*-omics

August 12, 2016 — January 22, 2020

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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 Incoming

Xyla

By analysing medical text and extracting biomedical entities and relations from the entire history of published medical science, Xyla can facilitate better real-world evidence-based clinical decision support and help make clinical research—such as research into new treatments, including de novo drug design as well as the repurposing of existing drugs—smarter and faster. In so doing, Xyla is fulfilling its mission of organising the world’s medical knowledge and making it more useful.
Home | Absci

Absci is a generative AI drug creation company. Our mission is simple: create better biologics for patients, faster.

2 References

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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.

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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].