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Explainable Artificial Neural Network for Recurrent Venous Thromboembolism Based on Plasma Proteomics

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Book cover Computational Methods in Systems Biology (CMSB 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12881))

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Abstract

Venous thromboembolism (VTE) is the third most common cardiovascular disease, affecting \(\sim \)1,000,000 individuals each year in Europe. VTE is characterized by an annual recurrent rate of \(\sim \)6%, and \(\sim \)30% of patients with unprovoked VTE will face a recurrent event after a six-month course of anticoagulant treatment. Even if guidelines recommend life-long treatment for these patients, about \(\sim \)70% of them will never experience a recurrence and will receive unnecessary lifelong anti-coagulation that is associated with increased risk of bleeding and is highly costly for the society. There is then urgent need to identify biomarkers that could distinguish VTE patients with high risk of recurrence from low-risk patients.

Capitalizing on a sample of 913 patients followed up for the risk of VTE recurrence during a median of \(\sim \)10 years and profiled for 376 plasma proteomic antibodies, we here develop an artificial neural network (ANN) based strategy to identify a proteomic signature that helps discriminating patients at low and high risk of recurrence. In a first stage, we implemented a Repeated Editing Nearest Neighbors algorithm to select a homogeneous sub-sample of VTE patients. This sub-sample was then split in a training and a testing sets. The former was used for training our ANN, the latter for testing its discriminatory properties. In the testing dataset, our ANN led to an accuracy of 0.86 that compared to an accuracy of 0.79 as provided by a random forest classifier. We then applied a Deep Learning Important FeaTures (DeepLIFT) – based approach to identify the variables that contribute the most to the ANN predictions. In addition to sex, the proposed DeepLIFT strategy identified 6 important proteins (DDX1, HTRA3, LRG1, MAST2, NFATC4 and STXBP5) whose exact roles in the etiology of VTE recurrence now deserve further experimental validations.

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Acknowledgments

M.R was financially supported by the GENMED Laboratory of Excellence on Medical Genomics [ANR-10-LABX-0013], a research program managed by the National Research Agency (ANR) as part of the French Investment for the Future. DA.T was partially supported by the EPIDEMIOM-VTE Senior Chair from the Initiative of Excellence of the University of Bordeaux. The proteomics screening was financed by a grant from Stockholm County Council (SLL 2017-0842) and from Familjen Erling Perssons Foundation. G.M has benefited from training offered by the EUR DPH, a PhD program supported within the framework of the PIA3 (Investment for the future), project reference 17-EURE-0019.

Additional Information. The script to build the model can be found on the following link: https://github.com/misbahch6/paper_script.

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Authors and Affiliations

  1. Univ. Bordeaux, Inserm, BPH, U1219, 33000, Bordeaux, France

    Misbah Razzaq, Gaëlle Munsch & David Alexandre Tregouet

  2. Laboratory of Excellence GENMED (Medical Genomics), Bordeaux, France

    Misbah Razzaq & David Alexandre Tregouet

  3. Aix Marseille Univ, Inserm, INRAE, C2VN, Marseille, France

    Louisa Goumidi, Manal Ibrahim-Kosta & Pierre-Emmanuel Morange

  4. Laboratory of Haematology, La Timone Hospital, Marseille, France

    Manal Ibrahim-Kosta & Pierre-Emmanuel Morange

  5. Science for Life Laboratory, Department of Protein Science, CBH, KTH Royal Institute of Technology, Stockholm, Sweden

    Maria-Jesus Iglesias, Lynn Butler & Jacob Odeberg

  6. Department of Clinical Medicine, Faculty of Health Science, The Arctic University of Tromsö, Tromsö, Norway

    Maria-Jesus Iglesias, Lynn Butler & Jacob Odeberg

  7. Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden

    Maria Bruzelius

  8. Department of Hematology, Karolinska University Hospital, Stockholm, Sweden

    Maria Bruzelius

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  1. Misbah Razzaq
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Correspondence to Misbah Razzaq or David Alexandre Tregouet .

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Editors and Affiliations

  1. Univ. Grenoble Alpes, Inria, Grenoble, France

    Eugenio Cinquemani

  2. Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800, Talence, France

    Loïc Paulevé

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Razzaq, M. et al. (2021). Explainable Artificial Neural Network for Recurrent Venous Thromboembolism Based on Plasma Proteomics. In: Cinquemani, E., Paulevé, L. (eds) Computational Methods in Systems Biology. CMSB 2021. Lecture Notes in Computer Science(), vol 12881. Springer, Cham. https://doi.org/10.1007/978-3-030-85633-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-85633-5_7

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