# Biomedical Computing and Informatics Strategies for Precision Medicine

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $332,242

## Abstract

The use of genomic measures for precision medicine will depend critically on our ability to identify genes
whose expression impacts the initiation, progression, and severity of common diseases such as sporadic
cancer. A multitude of powerful computational and statistical methods have been developed over the last 20
years to assist with this endeavor. However, the vast majority of these approaches focus on error or related
measures such as sensitivity and specificity as a measure of model quality. These measures are important but
do not capture other measures of model quality that may be meaningful to biomedical researchers and
physicians. We propose here to develop a comprehensive approach to modeling genomics data that takes into
consideration multiple objective and subjective measures of model quality simultaneously. It is our working
hypothesis that multiobjective methods will yield results that are more consistent, more reproducible, and with
greater clinical impact. Specifically, we will develop a novel Hierarchical Pareto Optimization (HiParOp)
algorithm that is capable of integrating multiple criteria for a given computational model of gene expression and
clinical outcomes (AIM 1). This approach will first be validated with simulated gene expression data that reflect
the hierarchical complexity of cancer. We will then evaluate the HiParOp algorithm by applying it to several
well-studied and well-characterized breast cancer data sets that have led to diagnostic tests and new drug
targets (AIM 2). Here, we will include a long list of measures of model quality that include traditional objective
measures such as the cohesiveness or distinctiveness of tumor clusters as well as subjective measures such
as clinical relevance and druggability. Experience applying HiParOp to a well-studied cancer where significant
progress has been made will be used to make further refinements to the algorithm. We will then apply the
HiParOp approach to the genomic analysis of non-small cell lung cancer (NSCLC) where there is substantial
opportunity for improved diagnosis and treatment. We will analyze several carefully conducted gene
expression studies in NSCLC cancer tissue (AIM 3). Finally, we will develop and release an R package that will
allow others to easily implement the HiParOp method (AIM 4).

## Key facts

- **NIH application ID:** 9999032
- **Project number:** 5R01LM012601-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Xiuzhen Huang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $332,242
- **Award type:** 5
- **Project period:** 2017-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999032

## Citation

> US National Institutes of Health, RePORTER application 9999032, Biomedical Computing and Informatics Strategies for Precision Medicine (5R01LM012601-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9999032. Licensed CC0.

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