Easy Semantification of Bioassays

TL;DR

This paper introduces an automatic method for semantifying biological assays using clustering, which outperforms labeling approaches and achieves a high F1 score, establishing a new benchmark in the field.

Contribution

The paper presents a novel clustering-based approach for automating the semantification of biological assays, outperforming existing labeling methods and providing the first standardized evaluation.

Findings

Clustering significantly outperforms deep neural network labeling.

Achieved nearly 83% F1 score in semantification.

Established a new benchmark for the task.

Abstract

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.