*-omics

August 12, 2016 — January 22, 2020

buzzword

gene

information provenance

networks

stringology

I do not truly understand the Roche biochemical pathways poster.

Proteomics, genomics, phenomics, connectomics. Understanding and inferring 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?

1 References

Bhattacharya, Das, and Mukherjee. 2020. “Motif Estimation via Subgraph Sampling: The Fourth Moment Phenomenon.” arXiv:2011.03026 [Math, Stat].

Gao, McDowell, Zhao, et al. 2016. “Context Specific and Differential Gene Co-Expression Networks via Bayesian Biclustering.” PLOS Comput Biol.

Lahiri, Gao, and Ganguli. 2016. “Random Projections of Random Manifolds.” arXiv:1607.04331 [Cs, q-Bio, Stat].

Marin, Teufel, Horlacher, et al. 2023. “BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks.”

Perkel. 2022. “A Graphics Toolkit for Visualizing Genome Data.” Nature.

Shen, Baingana, and Giannakis. 2016. “Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity.” arXiv:1610.06551 [Stat].

Srivastava, and Chen. 2010. “A Two-Parameter Generalized Poisson Model to Improve the Analysis of RNA-Seq Data.” Nucleic Acids Research.

Thanei, Shah, and Shah. 2016. “The Xyz Algorithm for Fast Interaction Search in High-Dimensional Data.” Arxiv.

Torres, Leung, Lutz, et al. 2022. “De Novo Design of High-Affinity Protein Binders to Bioactive Helical Peptides.”

Watson, Juergens, Bennett, et al. 2022. “Broadly Applicable and Accurate Protein Design by Integrating Structure Prediction Networks and Diffusion Generative Models.”

Yao, Zhang, and Shao. 2016. “Testing Mutual Independence in High Dimension via Distance Covariance.” arXiv:1609.09380 [Stat].