Leveraging heterogeneous network embedding for metabolic pathway prediction

Product Manifold Representations for Learning on Biological Pathways

Machine learning models that embed graphs in non-Euclidean spaces have shown substantial benefits in a variety of contexts, but their application has not been studied extensively in the biological domain, particularly with respect to biological pathway graphs. Such graphs exhibit a variety of complex network structures, presenting challenges to existing embedding approaches. Learning high-quality embeddings for biological pathway graphs is important for researchers looking to understand the underpinnings of disease and train high-quality predictive models on these networks. In this work, we investigate the effects of embedding pathway graphs in non-Euclidean mixed-curvature spaces and compare against traditional Euclidean graph representation learning models. We then train a supervised model using the learned node embeddings to predict missing protein-protein interactions in pathway graphs. We find large reductions in distortion and boosts on in-distribution edge prediction performance as a result of using mixed-curvature embeddings and their corresponding graph neural network models. However, we find that mixed-curvature representations underperform existing baselines on out-of-distribution edge prediction performance suggesting that these representations may overfit to the training graph topology. We provide our Mixed-Curvature Product Graph Convolutional Network code at https://github.com/mcneela/Mixed-Curvature-GCN and our pathway analysis code at https://github.com/mcneela/Mixed-Curvature-Pathways.

Multi-label classification with XGBoost for metabolic pathway prediction

BACKGROUND

Metabolic pathway prediction is one possible approach to address the problem in system biology of reconstructing an organism's metabolic network from its genome sequence. Recently there have been developments in machine learning-based pathway prediction methods that conclude that machine learning-based approaches are similar in performance to the most used method, PathoLogic which is a rule-based method. One issue is that previous studies evaluated PathoLogic without taxonomic pruning which decreases its performance.

RESULTS

In this study, we update the evaluation results from previous studies to demonstrate that PathoLogic with taxonomic pruning outperforms previous machine learning-based approaches and that further improvements in performance need to be made for them to be competitive. Furthermore, we introduce mlXGPR, a XGBoost-based metabolic pathway prediction method based on the multi-label classification pathway prediction framework introduced from mlLGPR. We also improve on this multi-label framework by utilizing correlations between labels using classifier chains. We propose a ranking method that determines the order of the chain so that lower performing classifiers are placed later in the chain to utilize the correlations between labels more. We evaluate mlXGPR with and without classifier chains on single-organism and multi-organism benchmarks. Our results indicate that mlXGPR outperform other previous pathway prediction methods including PathoLogic with taxonomic pruning in terms of hamming loss, precision and F1 score on single organism benchmarks.

CONCLUSIONS

The results from our study indicate that the performance of machine learning-based pathway prediction methods can be substantially improved and can even outperform PathoLogic with taxonomic pruning.

Graph embedding on mass spectrometry- and sequencing-based biomedical data

Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein-protein interaction networks and predicting novel drug functions.

A compendium of bacterial and archaeal single-cell amplified genomes from oxygen deficient marine waters

Oxygen-deficient marine waters referred to as oxygen minimum zones (OMZs) or anoxic marine zones (AMZs) are common oceanographic features. They host both cosmopolitan and endemic microorganisms adapted to low oxygen conditions. Microbial metabolic interactions within OMZs and AMZs drive coupled biogeochemical cycles resulting in nitrogen loss and climate active trace gas production and consumption. Global warming is causing oxygen-deficient waters to expand and intensify. Therefore, studies focused on microbial communities inhabiting oxygen-deficient regions are necessary to both monitor and model the impacts of climate change on marine ecosystem functions and services. Here we present a compendium of 5,129 single-cell amplified genomes (SAGs) from marine environments encompassing representative OMZ and AMZ geochemical profiles. Of these, 3,570 SAGs have been sequenced to different levels of completion, providing a strain-resolved perspective on the genomic content and potential metabolic interactions within OMZ and AMZ microbiomes. Hierarchical clustering confirmed that samples from similar oxygen concentrations and geographic regions also had analogous taxonomic compositions, providing a coherent framework for comparative community analysis.

Metabolic Pathway Prediction Using Non-Negative Matrix Factorization with Improved Precision

Machine learning provides a probabilistic framework for metabolic pathway inference from genomic sequence information at different levels of complexity and completion. However, several challenges, including pathway features engineering, multiple mapping of enzymatic reactions, and emergent or distributed metabolism within populations or communities of cells, can limit prediction performance. In this article, we present triUMPF (triple non-negative matrix factorization [NMF] with community detection for metabolic pathway inference), which combines three stages of NMF to capture myriad relationships between enzymes and pathways within a graph network. This is followed by community detection to extract a higher-order structure based on the clustering of vertices that share similar statistical properties. We evaluated triUMPF performance by using experimental datasets manifesting diverse multi-label properties, including Tier 1 genomes from the BioCyc collection of organismal Pathway/Genome Databases and low complexity microbial communities. Resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved precision on multi-organismal datasets.