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Metabolic pathway inference using multi-label classification with rich pathway features
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 16, Iss 10, p e1008174 (2020)
- Publication Year :
- 2020
- Publisher :
- Public Library of Science, 2020.
-
Abstract
- Metabolic inference from genomic sequence information is a necessary step in determining the capacity of cells to make a living in the world at different levels of biological organization. A common method for determining the metabolic potential encoded in genomes is to map conceptually translated open reading frames onto a database containing known product descriptions. Such gene-centric methods are limited in their capacity to predict pathway presence or absence and do not support standardized rule sets for automated and reproducible research. Pathway-centric methods based on defined rule sets or machine learning algorithms provide an adjunct or alternative inference method that supports hypothesis generation and testing of metabolic relationships within and between cells. Here, we present mlLGPR, multi-label based on logistic regression for pathway prediction, a software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks in organismal and multi-organismal datasets. We evaluated mlLGPR performance using a corpora of 12 experimental datasets manifesting diverse multi-label properties, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previous reports for organismal genomes and identify specific challenges associated with features engineering and training data for community-level metabolic inference.<br />Author summary Predicting the complex series of metabolic interactions e.g. pathways, within and between cells from genomic sequence information is an integral problem in biology linking genotype to phenotype. This is a prerequisite to both understanding fundamental life processes and ultimately engineering these processes for specific biotechnological applications. A pathway prediction problem exists because we have limited knowledge of the reactions and pathways operating in cells even in model organisms like Esherichia coli where the majority of protein functions are determined. To improve pathway prediction outcomes for genomes at different levels of complexity and completion we have developed mlLGPR, multi-label based on logistic regression for pathway prediction, a scalable open source software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks. We benchmark mlLGPR performance against other inference methods providing a code base and metrics for continued application of machine learning methods to the pathway prediction problem.
- Subjects :
- 0301 basic medicine
Computer science
ved/biology.organism_classification_rank.species
Inference
Genome
Biochemistry
Machine Learning
0302 clinical medicine
Software
Mathematical and Statistical Techniques
Metabolic potential
Databases, Genetic
Biology (General)
0303 health sciences
education.field_of_study
Ecology
Rule sets
Applied Mathematics
Simulation and Modeling
Statistics
Genomics
Enzymes
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Benchmark (computing)
Metabolic Pathways
Algorithms
Metabolic Networks and Pathways
Research Article
Computer and Information Sciences
Cell Physiology
QH301-705.5
Population
Computational biology
Research and Analysis Methods
Genome Complexity
Biosynthesis
Low complexity
03 medical and health sciences
Cellular and Molecular Neuroscience
Machine Learning Algorithms
Artificial Intelligence
Proteobacteria
Genetics
Statistical Methods
Model organism
education
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Multi-label classification
ved/biology
business.industry
Biology and Life Sciences
Proteins
Computational Biology
Cell Biology
Cell Metabolism
Metabolic pathway
030104 developmental biology
ComputingMethodologies_PATTERNRECOGNITION
Metabolism
Logistic Models
Enzymology
business
030217 neurology & neurosurgery
Mathematics
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 16
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....7e0c2a4cade2ac34170354896c759fd7