LISP is one of the oldest computer languages and perhaps one of the most influential of the early ones. Some of the other well-known Eisenhower era languages -- Fortran, COBOL and ALGOL, have certainly left their mark, but LISP and derivatives such as Scheme or Common LISP certainly carries more cachet among "serious" programmers. COBOL has always been a bit of an easy joke and Fortran tends to mark you as old-school; use of APL (once a language of mine) would mark you as dangerously reactionary. ALGOL begat Pascal and Modula II and clearly had impact on the C syntax family of languages (including bioinformatics mainstays Python, Perl and Java) As I'll detail below, learning LISP has embarrassingly ended up stuck seemingly permanently on my future plans queue. But that's also because life never forced the issue: while LISP has certainly been used in bioinformatics (as covered in a review from 2016 ) , its mindshare in the community would seem to be very minimal.
A computational biologist's personal views on new technologies & publications on genomics & proteomics and their impact on drug discovery
Sunday, September 24, 2017
Monday, September 18, 2017
Teaching Biology Evidence: Old or New?
I've been toying over a week with writing something based on an interesting Twitter discussion started by Dr. Laura Williams (@MicroWavesSci) of Providence College pondering the best way to approach teaching molecular genetics (really, science in general) at the undergraduate level. In particular, Professor Williams wondered about the dangers of branding various key experiments with the names of the experimenters, such as Hershey-Chase or Meselson-Stahl. The risk she points out is that this can devolve into an exercise in memorizing names and dates without assimilating concepts, or conversely that some students will find the names more of a hindrance than a help. I'm going to play a bit with this, but I do emphasize that for her this is reality and for me it is a hobby (or perhaps a retirement fantasy, if I should ever actually retire). Or in other words, for the academic this is her industry but for this industrial scientist it is academic.
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