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Computing on Phenotypic Descriptions for Candidate Gene Discovery and Crop Improvement.

Ian R Braun^1,2, Colleen F Yanarella^1,2, Carolyn J Lawrence-Dill^1,2,3

¹Interdepartmental Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA.

Plant Phenomics (Washington, D.C.)

|December 14, 2020

Summary

Automated natural language processing (NLP) and machine learning (ML) can analyze plant phenotypic descriptions from text, matching or exceeding human curator performance for data analysis and prediction.

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Area of Science:

Plant biology
Bioinformatics
Computational biology

Background:

Phenotypic descriptions are crucial for biological research but traditionally rely on manual curation into controlled vocabularies.
Human curation is time-consuming and does not scale to the vast amount of scientific literature.
Automated methods for analyzing natural language are emerging as a promising alternative.

Purpose of the Study:

To explore the application of NLP and ML for automated analysis of plant phenotypic descriptions.
To develop tools for the plant phenomics community to leverage these automated methods.
To assess the performance of automated methods compared to human curation.

Main Methods:

Utilizing natural language processing (NLP) and machine learning (ML) algorithms.
Applying these techniques to unstructured phenotypic descriptions from scientific literature.
Comparing the performance of automated data structures against human-curated data.

Main Results:

Automated methods can create data structures that perform as well as or better than human-curated data.
These methods show promise for tasks like predicting gene function and biochemical pathway membership.
Potential for in-field data collection using speech-to-text tools is highlighted.

Conclusions:

NLP and ML offer scalable and efficient solutions for analyzing plant phenomics data.
Automated analysis can enhance data aggregation, standardization, and predictive capabilities.
Future applications include in-field data collection for association genetics and breeding.