Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance

Abdur Rahman M. A. Basher, Steven J. Hallam, Steven J. Hallam

2022

Abstract

Metabolic pathway prediction from genomic sequence information is an essential step in determining the capacity of living things to transform matter and energy at different levels of biological organization. A detailed and accurate pathway map enables researchers to interpret and engineer the flow of biological information from genotype to phenotype in both organismal and multi-organismal contexts. In this paper, we propose two novel hierarchical mixture models, SOAP (sparse correlated pathway group) and SPREAT (distributed sparse correlated pathway group), to improve pathway prediction outcomes. Both models leverage pathway abundance to represent an organismal genome as a mixed distribution of groups, and each group, in turn, is a mixture of pathways. Moreover, both models deal with missing potential pathways in the training set by provisioning supplementary pathways into the learning framework as part of noise reduction efforts. Because the introduction of supplementary pathways may lead to overestimation of some pathways, dual sparseness is applied. The resulting pathway group dataset is then used to train multi-label learning algorithms. Model effectiveness was evaluated on metabolic pathway prediction where correlated models, in particular, SOAP was able to equal or exceed the performance of previous pathway prediction algorithms on organismal genomes.

Paper Citation

in Harvard Style

M. A. Basher A. and Hallam S. (2022). Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-552-4, SciTePress, pages 49-61. DOI: 10.5220/0010910100003123

in Bibtex Style

@conference{bioinformatics22,
author={Abdur Rahman M. A. Basher and Steven J. Hallam},
title={Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS},
year={2022},
pages={49-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010910100003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS
TI - Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance
SN - 978-989-758-552-4
AU - M. A. Basher A.
AU - Hallam S.
PY - 2022
SP - 49
EP - 61
DO - 10.5220/0010910100003123
PB - SciTePress