas of the biomedical field.
UR - http://www.scopus.com/inward/record.url?scp=85202267112&partnerID=8YFLogxK
U2 - 10.1016/j.cell.2024.07.045
DO - 10.1016/j.cell.2024.07.045
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C2 - 39178828
AN - SCOPUS:85202267112
SN - 0092-8674
VL - 187
SP - 4449
EP - 4457
JO - Cell
JF - Cell
IS - 17
ER -