Resume
Skills
Graph Theory & Network Analysis: Extensive experience in leveraging graph theoretical concepts such as betweenness, closeness, degree, and eigenvector centrality to analyze biological networks and identify critical biological features.
Bioinformatics & Computational Biology: Proficient in using bioinformatics tools and computational approaches, including PyMOL plugin development, AutoDock and PyRx, and graph neural networks (GNNs) to advance research in protein function, drug discovery, and molecular interactions.
Statistical Analysis & Data Extraction: Strong background in statistical analysis, including the use of SQL for extracting and analyzing clinical phenotypes from electronic health records. Experienced in conducting research to inform drug re-purposing and addiction studies.
Web Application Development: Skilled in designing and deploying CI/CD pipeline-driven web applications using Flask, Dash, Plotly, and AWS Amplify. Capable of developing applications that enable comprehensive analysis of biological networks and data visualization.
Programming Languages: Proficient in Python, Bash, and SQL with hands-on experience using libraries such as Pandas, NumPy, Matplotlib, NetworkX, Plotly, and py3Dmol for data analysis and visualization. Limited experience with CSS and JavaScript. Projects using MATLAB for solving ordinary and partial differential equations.
Automation & Scripting: Experienced in automating tasks such as the retrieval and analysis of PDB crystal structures and implementing scripts for complex data processing and analysis in research projects.
Scientific Communication: Demonstrated ability to convey complex scientific concepts through presentations and publications, with a strong track record of contributing to peer-reviewed journals.
Work Experience
Research Collaborator — 05/2023 to Current
The Blind Lab — Nashville, TN
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Leveraged graph theory, bioinformatics, and statistical analysis: Leveraged graph theory, bioinformatics, and statistical analysis to uncover physical mechanisms linking docked ligand binding energy to functional regulation in LRH-1. In publication.
Developed PyMOL plugin: Developed an easy-to-install, intuitive PyMOL plugin tailored for structural biologists. The plugin facilitates network analysis and visualization of protein structure networks (PSNs), including betweenness, closeness, degree, and eigenvector centrality, using simple point-and-click functionality. It allows users to toggle between two PSN views: a 'default' view displaying links or edges between residues in the 3D protein structure and a ball-and-stick representation. This tool aids in identifying allosteric communication pathways, functional 'hot spots,' and regions critical to biological function. Currently in preparation.
CI/CD pipeline-driven web application: Designed and deployed a CI/CD pipeline-driven web application using GitHub for version control and AWS Amplify for hosting. The application, built with Python's NetworkX, Plotly, py3Dmol, and Dash, provides comprehensive analysis of network properties in primary, secondary, and tertiary protein structures, enabling deeper insights into protein function and interactions.
Currently leading two independent projects: The first project is a graph-theoretical initiative to characterize the chemical and structural properties of allosteric binding sites using AutoDock and PyRx, along with a curated small-molecule library, aimed at advancing computational drug discovery through high-throughput screening. The second project leverages protein structure networks to quantify potential conformational switches associated with ligand binding in G-protein coupled receptors (GPCR).
Statistical Analyst I — 05/2021 to 05/2023
Center for Precision Medicine — Vanderbilt University Medical Center — Nashville, TN
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Iterative data modeling for clinical phenotyping:
- Initial data exploration—After being provided with draft definitions by stakeholders and senior staff, these guided the initial data exploration phase using elements such as diagnoses (e.g., ICD, CPT, or CUI codes), lab results, medications, and patient demographics. Preliminary results were aggregated into SQL views. Summary statistics, visualizations, and analyses were used to identify potential indicators that could define the clinical phenotype.
- Definition and Hypothesis Refinement—Successive rounds of analysis were guided by expert knowledge and continued consideration of clinical guidelines. Original SQL views were partitioned and restructured into increasingly specific subsets reflecting the emergence of more precise inclusion and exclusion criteria.
- Filtering and Data Transformation—Advanced filtering ensured that only those patients or data points that meet well-defined criteria were included in the final draft of the phenotype, filtering out patients with certain comorbidities or focusing only on patients with particular lab result thresholds.
- Finalization and Output—Once validated, the final SQL views were deployed as the 'canonical definition' of the clinical phenotype.
It is worth noting that this research was performed using the extensive electronic health record (EHR) data and BioVU resources managed by the Vanderbilt Institute for Clinical and Translational Research. This short description of iterative data modeling was used in the development of three clinical phenotypes: Alzheimer's detection based on mini-mental state examination (MMSE) scores, opioid use disorder in pregnancy, and juvenile idiopathic arthritis.
Web app development: Designed a web application using Python, Flask, D3.js, and Neo4j's Aura graph database to provide researchers with a queryable and interactive network visualization of the mapping between PheWAS codes and other standardized medical codes (ICD-9, ICD-10, CUI, CPT) to enhance the exploration and analysis of potential drug re-purposing opportunities.
Consultant — 07/2020 to 05/2021
Nexus Information Systems — Nashville, TN
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Automated the retrieval and analysis of 130+ PDB crystal structures which informed the identification of essential contact interactions in RORγ. In publication.
Delivered presentations on MRI physics, convolutional neural networks applied to fMRI analysis, and preliminary work on fMRI analysis using MATLAB’s Statistical Parametric Mapping (SPM) package.
Graduate Research Assistant — 06/2015 to 05/2019
University of Tennessee — Knoxville, TN
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Advanced the Staton Plant Biology Lab research objectives by automating the retrieval, transformation, and analysis of over 1500 soybean microarray datasets from the Gene Expression Omnibus using R programming. Constructed gene regulatory networks using partial correlations and partial variances (GeneNet, R), generating insights into complex genetic interactions. Conducted graph spectral analysis and paraclique clustering to identify functional modules within datasets. Applied statistical and qualitative assessments to ensure accuracy, consistency, and reliability. Contributed to published work by reviewing and summarizing literature from Web of Science and PubMed.
Education
Master of Science Degree — 01/2020
Genome Science and Technology
University of Tennessee — Knoxville, TN
ABT in Plant Systems Biology — 05/2015
Southern Illinois University — Carbondale, IL
Bachelor of Science — 05/2013
Mathematics
Southern Illinois University — Carbondale, IL
Bachelor of Arts — 05/2013
Quantitative Psychology
Southern Illinois University — Carbondale, IL
Certificates
- Coursera:
- Udemy:
Contact Me
If you have any questions or would like to discuss a potential project, please feel free to contact me at dfoutch@analysisandinformatics.org