Abstract
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive, invariably fatal neurological disease that attacks the nerve cells responsible for controlling voluntary muscles. The disease belongs to a group of disorders known as motor neuron diseases, which are characterized by the gradual degeneration and death of motor neurons. Although ALS is incurable and fatal, with median survival of 3–5 years, treatment can extend the length and meaningful quality of life for patients. Here, to be useful clinically, we tried several feature selection methods to choose predictive features identified using ALS clinical trials dataset. The feature selection method of random frog coupled with partial least square is an exact way that can be helpful for predictive feature selection. We further apply the proposed regression method partial least square regression to predict 3–12 month ALS progression slope, as measured using the ALS functional rating scale (ALSFRS). The experiment results show that the proposed selector and predictor has shown itself to be robust to extreme outliers. It is of great benefit to accelerate ALS research and development, identify new disease predictors and potentially significantly reduce the costs of future ALS clinical trials.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kiernan, M.C., Vucic, S., Cheah, B.C., Turner, M.R., Eisen, A., Hardiman, O., Burrell, J.R., Zoing, M.C.: Amyotrophic lateral sclerosis. Lancet 377(9769), 942–955 (2011)
Drigo, D., Verriello, L., Clagnan, E., Eleopra, R., Pizzolato, G., Bratina, A., D’Amico, D., Sartori, A., Mase, G., Simonetto, M., de Lorenzo, L.L., Cecotti, L., Zanier, L., Pisa, F., Barbone, F.: The incidence of amyotrophic lateral sclerosis in Friuli Venezia Giulia, Italy, from 2002 to 2009: a retrospective population-based study. Neuroepidemiology 41(1), 54–61 (2013)
Miller, R.G., Mitchell, J.D., Moore, D.H.: Riluzole for amyotrophic lateral sclerosis (ALS)/motor neuron disease (MND). Cochrane Database Syst. Rev. (3) (2012)
Kollewe, K., Mauss, U., Krampfl, K., Petri, S., Dengler, R., Mohammadi, B.: ALSFRS-R score and its ratio: a useful predictor for ALS-progression. J. Neurol. Sci. 275(1–2), 69–73 (2008)
Kuffner, R., Zach, N., Norel, R., Hawe, J., Schoenfeld, D., Wang, L.X., Li, G., Fang, L., Mackey, L., Hardiman, O., Cudkowicz, M., Sherman, A., Ertaylan, G., Grosse-Wentrup, M., Hothorn, T., van Ligtenberg, J., Macke, J.H., Meyer, T., Scholkopf, B., Tran, L., Vaughan, R., Stolovitzky, G., Leitner, M.L.: Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nat. Biotechnol. 33(1), 51-U292 (2015)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(6), 1157–1182 (2002)
Li, H.D., Xu, Q.S., Liang, Y.Z.: Random frog: an efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification. Anal. Chim. Acta 740, 20–26 (2012)
Wang, S.-L., Li, J., Fang, J.: Predicting progression of ALS disease with random frog and support vector regression method. In: Huang, D.-S., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS, vol. 9773, pp. 160–170. Springer, Cham (2016). doi:10.1007/978-3-319-42297-8_16
Yun, Y.H., Li, H.D., Wood, L.R.E., Fan, W., Wang, J.J., Cao, D.S., Xu, Q.S., Liang, Y.Z.: An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 111(7), 31 (2013)
Li, H., Xu, Q., Liang, Y.: LibPLS: an integrated library for partial least squares regression and discriminant analysis. PeerJ (2014)
Acknowledgement
This work was supported by the grants of the National Science Foundation of China (Grant Nos. 61472467, 61672011, and 61471169) and the Collaboration and Innovation Center for Digital Chinese Medicine of 2011 Project of Colleges and Universities in Hunan Province. We also specially thank both Prize4Life and Sage Bionetworks-DREAM for providing the ALS clinical data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, J., Wang, SL., Wang, J. (2017). Research on Feature Selection and Predicting ALS Disease Progression. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-63309-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63308-4
Online ISBN: 978-3-319-63309-1
eBook Packages: Computer ScienceComputer Science (R0)