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Experienced computational biologist with a demonstrated history of solving complex biological problems using Mathematical Modeling, Machine Learning, Large-scale Proteomics, Python, Cellular Biology and Computer Science. Currently working to develop novel proteomic technologies as part of Encodia, I earned a Doctor of Philosophy (PhD) in Computational Biology from Carnegie Mellon University and University of Pittsburgh in 2015 and went on to work on the Human Protein Atlas. My current research interests include proteomics, cell cycle dynamics, cancer biology, aging, and translational medicine.
Leader in the development of next-generation protein sequencing. Our novel technologies create scalable and parallelized approaches to protein measurement and analysis. My current focus is on creating pipelines that effectively combine modern machine learning techniques ("AI") with high-throughput experimentation to efficiently engineer proteins and enzymes.
(All figures and captions from https://www.encodia.com/technology)
Recent Publications
Recent Publications
Nature
Nature
The cell cycle is a fundamental process of life. Its dysregulation has devastating consequences. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. In this work we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.
Protein RNA
Nature Methods
Nature Methods
Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.
Science Signaling
Science Signaling
The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry–based proteomics and antibody-based immunoassays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood.
Students
Students
Congratulations Dr. Winsnes!!!
Congratulations Dr. Winsnes!!!
Dr. Casper Winsnes (M.S. & Ph.D.) Thesis: On computational methods for spatial mapping of the human proteome, 2015-2022.
Previous Students
Previous Students
Dr. Casper Winsnes (M.S. & Ph.D.) Thesis: On computational methods for spatial mapping of the human proteome, 2015-2022.
Lovisa Åkesson (Ph.D.) Nuclear and Subnuclear proteomics, 2015-2019
Trang Le (M.S.), An integrated cell model of nucleoli based on nuclei and microtubule representation, 2018.
Rebecca Elyanow (B.S.), Generative models of Neurons, 2013 – 2015.
Bilal Jaradat (B.S.), Temporal changes in cellular organization, 2014.
Rohan Arepally (B.S.), SBML-Spatial support in CellOrganizer, 2013 – 2014.
Marc-Daniel Julien (B.S.), CellOrganizer for Python, 2013.
Charlotte Darby (B.S.), Generative models of Neurons, 2013.
Xuexia Jiang (B.S.), Generative models of Neurons, 2012 – 2013. (Received the Elizabeth W. Jones award for excellence in undergraduate research in Experimental or Computational Biology).
Evan Cesank (B.S.), Learning structure-localization relationships from imaging data, 2012.
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