*-omics

I do not truly understand the Roche biochemical pathways poster.

Preoteomics, genomics, phenomics, connectomics. On the understanding and inference of networks of control in living systems using statistics. Generates lots of interesting problems at the nexus of various other statistical problems, like model selection, false discovery rates, causal graphs and so on.

Of course, there is a deep learning angle.

Is nilearn any good?

Gao, Chuan, Ian C. McDowell, Shiwen Zhao, Christopher D. Brown, and Barbara E. Engelhardt. 2016. “Context Specific and Differential Gene Co-Expression Networks via Bayesian Biclustering.” PLOS Comput Biol 12 (7): e1004791. https://doi.org/10.1371/journal.pcbi.1004791.

Lahiri, Subhaneil, Peiran Gao, and Surya Ganguli. 2016. “Random Projections of Random Manifolds,” July. http://arxiv.org/abs/1607.04331.

Shen, Yanning, Brian Baingana, and Georgios B. Giannakis. 2016. “Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity,” October. http://arxiv.org/abs/1610.06551.

Srivastava, Sudeep, and Liang Chen. 2010. “A Two-Parameter Generalized Poisson Model to Improve the Analysis of RNA-Seq Data.” Nucleic Acids Research 38 (17): e170–e170. https://doi.org/10.1093/nar/gkq670.

Thanei, Gian-Andrea, Nicolai Meinshausen Shah Rajen D., and Rajen D. Shah. 2016. “The Xyz Algorithm for Fast Interaction Search in High-Dimensional Data.” Arxiv 20 (9): 846–51. https://arxiv.org/abs/1610.05108.

Yao, Shun, Xianyang Zhang, and Xiaofeng Shao. 2016. “Testing Mutual Independence in High Dimension via Distance Covariance,” September. http://arxiv.org/abs/1609.09380.