linkstream2 microblog

The Dark Market for Personal Data
http://www.nytimes.com/2014/10/17/opinion/the-dark-market-for-personal-data.html We’re all “judged by a #bigdata Star Chamber of unaccountable decision makers”

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We need regulation to help consumers recognize the perils of the new information landscape without being overwhelmed with data. The right to be notified about the use of one’s data and the right to challenge and correct errors is fundamental. Without these protections, we’ll continue to be judged by a big-data Star Chamber of unaccountable decision makers using questionable sources.

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Delving into Deep Learning http://www.americanscientist.org/issues/pub/2014/3/delving-into-deep-learning History of #NeuralNets from perceptrons to today’s complex nets with many hidden layers

http://linkstream2.gerstein.info/tag/i0dataday
http://elischolar.library.yale.edu/dayofdata/2014/
https://storify.com/markgerstein/tweets-related-to-the-yale-day-of-data-2014-ydod20

Spatial Generalization in… Learning: Lessons from… #Basketball http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003623 How past success changes your tendencies to shoot

Describes constructing a learning matrix for how a player will update his tendency to shoot
from a certain region of the court based on his past successes or failures

simultaneously clustering of cancer data across data types, in a sense related to orthoclust
http://www.mskcc.org/research/epidemiology-biostatistics/biostatistics/icluster

https://storify.com/markgerstein/tweets-related-to-kdd-2014-i0kdd-kdd2014

https://www.flickr.com/photos/mbgmbg/tags/seriesspacyworldofbloombergbldg

http://linkstream2.gerstein.info/tag/i0kdd/

http://archive.gersteinlab.org/meetings/s/2014/08.28/kdd2014-i0kdd-meeting-materials/ (need password)

http://www.kdd.org/kdd2014/

#BigData & Its Technical Challenges
http://cacm.acm.org/magazines/2014/7/176204-big-data-and-its-technical-challenges/abstract Data acquisition, cleaning, aggregation, analysis, visualization & interpretation

Pandey mentions: Comparative Analysis of #Ensemble Classifiers [eg mean agg. or stacking]…in Genomics
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6729565&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6729565 #kdd2014

performance-diversity tradeoff: should one incl. higher performance, lower diversity ones…. but still adding diversity is good

related to https://github.com/shwhalen/datasink

http://www.amazon.com/The-Signal-Noise-Many-Predictions/dp/159420411X

Bioengineering & #systemsbiology. Classic def’n in terms of the “4 M’s”—Measurement, #Mining, Modeling & Manipulation
http://www.ncbi.nlm.nih.gov/pubmed/16474915

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Systems Biology can also be defined operationally, as
by the MIT Computational & Systems Biology Initiative,
in terms of the “4 M’s”—Measurement, Mining, Modeling,
and Manipulation—illustrated schematically in Fig. 1 (see
http://csbi.mit.edu/).

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