# Resolving and understanding the genomic basis of heterogeneous complex traits and diseases

> **NIH NIH R35** · MICHIGAN STATE UNIVERSITY · 2021 · $234,750

## Abstract

Parent grant
Resolving and understanding the genomic basis of heterogeneous complex traits and diseases.
Hundreds of genomics studies have exposed major gaps in our understanding of the mechanistic relationships
between genomic variation, cellular processes, tissue function, and trait variation. The goal of the parent
project is to develop a suite of computational frameworks that integrate massive collections of genomic and
biomedical data to make the following three advances:
Direction 1: Discern and leverage mechanism-based subtypes of complex traits and diseases.
Direction 2: Characterize physiology and disease along the human lifespan and across the sexes.
Direction 3: Find analogous contexts in model organisms for studying human traits/diseases.
As demonstrated by us and others, genome-wide molecular networks are grand unifiers of molecular data and
knowledge, and serve as powerful tools to contextually understand the roles genes play in cellular pathways,
tissue physiology, phenotype/disease mechanisms, and drug action. Hence, a central aspect of our parent
project is to develop multiple machine learning approaches to leverage molecular networks to generate
accurate, testable hypotheses about the roles genes play in defining subtypes, age/sex differences, and
cross-species analogs of a range of complex disorders.
 As part of this work, we have developed GenePlexus github.com/krishnanlab/GenePlexus, an open source
software to run and benchmark our state-of-the-art approach for combining genome-scale networks with
supervised machine learning (ML) to get accurate novel predictions about various gene attributes (e.g.,
pathway membership or disease association; Liu*, Mancuso*, et al., 2020 Bioinformatics).
Similarly, our group has committed efforts to make all our other computational methods available to the broader
biomedical research community in the form of software tools for open science. We have released such
software with nearly all our papers. Other recent examples include:
● PecanPy github.com/krishnanlab/PecanPy for parallelized, efficient, and accelerated node2vec.
● Expresto github.com/krishnanlab/Expresto for imputing unmeasured genes in transcriptomes.
● Txt2Onto github.com/krishnanlab/Txt2Onto for annotating –omics samples based on free-text metadata.
Goal of the supplement project and current prototype
GenePlexus: A cloud platform for network-based machine learning
The goal of the proposed supplement is to take our software development to the next level by a building a new
cloud-based GenePlexus platform to enable: i) biomedical/experimental researchers to seamlessly take
advantage of network-based ML to generate interpretable genome-wide predictions, and ii) computational
researchers to run network-based ML, retrieve results, and integrate with existing data analysis workflows.
The project team, which includes the PI, a postdoc trained in cloud computing, and two professional software
engineers – has worked together over the past...

## Key facts

- **NIH application ID:** 10406616
- **Project number:** 3R35GM128765-04S1
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Arjun Krishnan
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $234,750
- **Award type:** 3
- **Project period:** 2018-08-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406616

## Citation

> US National Institutes of Health, RePORTER application 10406616, Resolving and understanding the genomic basis of heterogeneous complex traits and diseases (3R35GM128765-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10406616. Licensed CC0.

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