Abstract
Prediction models that rely on time series data are often affected by diminished predictive accuracy. This occurs from the causal relationships of the data that shift over time. Thus, the changing weights that are used to create prediction models lose their informational value. One way to correct this change is by using concept drift information. That is exactly what prediction models in biomedical applications need. Currently, metabolomics is at the forefront in modeling analysis for phenotype prediction, making it one of the most interesting candidates for biomedical prediction diagnosis. However, metabolomics datasets include dynamic information that can harm prediction modeling. The study presents concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Birks, J., et al.: Evaluation of a prediction model for colorectal cancer: retrospective analysis of 2.5 million patient records. Cancer Med. 6(10), 2453–2460 (2017)
Jae-woo, L., et al.: The development and implementation of stroke risk prediction model in national health insurance Service’s personal health record. Comput. Methods Program. Biomed. 153, 253–257 (2018)
Tantawy, A.A., Naguib, D.M.: Arginine, histidine and tryptophan: a new hope for cancer immunotherapy. PharmaNutrition 8, 100149 (2019)
Changsong, G., et al.: Isoleucine plays an important role for maintaining immune function. Curr. Protein Pept. Sci. 20(7), 644–651 (2019)
Iyer, A., Fairlie, D.P., Brown, L.: Lysine acetylation in obesity, diabetes and metabolic disease. Immunol. Cell Biol. 90(1), 39–46 (2012)
Andras, P.: Metabolic control of immune system activation in rheumatic diseases. Arthritis Rheumatol. 69(12), 2259–2270 (2017)
Webb, G.I., Hyde, R., Cao, H., Nguyen, H.L., Petitjean, F.: Characterizing concept drift. Data Min. Knowl. Disc. 30(4), 964–994 (2016). https://doi.org/10.1007/s10618-015-0448-4
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28645-5_29
Grulich, P.M., et al. Scalable detection of concept drifts on data streams with parallel adaptive windowing. In: EDBT, pp. 477–480 (2018)
Imen, K., et al.: Self-adaptive windowing approach for handling complex concept drift. Cogn. Comput. 7(6), 772–790 (2015)
Huang, D.T.J., et al. Detecting volatility shift in data streams. In: 2014 IEEE International Conference on Data Mining, pp. 863–868. IEEE (2014)
Sun, J., Li, H., Adeli, H.: Concept drift-oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction. IEEE Trans. Syst. Man Cybern. Syst. 43(4), 801–813 (2013)
Aggarwal, C.C.: On biased reservoir sampling in the presence of stream evolution. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 607–618 (2006)
Guajardo, J.A., Weber, R., Miranda, J.: A model updating strategy for predicting time series with seasonal patterns. Appl. Soft Comput. 10(1), 276–283 (2010)
Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: an ensemble method for drifting concepts. J. Mach. Learn. Res. 8, 2755–2790 (2007)
Sun, Y., et al.: Concept drift adaptation by exploiting historical knowledge. IEEE Trans. Neural Netw. Learn. Syst. 29(10), 4822–4832 (2018)
Shenglan, L., et al.: Concept drift detection for data stream learning based on angle optimized global embedding and principal component analysis in sensor networks. Comput. Electr. Eng. 58, 327–336 (2017)
Pless, R., Souvenir, R.: A survey of manifold learning for images. IPSJ Trans. Comput. Vis. Appl. 1, 83–94 (2009)
Wei, L., et al.: Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J. Med. Internet Res. 18(12), e323 (2016)
Žliobaité, I.: Learning under concept drift: an overview. arXiv preprint arXiv:1010.4784 (2010)
Wang, T.J., et al.: Metabolite profiles and the risk of developing diabetes. Nat. Med. 17(4), 448–453 (2011)
Clement, I.P., et al.: Chemical form of selenium, critical metabolites, and cancer prevention. Cancer Res. 51(2), 595–600 (1991)
Montemayor, D., Sharma, K.: mGWAS: next generation genetic prediction in kidney disease. Nat. Rev. Nephrol. 16(5), 255–256 (2020)
Moats, R.A., et al.: Abnormal cerebral metabolite concentrations in patients with probable Alzheimer disease. Magn. Reson. Med. 32(1), 110–115 (1994)
Schwarzerova, J., Bajger, A., Pierdou, I., Popelinsky, L., Sedlar, K., Weckwerth, W.: An innovative perspective on metabolomics data analysis in biomedical research using concept drift detection. In: Proceedings 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM2021) (2021). (in press)
Xiaojing, C., et al.: Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol. 22(1), 1–22 (2021)
Jacob, M., et al.: Scikit-multiflow: a multi-output streaming framework. J. Mach. Learn. Res. 19(1), 2914–2915 (2018)
Ekaba, B.: Logistic regression. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform, Apress, Brekeley, CA, pp. 243–250 (2019)
Oza, N.C., Russell, S.J.: Online bagging and boosting. In: International Workshop on Artificial Intelligence and Statistics. PMLR, pp. 229–236 (2001)
Sanjeev, K., et al.: Design of adaptive ensemble classifier for online sentiment analysis and opinion mining. PeerJ. Comput. Sci. 7, e660 (2021)
Manuel, B.G., et al.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams, pp. 77–86 (2006)
de Barros, R.S.M., de Carvalho Santos, S.G.T.: An overview and comprehensive comparison of ensembles for concept drift. Inf. Fusion 52, 213–244 (2019)
Bei, D., et al.: A prospective study of serum metabolomic and lipidomic changes in myopic children and adolescents. Exp. Eye Res. 199, 108182 (2020)
Acknowledgement
This work has been supported by grant FEKT-K-21-6878 realised within the project Quality Internal Grants of BUT (KInG BUT), Reg. No. CZ.02.2.69/0.0 /0.0/19_073/0016948, which is financed from the OP RDE.
We would like to thank Adam Hospodka for their support of our study, by building on the project team’s results in Machine Learning and Data Mining (PV056) course at Masaryk University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Schwarzerova, J. et al. (2022). A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_42
Download citation
DOI: https://doi.org/10.1007/978-3-031-09135-3_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09134-6
Online ISBN: 978-3-031-09135-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)