I'm Anna Shcherbina.

About Me

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I am a PhD student in the Biomedical Informatics program at Stanford University. I am jointly advised by Dr. Anshul Kundaje and Dr. Euan Ashley. I am interested in developing algorithms that utilize machine learning and data mining approaches to derive medically relevant conclusions from multi-layer omics data. Prior to beginning my graduate studies at Stanford, I was a researcher at MIT Lincoln Laboratory in the Bioengineering Systems and Technologies group. During this time I developed algorithms to characterize microbiome metagenomic datasets and was involved in developing and administering the DTRA Metagenomics Grand Challenge[1]. I developed the FASTQSim algorithm[2,3] to simulate complex, platform-indepentent HTS datasets for use as an algorithm benchmarking tool. Additionally, I developed algorithms to detect kinship and deconvolve mixtures in forensic samples by analyzing allele frequencies and inheritance patterns in forensic SNP panels[4,5,6]. During this time, I also developed a strong interest in epigenetics and participated in a time-series CHiP-seq study to characterize different stages of healing from muskoskeletal injury in a mouse model[7].

I am developing deep learning algorithms for identifying pathogenic variants in undiagnosed diseases. I am also mining big data resources such as the UK Biobank and the MyHeart Counts mobile health data for meaningful associations between physical activity and health. My long term goal is to contribute to precision medicine by integrating physical activity, medical history, and genetic information to build a more complete picture of patient health

Research Interests

• Convolutional and Recurrent Neural Networks
• Data Mining
• Epigenetic mechanisms ofgene regulation
• Algorithms for identification of causal pathogenic variants in disease
• Precision Health
• Mobile Health (Wearable devices)

Publications

• A. Shcherbina, et al. Accuracy in wrist-worn sensor-based measurements of heart rate and energy expenditure in a diverse cohort. JPM (under review). Preprint: https://doi.org/10.1101/094862
• E. Ashley, A. Shcherbina, et al. Feasibility of obtaining measures from a Smartphone App: The MyHeartCounts Cardiovascular Heatlh Study. JAMA Cardiol. 2017;2(1):67-76. doi:10.1001/jamacardio.2016.4395
• Aguilar C, Pop R, Shcherbina A, Meissner A, et al. Transcriptional and chromatin dynamics of muscle regeneration after severe trauma. Stem Cell Reports. 2016 Nov 8. 7(5):983-997. doi:10.1016/j.stemcr.2016.09.009.
• Gafney-Stomberg E, Lutz J, Shcherbina A., Ricke D, Petrovick M, Cropper T, Cable S, McClung J. Associations between single gene polymorphisms and bone biomarkers and response to calcium and vitamin D supplementation in young adults undergoing military training. J Bone Miner Res. 2016 Sep 28. doi:10.1002/jbmr.3008.
• Shrikumar A, Greenside P, Shcherbina A, Kundaje A. Not just a black box: learning important features through propagating activation differences. ArXiv e-prints [Internet]. 2016 May 1; 1605 (Preprint)
• D. Ricke, J. Harper, A. Shcherbina, N. Chiu. Integrated Biomedical System. BioRxiv e-prints [Internet]. 2016 April 25; doi: http://dx.doi.org/10.1101/050138.
• D. Ricke, A. Shcherbina, N. Chiu. Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim. BioRxiv e-prints [Internet]. 2016 March 31; doi:http://dx.doi.org/10.1101/046532.
• A. Shcherbina. FASTQSim: platform-independent data characterization and in silico read generation for NGS datasets. BMC Research Notes. 2014. 7(1):533.
• C. Aguilar, A. Shcherbina, D. Ricke, R. Pop, C. Carrigan, C. Gifford, M. Urso, M. Kottke, A. Meissner. In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing. Scientific Reports. 2015 Sep 18;5:13885. doi: 10.1038/srep13885.
• D. Ricke, A. Shcherbina. DAWN: Rapid Larse-Scale Protein Multiple Sequence Alignment and Conservation Analysis. IEEE HPEC, Sep. 2015.
• D. Ricke, A. Shcherbina, N. Chiu, J. Harper, M. Petrovick, T. Boettcher, C. Zook, J. Bobrow, E. Wack. Sherlock's Toolkit: A Forensic DNA Analysis System. IEEE International Conference. Mar. 2015.
• A. Shcherbina, D. Ricke, N. Chiu, T. Boettcher, C. Zook, J. Bobrow, M. Petrovick, E. Schwoebel. KinLinks:Software tool for kinship analysis and pedigree generation from NGS datasets. BioRxiv e-prints [Internet]. 2016 April 06; doi: http://dx.doi.org/10.1101/046938.
• J Isaacson, E. Schwoebel, A. Shcherbina, D. Ricke, J. Harper, M. Petrovick, J. Bobrow, T. Boettcher, B. Helfer, C. Zook, E. Wack. Robust detection of individual forensic profiles in DNA mixtures. Forensic Sci. Int. Genetics. 2014. 14C:31-37.
• S. Dodson, D. Ricke, J. Kepner, N. Chiu, A. Shcherbina. Rapid sequence identification of potential pathogens using techniques from sparse linear algebra. IEEE International Conference. 2015.

Education

I hold M.Eng (computer science) and BS (computer science, biological engineering) degrees from MIT. My M.Eng research focused on developing machine learning algorithms for short tandem repeat (STR) profile authentication for forensic applications.