For example, there are few multi-functional genes like p53, HSP90, etc that tend to be involved in lots of functions. If a network simply predicts these for all functions it does pretty well for retrospective gene function prediction. https://t.co/9dRCxVZD
@NatureNews @ElieDolgin Interesting article! We've long been documenting that these sorts of imbalances have a big impact on genomics and bioinformatics, for example https://t.co/ZvNUl8gY9F https://t.co/mSDnhZEqOz https://t.co/UUTpR5SQLh
Pavlidis mentions: ...Multifunctional Genes... http://t.co/aotIdgAMV8 hubbiness predicts these better than more complex methods #ismb #afp14
@bgood I wonder if "down-weighting overlapping genes" is addressing the same problem of multifunctionality observed in http://t.co/FQNxKzLc
RT @biocs: J. Gilles: the emperor has no clothes, can predict gene function from network degree alone (no data used) http://t.co/EuJ3irb
RT @AmitDeshwar: Jesse Gillis is savaging GO enrichment analysis in Hall A http://t.co/BIdBGG1 #ISMB
RT @AmitDeshwar: Jesse Gillis is savaging GO enrichment analysis in Hall A http://t.co/BIdBGG1 #ISMB
Jesse Gillis is savaging GO enrichment analysis in Hall A http://t.co/BIdBGG1 #ISMB