linkstream2 microblog

paper comparing human and mouse lncRNAs
The Vast, Conserved Mammalian lincRNome
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002917

Two papers in Science talking about recurrent mutations in TERT promoter in melanoma.

1) http://www.sciencemag.org/content/339/6122/957.full
Highly Recurrent TERT Promoter Mutations in Human Melanoma
Franklin W. Huang1,2,3,* Eran Hodis1,3,4,*,Mary Jue Xu1,3,4,Gregory V. Kryukov1,Lynda Chin5,6,Levi A. Garraway1,2,3,†

2) http://www.sciencemag.org/content/339/6122/959.full
TERT Promoter Mutations in Familial and Sporadic Melanoma
Susanne Horn1,2,Adina Figl1,2,P. Sivaramakrishna Rachakonda1,Christine Fischer3,Antje Sucker2,Andreas Gast1,2,Stephanie Kadel1,2,Iris Moll2,Eduardo Nagore4,Kari Hemminki1,5,Dirk Schadendorf2,*,†,Rajiv Kumar1,*,†

http://www.genetics.org/content/180/2/1023.full.pdf

Old idea or miRNS sponging applied to a new class of RNAs — circRNAs
http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11993.html?WT.ec_id=NATURE-20130228

used the singlish/multi concept. Then used machine learning to predict whether a protein is singlish or multi

PLOS ONE
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0056833
Taner Z. Sen

http://www.cell.com/developmental-cell/abstract/S1534-5807%2812%2900142-6

Michal Levin, Tamar Hashimshony, Florian Wagner, and Itai Yanai.
Developmental milestones punctuate gene expression in the
Caenorhabditis embryo. Developmental Cell (May 2012)

Nice disussion of the phylotypic stage .
Figure 4S-E have stage mapping between dmel and celegans

Abstractions for genomics
Communications of the ACM
Volume 56 Issue 1, January 2013
Pages 83-93

http://cseweb.ucsd.edu/~varghese/PAPERS/querythegenome.pdf
http://dl.acm.org/citation.cfm?id=2398376

intron & exon length distributions

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0020015

nice maps

http://cacm.acm.org/magazines/2013/1/158775-human-mobility-characterization-from-cellular-network-data/fulltext

Some thoughts on a good paper giving intuition on machine learning approaches

http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
http://dl.acm.org/citation.cfm?id=2347755

In particular, the paper gives good intuition about:

– overfitting (e.g. how it’s related to multiple testing & bias v variance)
– the curse of dimensionality (in high-D all neighbors look the same)
– the non-practicality of theoretical guarantees
– how different frontiers can give the same prediction
– ensembles (which reduce variance greatly without increasing bias that much)
– ensembles vs Bayesian model averaging (which essentially select the best model)