Updated Focus of NIGMS-Supported Predoctoral Training in Computational Biology, Bioinformatics, and Biomedical Data Science


Modern biomedical research is becoming increasingly quantitative and reliant on computational methods, with growing use of large and complex datasets to address biomedical research questions and advance human health. To help address the need for biomedical researchers with cutting-edge computational and quantitative skills, we have updated the focus areas of our Predoctoral T32 Training Program in Computational Biology, Bioinformatics, and Biomedical Data Science (formerly called Bioinformatics and Computational Biology). In doing this, we aim to better integrate training in data-science approaches throughout the curriculum and during the mentored research period. We are now placing a strong emphasis on programs that:

  • Focus on new and emerging areas of data science, including machine learning, deep learning, and artificial intelligence.
  • Integrate training in biological sciences and quantitative and computational sciences (e.g., data science, computer science, statistics, mathematics, informatics, engineering).
  • Provide multidisciplinary training to students in the fundamentals and applications of computational and information sciences.
  • Include training in fair and ethical data use, data sharing, and data security and confidentiality.
  • Take advantage of the resources and expertise available in the private sector to develop student skills such as the ability to write efficient, industry-standard computer code and the use of emerging technologies and platforms.
  • Help develop career pathways for trainees, including by forming internship/training partnerships with industry and other sectors.

These changes will be effective with the January 25, 2019, application receipt date.

For more information about the changes to the focus areas for this training program, please see the NIH Guide Notice, and the NIGMS Predoctoral T32 Training Program website. As usual, we welcome your comments and suggestions.

4 Replies to “Updated Focus of NIGMS-Supported Predoctoral Training in Computational Biology, Bioinformatics, and Biomedical Data Science”

  1. Do you happen to know about postdoctoral training grants or fellowships that could enable a person with little training in comp. bio/bioinformatics to pursue research on these quantitative subjects? Or any advice?

    1. Individuals working with or wishing to work with postdoctoral mentors in biomedical quantitative research such as computational biology or bioinformatics can apply for the individual F32 NRSA fellowship (PA-18-670) or the K99/R00 Pathway to Independence Award (PA-18-398). PIs of NIH research grants can support postdocs in their labs by requesting administrative supplements to promote diversity (PA-18-906) or re-entry into research careers (PA-18-592). The spectrum of NIGMS programs supporting postdoctoral training can be found at https://www.nigms.nih.gov/research-training/research-training-grant-programs/postdoctoral-grant-programs.

  2. There is a tendency to jump into ‘hot’ areas -it’s like wanting to be friends with the popular kids in high school. This initiative is likely a bad idea -there’s already an emphasis on data science and enormous interest in it, and I’m sure it will deliver a lot. But how it will deliver is yet to be worked out, and it’s ability to deliver will be tempered by limitations in the understanding of biology, biochemistry, and biophysics of its practitioners. And these fundamental and foundational areas are receiving less and less emphasis and interest. I’m not a Luddite, but maybe this is a time for NIGMS to redouble its focus on the fundamentals, and let creative and smart scientists impact data science and other areas from that foundation.

  3. That is really some wonderful information for people who are planning to enter this field. Before choosing any field, it is quite necessary that you have good knowledge about it. Along with that one should know all the consequences and possibilities. I would like to see more on this. Thanks.

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