• 👋 Hi, I’m Daniel J. Gomez, a researcher at Stanford University School of Medicine (SUSOM).
• 👀 I’m interested in computational & systems immunology, evidence-based and precision medicine.
• 🌱 I’m currently learning cancer genetics and multimodal omics approaches.
• 💞️ I’m doing big data in basic and translational cancer research for MoTrPAC, HuBMAP, HTAN, PsychENCODE, and All of Us Researchers.
• 📫 How to reach me [email protected]
• 😄 Pronouns: he/him/his
• ⚡ Fun fact: I won 1st, 2nd, 3rd place in grappling/jiu-jitsu competitions, played football, soccer, volleyball, played clarinet, saxophone, violin, and did academic research in 6 different medical schools (JABSOM, JHUSOM, UCSDSOM, UFCVM, DUCOM).

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

• 🗺️ 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

Research:

• Precision Bioinformatics
• 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)

• 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

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