PT  - JOURNAL ARTICLE
AU  - Nathaniel T. Hawkins
AU  - Marc Maldaver
AU  - Anna Yannakopoulos
AU  - Lindsay A. Guare
AU  - Arjun Krishnan
TI  - Systematic tissue annotations of –omics samples by modeling unstructured metadata
AID  - 10.1101/2021.05.10.443525
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.10.443525
4099  - http://biorxiv.org/content/early/2021/05/20/2021.05.10.443525.short
4100  - http://biorxiv.org/content/early/2021/05/20/2021.05.10.443525.full
AB  - There are currently >1.3 million human –omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language. We propose a natural-language-processing-based machine learning approach (NLP-ML) to infer tissue and cell-type annotations for –omics samples based only on their free-text metadata. NLP-ML works by creating numerical representations of sample descriptions and using these representations as features in a supervised learning classifier that predicts tissue/cell-type terms. Our approach significantly outperforms an advanced graph-based reasoning annotation method (MetaSRA) and a baseline exact string matching method (TAGGER). Model similarities between related tissues demonstrate that NLP-ML models capture biologically-meaningful signals in text. Additionally, these models correctly classify tissue-associated biological processes and diseases based on their text descriptions alone. NLP-ML models are nearly as accurate as models based on gene-expression profiles in predicting sample tissue annotations but have the distinct capability to classify samples irrespective of the –omics experiment type based on their text metadata. Python NLP-ML prediction code and trained tissue models are available at https://github.com/krishnanlab/txt2onto.Competing Interest StatementThe authors have declared no competing interest.