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Genetics Biosciences
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gomez-dan/README.md

Hello ✨ I am Daniel Gomez, a visionary πŸ‘¨β€πŸ’» Molecular Biologist and Bioinformatician πŸ‘¨β€πŸ”¬!

  • πŸ‘‹ Hi, I’m Daniel J. Gomez, a graduate researcher at Stanford University School of Medicine.
  • πŸ‘€ I’m interested in exercise immunology, computational & systems cancer biology, spatial immunotherapy, precision health and evidence-based medicine.
  • 🌱 I’m currently learning computational cancer genetics, exercise immunology, and multimodal spatial and big data omics approaches.
  • πŸ’žοΈ I’m doing research, development, and analytics in basic and translational research for multiple consortia MoTrPAC, HuBMAP, HTAN, PsychENCODE, and All of Us Researchers.
  • πŸ“« How to reach me sfdanielgomez@gmail.com
  • πŸ˜„ Pronouns: he/him/his
  • ⚑ Fun fact: I won 1st, 2nd, 3rd place in grappling/jiu-jitsu competitions, played football, soccer, volleyball, played clarinet, saxophone, guitar, violin, and did academic research in 5 different medical schools and 1 veterinary medical college (JABSOM, JHUSOM, UCSDSOM, DUCOM, UFCVM).

My Academic profile is here for your viewing pleasure 🧭 🌎.

  • πŸ—ΊοΈ My present graduate studies is in Molecular and Cellular Atlases, Spatially Resolved Transcriptomics. Single-Cell RNA Sequencing, Deep Omics Profiling in Health and Disease, AI/ML Data Science and Cloud Computing in Precision Medicine, Biomedicine, Genetics and Genomics, Multiomics, Translational Medicine, Immunology, Pathogenomics and Computational Biology.

  • Currently, I am doing my thesis research in exerkine mapping in preclinical models and the human body, exercise and physical activity, multiomics, interorgan communication, signal transduction networks, and building multiscale spatial atlases of interorgan crosstalk at single-cell resolution, near-single cell super-resolution, and connect cell-cell interactions with ligand-receptor interactions and their functions inside the cell that display the effect of exerkines measured in health, resilience and disease.

You can access and read my papers on Google Scholar ORCID

Research:

  • Precision Medicine
  • Exerkines and Exercise
  • Mechanisms that underlie the benefits of exercise (exercise science research)
  • Exercise Genetics, Biochemistry, Molecular Biology, and Physiology
  • Precision Medicine to Network Medicine
  • Computational biology and whole-organism models
  • Spatial Multi-Omics and Multiplex Imaging
  • Histology and histopathology (Pathogenetics and pathogenomics)
  • Single-cell sequencing (sc/snATACseq, sc/snRNAseq, CITE-seq, etc)
  • Developing analytical tools to harness both high-dimensional single-cell phenotype data and spatial info
  • Spatial analysis of tissue architecture, neighborhood coordination and proximity analysis (cellular niches/areas)
  • Annotating spatially resolved single-cell data by spatial cell learning
  • Multi-omics multi-tissue molecular mapping (Tissue- and Organism-Wide Multi-omics)
  • Molecular Bioengineering, Nanotechnology, Nanomedicine, and Cell and Gene Therapy
  • Cellular Physiology Contextualization

Technique Interests:

  • Genomics and Proteomics, Metabolomics (multiomics), Structural Variations and Predictions
  • Systems Biology and Applications
  • Biological Modeling and Evaluation, Drug Development
  • Data visualization, Data analysis, Data mining
  • Biological and Disease Modeling (AI/ML/DL)
  • Molecular neuroimmune-pathology, psychoneuroimmunology (PNI), neuroimmunopharmacology (NIP)
  • Neurotherapeutics and Nanotherapeutics discovery
  • Morphology and imaging (histology, whole slide imaging, multiplexing)

Skills

  • Data Science and Cloud Computing of Precision Medicine
  • Bioinformatics
  • Data Analysis and Data Visualization
  • Algorithm Development
  • Computational Biology
  • Statistical analysis and computing
  • Functional assay development and experimental design
  • Sequence analysis
  • DNA isolation
  • Phylogenetics
  • Tissue (in situ) experiments (immunohistochemistry, in situ hybridization)
  • Machine Learning & Generative AI
  • Deep Learning, Reinforcement Learning
  • Processing large data sets
  • Neural networks
  • Big Data and Omics
  • Single-cell multiomics and Spatial omics research

Future Directions πŸ‘¨β€πŸ’»

- Principal Advisor/Investigator - Biomedical Data Scientist - Postdoctoral Fellow - PhD Researcher - Scientist I/II - Research Associate

Hobbies

  • Working out (resistance training, cardiovascular exercise)
  • Hiking, Cycling, and Climbing
  • Reading, Listening to Audiobooks and Podcasts
  • Music and Movies

Pinned Loading

  1. cwltool cwltool Public

    Forked from hubmapconsortium/cwltool

    Common Workflow Language reference implementation

    Python

  2. intro_dgm intro_dgm Public

    Forked from jmtomczak/intro_dgm

    "Deep Generative Modeling": Introductory Examples

    Jupyter Notebook

  3. SPACEc SPACEc Public

    Forked from yuqiyuqitan/SPACEc

    Jupyter Notebook

  4. immunitastx/monkeybread immunitastx/monkeybread Public

    Analyzing cellular niches in spatial transcriptomics data

    Python 27 4

  5. Teichlab/visium_stitcher Teichlab/visium_stitcher Public

    Stitch multiple Visium slides together

    Jupyter Notebook 13 1

  6. liana-py liana-py Public

    Forked from saezlab/liana-py

    LIANA+: an all-in-one framework for cell-cell communication

    Python 1