Defining a Decade Envisioning CSTB's Second 10 Years (1997) / Chapter Skim
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7 COMPUTATIONAL BIOLOGY AND THE CROSS-DISCIPLINARY CHALLENGES
7 COMPUTATIONAL BIOLOGY AND THE CROSS-DISCIPLINARY CHALLENGES
Pages 53-62
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From page 53... ...
Computer scientists are fairly familiar with the problem of predicting the secondary and tertiary structure of biological molecules, and applications in drug design. The artificial intelligence community had done some nice work using neural nets and hidden Markov models for finding genetic indicators and genes within genome sequencing data.
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From page 54... ...
Many of the database and visualization tools developed outside of biology are now being imported into biological research tools. Consider the work I discussed earlier on the construction of phylogenetic trees from molecular sequence data.
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From page 55... ...
However, I am going to broaden my charge to discuss issues related to how people who are trained in these intersecting fields find professional outlets in the world beyond, whether in academia, industrial research and development, or other settings where leaders increasingly are recognizing the need for people who have insights and experience that cross disciplines. Much of what I am going to say today harkens back to the CSTB report Academic Careers for Experimental Computer Scientists and Engineers, which was completed in 1994 (Larry Snyder from the University of Washington chaired
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From page 56... ...
The predoctoral candidates tend to be either medical students who are getting joint degrees or students straight out of undergraduate school. The latter group does not necessarily want to get a medical degree or another health science degree but does want to make biomedical informatics its professional career commitment.
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From page 57... ...
Similarly, the same project often needs to be explained in a medical grand rounds, a pediatric seminar, or a molecular biology research seminar. When the same student goes into that setting, he or she needs to be able to play down the computer science, which the typical person in the audience will not understand, and focus instead on what is exciting and challenging about the biology being done in those projects.
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From page 58... ...
Among these are courses in computational biology, an introduction to medical applications of computing, medical decision support systems, biomedical imaging, and a project course in which students build small systems under the guidance of medical informatics faculty. Our informatics courses are cross-listed in the computer science department, and many computer science and engineer FIGURE 7.2 Areas of knowledge required in the held of biomedical informatics.
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From page 59... ...
The crucial role of an advanced computing environment is probably obvious to anyone in computer science, but this point is not always obvious to deans of medical schools. It is difficult to provide high-quality training in medical informatics unless you have a computing environment that is just as advanced as one expects to find in a typical engineering school department.
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From page 60... ...
They may not be working at the theoretical level that would more easily validate their role as traditional computer scientists in an academic setting. The same picture applies to those doing medical informatics (Figure 7.3, bottoms.
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From page 61... ...
Frankly, it is impossible now to do modern molecular biology research without local expertise in computing, especially for DNA homology searching and analysis of some of the protein databases and the like. Molecular biologists can no longer do effective research unless they have somebody in their midst who can help them understand and use the newer technologies.
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From page 62... ...
There is a difference between learning a lot about medicine and then learning a little about computing, or learning a lot about computing and then a little about medicine, and focusing your entire graduate training at the intersection itself. I believe there is also a significant difference between formal informatics training and having someone first finish a medical degree and then earn a computer science degree in a conventional computer science environment; the connections to medicine are not part and parcel of the way computer science is taught, and understanding the relationships and relevance is thus inherently left as an exercise to the student.
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