Machine Learning Methods in the Computational Biology of Cancer is an arXiv preprint of a pretty nice article dealing with some analysis that can be used for high-dimensional biological (and other) data – although the examples come from cancer research, they could easily be about something else. This paper does a good job of describing penalized regression methods such as lasso, ridge regression and elastic net. It also goes into compressed sensing and its applicability to biology, although cautioning that it cannot yet be straightforwardly applied to biological data. This is because compressed sensing is based on the assumption that one can choose the “measurement matrix” freely, whereas in biology, it (usually called “design matrix” in this context) is already fixed.
The Critical Assessment of Massive Data Analysis (CAMDA) 2014 conference has released its data analysis challenges. Last year’s challenges on toxicogenomics and toxicity prediction will be reprised (perhaps in modified form, I didn’t check), but they have added a new challenge which I find interesting because it focuses on data integration (combining distinct data sets on gene, protein and micro-RNA expression as well as gene structural variations and DNA methylation) and uses published data from the International Cancer Genome Consortium (ICGC). I think it’s a good thing to re-analyze, mash up and meta-analyze data from these large-scale projects, and the CAMDA challenges are interesting because they are so open-ended, in contrast to e g Kaggle challenges (which I also like but in a different way). The goals in the CAMDA challenges are quite open to interpretation (and also ambitious), for instance:
Question 1: What are disease causal changes? Can the integration of comprehensive multi-track -omics data give a clear answer?
Question 2: Can personalized medicine and rational drug treatment plans be derived from the data? And how can we validate them down the road?