# Knowledge Management Center for Illuminating the Druggable Genome

> **NIH NIH U24** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $249,999

## Abstract

SUMMARY
The understudied protein targets that are the focus of the implementation phase of the Illuminating the Druggable
Genome (IDG) project need to be placed in the contexts of gene-sets/pathways, drugs/small-molecules,
diseases/phenotypes, and cells/tissues. By extending our previous methods, we will impute knowledge about
the understudied potential target protein kinases, GPCRs, and ion channels listed in the RFA using machine
learning strategies. To establish this classification system, we will organize data from many omics- and literature-
based resources into attribute tables where genes are the rows and their attributes are the columns. Examples
of such attribute tables include gene or protein expression in cancer cell lines (CCLE) or human tissues (GTEx),
changes in expression in response to drug perturbations or single-gene knockdowns (LINCS), regulation by
transcription factors based on ChIP-seq data (ENCODE), and phenotypes in mice observed when single genes
are knocked out (KOMP). In total, we will process and abstract data from over 100 resources. We will then predict
target functions, target association with pathways, small-molecules/drugs that modulate the activity and
expression of the target, and target relevance to human disease. To further validate such predictions, we will
employ text mining to identify knowledge that corroborates with the data mining predictions, perform molecular
docking of predicted small molecules using homology modeling, and seek associations between variants and
human diseases by mining electronic medical records (EMR) together with genomic profiling of thousands of
patients. In addition, we will develop innovative data visualization tools to allow users to interact with all the
collected data, and develop social networking software to build communities centered around
proteins/genes/targets as well as biological topics including pathways, cell types, drugs/small-molecules, and
diseases. Overall, we will develop an invaluable resource that will accelerate target and drug discovery.

## Key facts

- **NIH application ID:** 9843449
- **Project number:** 5U24CA224260-03
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Avi Ma'ayan
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,999
- **Award type:** 5
- **Project period:** 2018-01-08 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9843449

## Citation

> US National Institutes of Health, RePORTER application 9843449, Knowledge Management Center for Illuminating the Druggable Genome (5U24CA224260-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9843449. Licensed CC0.

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