# Support for New Bioinformatics Methods Development

> **NIH NIH N01** · SCIOME, LLC · 2020 · $210,270

## Abstract

New bioinformatics method development support includes, image analysis for glyphosate toxicity where deep-learning based image processing tmethods were used to discriminate between normal, stressed and cell-death conditions of HepaRG cells and primary hepatocytes; Evidence tagging protocols were develop for evidence mapping for the OHAT group;  an evaluation of existing tagging methods was performed currently available in the SWIFT-Review program; Machine Learning methods were used for Document tagging activity exploring alternative to the keyword-based tagging strategy currently used in SWIFT-Review.

## Key facts

- **NIH application ID:** 10281443
- **Project number:** 273201700001C-P00005-9999-5
- **Recipient organization:** SCIOME, LLC
- **Principal Investigator:** RUCHIR SHAH
- **Activity code:** N01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $210,270
- **Award type:** —
- **Project period:** 2017-03-24 → 2021-03-23

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10281443

## Citation

> US National Institutes of Health, RePORTER application 10281443, Support for New Bioinformatics Methods Development (273201700001C-P00005-9999-5). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10281443. Licensed CC0.

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