Biology subjects

Huckvale, E. D.

Publications and source records attributed to Huckvale, E. D..

7 recordsLinked to original sources

bioRxiv2024

Predicting the Pathway Involvement of Compounds Annotated in the Reactome Knowledgebase

MotivationPathway annotations of non-macromolecular (relatively small) biomolecules facilitate biological and biomedical interpretation of metabolomics datasets. However, low pathway annotation levels of detected biomolecules hinder this type of interpretation. Thus, predicting the pathway involvement of detected but unannotated biomolecules has high potential to improve metabolomics data analysis and omics integration. Past publications have only made use of the Kyoto Encyclopedia of Genes and Genomes derived datasets to develop machine learning models to predict pathway involvement. However, to our knowledge, the Reactome knowledgebase has not been utilized to develop these types of predictive models. ResultsWe created a dataset ready for machine learning using chemical representations of all path-way-annotated compounds available from the Reactome knowledgebase. Next, we trained and evaluated a single multilayer perceptron binary classifier using combined metabolite-pathway paired feature vectors engineered from this new dataset. While models trained on a prior corresponding KEGG dataset with 502 pathways scored a mean Matthews correlation coefficient (MCC) of 0.847 and 0.0098 standard deviation, the models trained on the Reactome dataset with 3,985 pathways demonstrated improved performance with a mean MCC of 0.916, but with a higher 0.0149 standard deviation. These results indicate that the pathways in Reactome can also be effectively predicted, greatly increasing the number of human-defined pathways available for prediction. AvailabilityCode and data for fully reproducing the results in this work are available at https://doi.org/10.6084/m9.figshare.27478065. Contacthunter.moseley@uky.edu.

systems biology↗

bioRxiv2024

Predicting the pathway involvement of metabolites annotated in the MetaCyc knowledgebase

The associations of metabolites with biochemical pathways are highly useful information for interpreting molecular datasets generated in biological and biomedical research. However, such pathway annotations are sparse in most molecular datasets, limiting their utility for pathway level interpretation. To address these shortcomings, several past publications have presented machine learning models for predicting the pathway association of small biomolecule (metabolite and zenobiotic) using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG). But other similar knowledgebases exist, for example MetaCyc, which has more compound entries and pathway definitions than KEGG. As a logical next step, we trained and evaluated multilayer perceptron models on compound entries and pathway annotations obtained from MetaCyc. From the models trained on this dataset, we observed a mean Matthews correlation coefficient (MCC) of 0.845 with 0.0101 standard deviation, compared to a mean MCC of 0.847 with 0.0098 standard deviation for the KEGG dataset. These performance results are pragmatically the same, demonstrating that MetaCyc pathways can be effectively predicted at the current state-of-the-art performance level. Author summaryMany thousands of different molecules play important roles in the processes of life. To generally handle the complexity of life, biological and biomedical researchers typically organize the molecular parts and pieces of biological processes into pathways of biomolecules and their myriad of molecular interactions. While the role of large macromolecules like proteins are well characterized within these pathways, the role of small biomolecules are not as comprehensively known. To close this knowledge gap, several machine learning models have been trained on data from a knowledgebase known as the Kyoto Encyclopedia of Genes and Genomes (KEGG) to predict which pathways a small biomolecule is associated with. More data generally improves these machine learning models. So in this work, we used the MetaCyc knowledgebase to increase the amount of data available by about ten-fold and then trained new machine learning models that demonstrate comparable prediction performance to models trained on KEGG, but covering 8-fold more pathways defined in MetaCyc vs KEGG.

systems biology↗

bioRxiv2024

Predicting The Pathway Involvement For All Pathway and Associated Compound Entries Defined in the Kyoto Encyclopedia of Gene and Genomes

Background/ObjectivesPredicting 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 on metabolic pathways. However, there are many other types of pathways in cells and organisms which are of interest to biologists; MethodsWhile 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 KEGG. From this 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; ResultsThe model trained on all KEGG compounds and pathways scored an overall mean performance of 0.847, median of 0.848, and standard deviation of 0.0098. ConclusionsThe mean performance of 0.847 with a standard deviation of 0.0098 for all KEGG pathways, compared to the performance of 0.800 and standard deviation of 0.021 of metabolic KEGG pathways only, demonstrates 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.

systems biology↗

bioRxiv2024

Predicting the Pathway Involvement of Metabolites in Both Pathway Categories and Individual Pathways

Metabolism is the network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic knowledgebases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) contain metabolites, reactions, and pathway annotations; however, such resources are incomplete due to current limits of metabolic knowledge. To fill in missing metabolite pathway annotations, past machine learning models showed some success at predicting KEGG Level 2 pathway category involvement of metabolites based on their chemical structure. Here, we present the first machine learning model to predict metabolite association to more granular KEGG Level 3 metabolic pathways. We used a feature and dataset engineering approach to generate over one million metabolite-pathway entries in the dataset used to train a single binary classifier. This approach produced a mean Matthews correlation coefficient (MCC) of 0.806 {+/-} 0.017 SD across 100 cross-validations iterations. The 172 Level 3 pathways were predicted with an overall MCC of 0.726. Moreover, metabolite association with the 12 Level 2 pathway categories were predicted with an overall MCC of 0.891, representing significant transfer learning from the Level 3 pathway entries. These are the best metabolite-pathway prediction results published so far in the field.

systems biology↗

bioRxiv2024

Predicting The Pathway Involvement Of Metabolites Based on Combined Metabolite and Pathway Features

A major limitation of most metabolomics datasets is the sparsity of pathway annotations of detected metabolites. It is common for less than half of identified metabolites in these datasets to have known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a "pathway category", as defined by one of the metabolic knowledgebases like the Kyoto Encyclopedia of Gene and Genomes. Most of these models are implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating predictions for multiple pathway categories. This single binary classifier per pathway category approach both multiplies the computational resources necessary for training while diluting the positive entries in gold standard datasets needed for training. To address the limitations of training separate classifiers, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts both features representing a metabolite and features representing a generic pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite-pathway features-pair approach is not only competitive with the combined performance of training separate binary classifiers, but it outperforms the previous benchmark models.

systems biology↗

bioRxiv2023

A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement

The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to predict these metabolite-to-pathway mappings, utilizing a training dataset of known metabolite-to-pathway mappings. A few such training datasets have been derived from the Kyoto Encyclopedia of Gene and Genomes (KEGG). However, several prior published machine learning approaches utilized an erroneous KEGG-derived training dataset that used SMILES molecular representations strings (KEGG-SMILES dataset) and contained a sizable proportion ([~]26%) duplicate entries. The presence of so many duplicates taint the training and testing sets generated from k-fold cross-validation of the KEGG-SMILES dataset. Therefore, the k-fold cross-validation performance of the resulting machine learning models was grossly inflated by the erroneous presence of these duplicate entries. Here we describe and evaluate the KEGG-SMILES dataset so that others may avoid using it. We also identify the prior publications that utilized this erroneous KEGG-SMILES dataset so their machine learning results can be properly and critically evaluated. In addition, we demonstrate the reduction of model k-fold cross-validation performance after de-duplicating the KEGG-SMILES dataset. This is a cautionary tale about properly vetting prior published benchmark datasets before using them in machine learning approaches. We hope others will avoid similar mistakes.

bioinformatics↗

bioRxiv2023

Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites

Metabolic pathways are a human-defined grouping of life sustaining biochemical reac-tions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic inter-pretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to pre-dict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific compu-tational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1-score of 0.8180 and Matthews correlation coeffi-cient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.

bioinformatics↗