# Robust and efficient statistical inference methods for genomics

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA BERKELEY · 2023 · $61,248

## Abstract

PROJECT SUMMARY
Technological advances in sequencing and experimental assays have greatly increased the availability of vari-
ous kinds of genomic data, enabling us to catalog genetic and epigenetic variation in diverse populations, and
to probe fundamental biological processes (e.g., transcription and translation) in unprecedented detail. This de-
velopment is providing a number of new opportunities for basic and biomedical research, but often the data are
noisy and multifaceted, while the underlying biology is very complex, thus presenting both theoretical and com-
putational challenges for analysis and interpretation. New efficient and robust statistical inference tools, as well
as theoretical analysis of mathematical models, are much in need of development to bring the promise of the big
data era in biology to full fruition. The central goal of the parent project (R35-GM134922) is to develop a suite
of useful statistical and computational tools that will help to tackle this challenge, by enabling inference under
complex models and helping researchers integrate information from different types of data to reveal fundamental
biological processes. In particular, the parent project aims to achieve the following goals: (1) Improve and widen
deep learning/neural network applications in genomics. (2) Leverage cutting-edge techniques in natural lan-
guage processing (NLP) and massive protein databases to improve biological sequence representations, which
can facilitate downstream prediction tasks. (3) Develop novel computational methods for integrative analysis of
genomic data. The proposed diversity supplement will train and mentor an underrepresented minority student
through research projects that will help to achieve the above specific objectives of the parent grant.

## Key facts

- **NIH application ID:** 10669892
- **Project number:** 3R35GM134922-04S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Yun S Song
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $61,248
- **Award type:** 3
- **Project period:** 2019-12-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10669892

## Citation

> US National Institutes of Health, RePORTER application 10669892, Robust and efficient statistical inference methods for genomics (3R35GM134922-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10669892. Licensed CC0.

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