ventura = {
"name" : "Nicholas A. Ventura",
"location" : "United States 🇺🇸",
"background" : ["Life Sciences Consulting", "Bachelor of Science in Cell, Molecular and Developmental Biology",
"Master of Business and Science in Biotechnology Management"],
"goal" : "Graduate research in Genetics, Genomics & Bioinformatics",
"interests" : ["Comparative Genomics", "Human Genetics",
"Quantitative Genetics", "AI & Data Science"],
"currently" : "Building CS foundations for computational biology research"
}My current work sits at the intersection of Python application development and data tooling — building dashboards and pipelines that surface intelligence for research and ML investigations. I am targeting graduate programs in Genetics, Genomics & Bioinformatics (GGB) to extend this work into the computational biology domain.
Languages
Tools & Frameworks
Visualizations
Machine Learning
APIs
Domain
Interactive Streamlit dashboard consuming the ClinicalTrials.gov v2 API and NIH MeSH ontology. Multi-filter search (status, phase, intervention type, agency class, date range), Plotly visualizations, LLM-assisted query refinement, and CSV/PDF export.
# Pull studies from ClinicalTrials.gov v2 API with field-level selection
params = {
"fields": (
"protocolSection.identificationModule.nctId,"
"protocolSection.statusModule.overallStatus,"
"protocolSection.designModule.phases,"
"protocolSection.identificationModule.organization.class,"
"protocolSection.contactsLocationsModule.locations.country"
)
}
response = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params)Stack: Python · Streamlit · Pandas · Plotly · OpenAI API · ClinicalTrials.gov API · NIH MeSH XML
2024 ──── Clinical Research Analysis Tool.....................🔄 Active Sprint
└─ Python, Streamlit, REST APIs, LLM integration
Multiple Regression/Commercial Forecasting Tool.....🔄 Active Sprint
└─ Python, Streamlit, REST APIs, LLM integration
2025 ──── Clinical Trial Analytics Platform ··················🔄 Active Sprint
└─ Python, Streamlit, REST APIs, LLM integration
Intro to Software Engineering (IBM & Coursera)······✅ Complete
└─ Java OOP, Collections, File I/O, Date/Time API
2026 ──── Graduate Program (GGB) ······························🎯 Target
└─ Comparative Genomics · Human Genetics
Quantitative Genetics · AI & Data Science
── Bioinformatics Tooling ····························🔜 Next
└─ Biopython, R/Bioconductor, GATK pipelines,
FASTQ/BAM/VCF processing, genomic data structures
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