Analysis, biomedicine, collaboration, and determinism challenges and guidance: wish list for biopharmaceuticals on the interface of computing and statistics.
I have personally witnessed processing advance from desk calculators and mainframes, through timesharing and PCs, to supercomputers and cloud computing. I have also witnessed resources grow from too little data into almost too much data, and from theory dominating data into data beginning to dominate theory while needing new theory. Finally, I have witnessed problems advance from simple in a lone discipline into becoming almost too complex in multiple disciplines, as well as approaches evolve from analysis driving solutions into solutions by data mining beginning to drive the analysis itself. How we do all of this has transitioned from competition overcoming collaboration into collaboration starting to overcome competition, as well as what is done being more important than how it is done has transitioned into how it is done becoming as important as what is done. In addition, what or how we do it being more important than what or how we should actually do it has shifted into what or how we should do it becoming just as important as what or how we do it, if not more so. Although we have come a long way in both our methodology and technology, are they sufficient for our current or future complex and multidisciplinary problems with their massive databases? Since the apparent answer is not a resounding yes, we are presented with tremendous challenges and opportunities. This personal perspective adapts my background and experience to be appropriate for biopharmaceuticals. In these times of exploding change, informed perspectives on what challenges should be explored with accompanying guidance may be even more valuable than the far more typical literature reviews in conferences and journals of what has already been accomplished without challenges or guidance. Would we believe that an architect who designs a skyscraper determines the skyscraper's exact exterior, interior and furnishings or only general characteristics? Why not increase dependability of conclusions in genetics and translational medicine by enriching genetic determinism with uncertainty? Uncertainty is our friend if exploited or potential enemy if ignored. Genes design proteins, but they cannot operationally determine all protein characteristics: they begin a long chain of complex events occurring many times via intricate feedbacks plus interactions which are not all determined. Genes influence proteins and diseases by just determining their probability distributions, not by determining them. From any sample of diseased people, we may more successfully infer gene probability distributions than genes themselves, and it poses an issue to resolve. My position is supported by 2-3 articles a week in ScienceDaily, 2011.
Collaborative Data Solutions, Villa Park, California 92861-1227, USA. datagoodman@att.net
Arnold F
AF
Goodman
D000818:Animals; D020811:Biomedical Technology / Q000379:methods / Q000639:trends; D001702:Biopharmaceutics / Q000379:methods / Q000639:trends*; D019295:Computational Biology / Q000379:methods / Q000639:trends*; D003201:Computers / Q000639:trends; D003299:Cooperative Behavior; D006801:Humans; D017410:Practice Guidelines as Topic / Q000592:standards*; D040081:Protein Array Analysis / Q000379:methods / Q000639:trends; D057170:Translational Research, Biomedical / Q000379:methods / Q000639:trends; D014584:User-Computer Interface