It can be viewed as the study and application of data science to solve biomedical problems.
Examples of biomedical data science research include: The National Library of Medicine of the US National Institutes of Health (NIH) identified key biomedical data scientist attributes in an NIH-wide review: general biomedical subject matter knowledge; programming language expertise; predictive analytics, modeling, and machine learning; team science and communication; and responsible data stewardship.
Significant computational resources were required to process the data in the HGP, as the human genome contains over 6 billion DNA base pairs.
[13] Scientists constructed the genome by piecing together small fragments of DNA, and computing overlaps between these sequences alone required over 10,000 CPU hours.
[15] The HGP, completed in 2004, has had immense impact both biologically, shedding light on human evolution, and medically, launching the field of bioinformatics and leading to technologies such as genetic screening and gene therapy.