Predicting the Pathway Involvement of All Pathway and Associated Compound Entries Defined in the Kyoto Encyclopedia of Genes and Genomes

• Published in: Metabolites 2024-10, Vol.14 (11), p.582
• Main Authors: Huckvale, Erik D, Moseley, Hunter N B
• Format: Article
• Language: English
• Subjects: Annotations; Cell culture; Datasets; Genes; Genomes; Genomics; Information processing; KEGG; Machine learning; Matthews correlation coefficient; Medical research; Metabolic pathways; Metabolism; Metabolites; multi-layer perceptron; pathway prediction; Physiological aspects; Random access memory; Standard deviation; Transfer learning
• ISSN: 2218-1989; 2218-1989
• DOI: 10.3390/metabo14110582

: Predicting the biochemical pathway involvement of a compound could facilitate the interpretation of biological and biomedical research. Prior prediction approaches have largely focused on metabolism, training machine learning models to solely predict based on metabolic pathways. However, there are many other types of pathways in cells and organisms that are of interest to biologists. : While several publications have made use of the metabolites and metabolic pathways available in the Kyoto Encyclopedia of Genes and Genomes (KEGG), we downloaded all the compound entries with pathway annotations available in the KEGG. From these data, we constructed a dataset where each entry contained features representing compounds combined with features representing pathways, followed by a binary label indicating whether the given compound is associated with the given pathway. We trained multi-layer perceptron binary classifiers on variations of this dataset. : The models trained on 6485 KEGG compounds and 502 pathways scored an overall mean Matthews correlation coefficient (MCC) performance of 0.847, a median MCC of 0.848, and a standard deviation of 0.0098. : This performance on all 502 KEGG pathways represents a roughly 6% improvement over the performance of models trained on only the 184 KEGG metabolic pathways, which had a mean MCC of 0.800 and a standard deviation of 0.021. These results demonstrate the capability to effectively predict biochemical pathways in general, in addition to those specifically related to metabolism. Moreover, the improvement in the performance demonstrates additional transfer learning with the inclusion of non-metabolic pathways.