# Maximizing the predictive power of high-throughput, microscopy-based phenotypic screens

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $480,849

## Abstract

PROJECT SUMMARY
We currently have an unprecedented ability to profile the genetic- and pathway-level changes that occur in cancer. Yet, clinicians lack the diverse arsenal of drugs needed to treat subpopulations of patients more effectively, reduce side effects and offer second-line treatment when drug resistance emerges. There is a pressing need to dramatically increase the repertoire of drugs available to fight cancer.
Advances in automated microscopy and computer vision, allow the widespread use of phenotypic profiling in early drug discovery. In this grant, we address two challenges. First, the power of phenotypic profiling has led to a growing number of large, disparate image datasets. In aim 1, we will develop machine-learning approaches that combine disparate datasets to obtain accurate predictions of uncharacterized compound function. Second, phenotypic screens can identify candidate compounds across multiple, diverse pathways, but often only use a single cancer cell line. In aim 2, we will develop strategies to identify minimal collections of cell lines that maximize detection of small molecule activities.
Successful execution will: increase the power of phenotypic profiling by harnessing existing phenotypic screening datasets and providing a rational approach for selecting cell lines that maximize the chance of discovering hits in desired pathways.

## Key facts

- **NIH application ID:** 9885647
- **Project number:** 2R01CA184984-06A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** LANI F WU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $480,849
- **Award type:** 2
- **Project period:** 2014-08-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9885647

## Citation

> US National Institutes of Health, RePORTER application 9885647, Maximizing the predictive power of high-throughput, microscopy-based phenotypic screens (2R01CA184984-06A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9885647. Licensed CC0.

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