Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.   –Marie Curie

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Diseases such as cancer are complex, involving changes on multiple biological levels. Disease-associated alterations range from the single gene level to cellular networks and tissue characteristics. Not only this, but each individual’s body presents a unique environment within which the disease manifests. Progress in treating such diseases requires application of sophisticated data analyses to multi-omic and phenotypic data in order to predict drug response and tolerability. Lyrid is committed to tackling these challenges with cutting edge science and machine learning methods to advance the state of clinical care.


From data to decisions: a personal story of scientific oncology




*DNAxFractal image generated using Malin Christersson’s image fractalizer
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Dr. Shirley Pepke is a computational biologist and principal scientist/owner at Lyrid LLC. She earned a BA with Honors from the University of Chicago and a PhD from University of California, Santa Barbara, both in physics. She has deep computational skills encompassing machine learning, dynamical systems model development, analysis, numerical simulations, and genomics. She has worked on diverse advanced software engineering projects including artificial intelligence applications at NASA, where she co-authored an open source release of a real-time diagnostic engine. Since the early 2000’s, Dr. Pepke’s work has focused on computational and systems biology research, especially in the context of high-throughput genomics, epigenomics, and transcriptomics. Her research in this area is routinely published in respected, peer reviewed journals. She was a staff computational scientist at the California Institute of Technology for several years. She has been an invited speaker for conferences at top universities. Dr. Pepke is well-known for her advocacy of clinically-oriented applications of genomics and machine learning, especially related to personalized medicine. She has performed research on combining these to inform cancer therapeutic development. Dr. Pepke will ensure a project is not only completed, but executed to the highest standard.


Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal. Pepke S, Nelson WM, Ver Steeg G.  (October 11, 2019). Manuscript and video at JoVE

A Portfolio Approach to Accelerate Therapeutic Innovation in Ovarian Cancer. Chaudhuri S, Cheng K, Lo AW, Pepke S, Rinaudo S, Roman L, and Spencer, R.  (October 25, 2018). Available at SSRN:

Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer. Pepke S, Ver Steeg G. BMC Med Genomics2017 Mar 15;10(1):12.

Single-cell transcriptome analysis reveals dynamic changes in lncRNA expression during reprogramming.   Kim DH, Marinov GK, Pepke S, Singer ZS, et al. Cell Stem Cell. 2015 Jan 8;16(1):88-101.

Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps.  Mortazavi A, Pepke S, Jansen C, Marinov GK, et al. Genome Research.2013 23:2136-2148.

An integrated encyclopedia of DNA elements in the human genome. ENCODE Project Consortium. Nature. 2012 Sep 6;489(7414):57-74.

A user’s guide to the encyclopedia of DNA elements (ENCODE). ENCODE Project Consortium. PLoS Biol. 2011 Apr;9(4):e1001046.

High resolution mapping of Twist to DNA in Drosophila embryos: Efficient functional analysis and evolutionary conservation. Ozdemir A, Fisher-Aylor KI, Pepke S, Samanta M, Dunipace L, McCue K, Zeng L, Ogawa N, Wold BJ, Stathopoulos A. Genome Res. 2011 Apr;21(4):566-77.

A dynamic model of interactions of Ca2+, calmodulin, and catalytic subunits of Ca2+/calmodulin-dependent protein kinase II. Pepke S, Kinzer-Ursem T, Mihalas S, Kennedy MB. PLoS Comput Biol. 2010 Feb 12;6(2):e1000675.

Computation for ChIP-seq and RNA-seq studies. Pepke S, Wold B, Mortazavi A. Nat Methods. 2009 Nov;6(11 Suppl):S22-32. doi: 10.1038/nmeth.1371. Review.

Using confidence set heuristics during topology search improves the robustness of phylogenetic inference. Pepke SL, Butt D, Nadeau I, Roger AJ, Blouin C. J Mol Evol. 2007 Jan;64(1):80-9.


From Data to Decisions: A personal Story of Scientific Oncology. Keynote at Precision Medicine 2017: Breakaway Business Models hosted by Harvard University Dept. of Biomedical Informatics.

A Story of Big Data, Cancer, and Collaboration. DataEDGE Conference 2017 — UC Berkeley School of Information.