School of Medicine


Showing 201-210 of 299 Results

  • Milos Pjanic

    Milos Pjanic

    Postdoctoral Research Fellow, Cardiovascular Medicine

    Bio Research statement:
    My major fields of interest are computational biology and bioinformatics, coupled with the passion for the next-generation sequencing technologies, and a profound scientific interest in genomics, transcriptomics, regulation of gene expression, specificity of binding of transcription factors to the genome, histone modifications, nucleosome positioning, long-range genomic interactions and compartmentalization of the genome. My research lies on the frontier of the contemporary computational genomics, with the emphasis on development and testing of scripts and algorithms for the analysis of human genome and transcriptome. My focus is the improvement of methods for the various applications of the next generation sequencing, such as chromatin - immunoprecipitation sequencing or ChIP-Seq, RNA-sequencing or RNA-Seq, and probing open chromatin, DNase-Seq/ATAC-Seq, in order to answer key biological question that will ultimately help us understand better the underlying mechanisms of life. As a postdoc at Stanford’s Cardiovascular Institute, I am elucidating complex networks of interactions of transcription factors in human cardiac and vascular tissues, and molecular mechanisms that explain how cardiovascular disease risk-associated genomic loci confer disease risk. I am also employing allele specific computational pipelines to the existing next generation sequencing techniques, i.e. ChIP-Seq and RNA-Seq, in combination with the generation of eQTL data for human arterial smooth muscle cells (primary cell type of atherosclerotic lesions) to identify the causal variants that underlie disease susceptibility. In addition, I am modeling vascular SMC tissue-specific open chromatin with ATAC-Seq and DNase-Seq to understand the underlying mechanisms for cardiovascular disease causal variants. I am also active as a blogger, started a blog www.genomicscode.org and continually post UNIX and R related tips and resolve computational problems that can be applied to genomics.

  • Pornprang Plangsrisakul

    Pornprang Plangsrisakul

    Administrative Associate 3, Biomedical Data Science

    Current Role at Stanford Administrative Assistant in the Department of Biomedical Data Science

  • Giles W Plant

    Giles W Plant

    Associate Professor of Neurosurgery

    Current Research and Scholarly Interests Our research focuses on the repair of the injured spinal cord. We investigate the following areas:
    - Spinal cord injury (SCI): Axonal regeneration, myelination and gene therapy
    - Stem cell transplantation (adult, embryonic and iPS)
    - Endogenous stem cell activity after SCI
    - Olfactory ensheathing glia and olfactory neurogenesis

  • Andres Plata Stapper

    Andres Plata Stapper

    Postdoctoral Research Fellow, Otolaryngology - Head & Neck Surgery

    Current Research and Scholarly Interests I aim to discern the molecular mechanisms driving lineage specification during embryonic development, neurogenesis, organogenesis and disease, with a long term goal of the development of tools for precision medicine at single cell, tissue, individual and population level.

    My current research uses inner ear development as a model system. The inner ear is a complex structural and functional interconnected collection of sensory organs, responsible for our perception of sound, acceleration and balance. The inner ear semi-autonomously originates in early embryonic development from a patch of thickened ectoderm known as the otic placode. Advances in the understanding on otic development and lineage specification could lead to medical applications such as treating and identifying developmental disorders, as well as the development of in-vitro and in-vivo protocols for guided differentiation of sensory hair cells to cure deafness.

    I am working to generate a cell atlas specific to the initiation of inner ear development, when the otic placode thickens and undergoes molecular and morphological changes to form an otocyst, which eventually develops into a fully functional inner ear. I aim to identify early otic-specific lineages, the molecular signatures specific to each, and describe the spatio-temporal dynamics of cells and genes during this developmental time frame.

    I use computational tools to identify otic from non-otic cells in transgenic and wild type model organisms from multi-parallel qrtPCR from single cells, and concentrate on the otic populations for deep learning to accurately identify, characterize, and classify otic specific subpopulations. Computational approaches also allows us to determine the lineages composing the developing otic placode, to generate spatial and temporally accurate 3d models of organogenesis, and to design an otic cellular classifier using machine learning.

    I use multidisciplinary approaches including: 1) microfluidic technology for the generation of single cell gene expression data, 2) computational and statistical multi-dimensional data analysis approaches in the form of supervised and unsupervised machine learning, 3) molecular biology tools such as transgenics, multi-parallel qRT-PCR, immuno-histochemistry, single molecule in-situ hybridization and 4) confocal microscopy.

  • Terry Platchek

    Terry Platchek

    Clinical Associate Professor, Pediatrics

    Current Research and Scholarly Interests Research interest focuses on improving clinical processes using a "Lean" business strategy and engaging clinicains in systems based clinical improvement efforts.

  • Jessica Plaza

    Jessica Plaza

    Temp - Non-Exempt, Surgery - Anatomy

    Current Role at Stanford Anatomy Scholar in the School of Medicine Clinical Anatomy Division
    Stanford Alumni '16

  • Sylvia K. Plevritis, PhD

    Sylvia K. Plevritis, PhD

    Professor of Radiology (General Radiology), of Biomedical Data Science and, by courtesy, of Management Science and Engineering

    Current Research and Scholarly Interests My research program focuses on computational modeling of cancer biology and cancer outcomes. My laboratory develops stochastic models of the natural history of cancer based on clinical research data. We estimate population-level outcomes under differing screening and treatment interventions. We also analyze genomic and proteomic cancer data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.