DBT seeks AI researchers’ interest to develop applications for affordable healthcare

New Delhi: The Department of Biotechnology (DBT) has announced a fresh call to researchers under the new initiative “Artificial Intelligence Applications for Affordable and Accessible Healthcare – Big Data and Genomics”.

The objective behind this call is to seek pre-proposals from the team of researchers who intend to develop AI-based-solutions or tools to unlock clinically relevant information hidden in the massive amount of data, guided by relevant clinical questions. The same in turn should assist clinical decision making on disease prediction and prognosis evaluation, early detection and diagnosis, therapy choice, patient monitoring, and care, etc.

This call for proposals is open to all applicants normally eligible for DBT funding. The team of researchers should be multi-disciplinary with expertise in the areas: Data Science (for example, capability to deal with a large amount of data, having expertise in building prediction tools involving computational biology, statistics, machine learning, deep learning, etc.), High-throughput Omics and Clinical expertise (for example, biologists, clinician, specialist in a specific disease in question).

Researchers working in the industry are not eligible to apply alone but can apply with academic partners. The proposals will be considered for funding for 2 to 5 years. If the existing data is to be used, the proposal may be formulated for a short duration.

Pre-Proposals may be submitted in the prescribed Format, clearly stating ‘Pre-Proposal on Artificial Intelligence Applications for Affordable and Accessible Healthcare – Big Data and Genomics’ in the subject line of the email and sent to Dr Suchita Ninawe, Adviser, DBT at the email ID, bioinformatics@dbt.nic.in. The closing date of the call is 15th May 2019. Subsequently, two hard copies should also be sent by 20th May 2019. Details can be found here.

Under this call, DBT has encouraged the usage of existing bioresources, biobanks, databanks or datasets in these proposals.

The pre-proposals may be focused on AI programs to be developed and applied to practices such as diagnosis processes; treatment protocol development; drug creation/development; personalized medicine; patient follow up; etc for priority diseases such as Cancer, Tuberculosis, Cardiomyopathy, Diabetic Retinopathy, Genetic Diseases, and Rare Disorders.

Integration of Artificial intelligence approaches such as machine learning, deep learning, and natural language processing to tackle the challenges of scalability and high dimensionality of data, a better understanding of genomics and find patterns in the data that makes sense and to transform big data into clinically actionable knowledge is expanding.