Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

Shah, Hayat Ali; Liu, Juan; Yang, Zhihui; Feng, Jing

doi:10.3389/fmolb.2021.634141

Cited by 19 publications

(7 citation statements)

References 91 publications

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“…This innovative approach enables the inference of a new model for a closely related or phylogenetically linked species. It capitalizes on the correspondence between organisms to establish connections between metabolites across compartments through gene associations and reversible enzymatic reactions, as outlined in prior studies [ 76 , 77 ].…”

Section: Resultsmentioning

confidence: 99%

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Angarita-Rodríguez,

González-Giraldo,

Rubio-Mesa

et al. 2023

IJMS

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Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

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Section: Resultsmentioning

confidence: 99%

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Angarita-Rodríguez,

González-Giraldo,

Rubio-Mesa

et al. 2023

IJMS

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“…AI can help uncover hidden connections among diverse data fragments, thereby facilitating the extraction of crucial biological insights 36 . It has been demonstrated that AI models can successfully analyze complex omics data for diverse applications, such as the reconstruction of metabolic pathways 37 , drug discovery, and biomarkers 38,39 . Glycomics undoubtedly involves the deciphering process of hidden connections, especially given the ambiguity found in glycan structures (such as the non-template-directed, flexible, and repeating structural units available for a variety of modifications) and the various molecules that bind to glycans.…”

Section: Introductionmentioning

confidence: 99%

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Li,

Peralta,

Schoffelen

et al. 2024

Preprint

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Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.

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“…Machine learning approaches are effective in integrating prior information. However, its biological interpretability is limited ( Shah et al, 2021 ) and the performance is constrained by the sparsity of biological interactions. Also, due to the complexity of matrix operations, processing large-scale data is highly challenging.…”

Section: Introductionmentioning

confidence: 99%

Drug–disease association prediction with literature based multi-feature fusion

Kang

Hou

et al. 2023

Front. Pharmacol.

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Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge.Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism.Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916.Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

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Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

Cited by 19 publications

References 91 publications

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Drug–disease association prediction with literature based multi-feature fusion

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