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Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients’ Classification

  • Systems-Level Quality Improvement
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Abstract

The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step. Then we apply RF to classify new patients into their corresponding classes. The evaluation technique is to run the RF classifier on the training data to get a model. Then, we apply this model on the test data to find the appropriate class. We use as metrics the balanced accuracy (BAC) and the area under the receiver operating characteristic curve (AUROC) to measure the performance of our model. The proposed method is evaluated on the dataset issued from DREAM 9 challenge. The comparison is done with a pure random forest (without feature selection), and with the two best ranked results of the DREAM 9 challenge. We used three types of data: only clinical data, only proteomics data, and finally clinical and proteomics data combined. The numerical results show that the highest scores are obtained when using clinical data alone, and the lowest is obtained when using proteomics data alone. Further, our method succeeds in finding promising results compared to the methods presented in the DREAM challenge.

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Notes

  1. http://dreamchallenges.org/project/dream-9-acute-myeloid-leukemia-aml-outcome-prediction/

  2. These data were provided by Dr. Steven Kornblau from The University of Texas MD Anderson Cancer Center and were obtained through Synapse syn2455683 as part of the AML DREAM Challenge.

  3. http://www.pf-bird.univ-nantes.fr/

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Acknowledgements

The authors would like to thank DREAM 9 Challenge for providing data in public. Also PROFAS B + as well as the 1 month program stay provided by the university of Tizi Ouzou. We are most grateful to the bioinformatics core facility of Nantes (BiRD - Biogenouest) for its technical support.

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Authors and Affiliations

  1. Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), El-Alia BP 32, Bab-Ezzouar, Algiers, Algeria

    Lokmane Chebouba & Dalila Boughaci

  2. LS2N, UMR 6004, École Centrale de Nantes, Nantes, France

    Carito Guziolowski

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  1. Lokmane Chebouba
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  2. Dalila Boughaci
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  3. Carito Guziolowski
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Correspondence to Lokmane Chebouba.

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Author L. CHEBOUBA declares that he has no conflict of interest. Author D. BOUGHACI declares that she has no conflict of interest. Author C. GUZIOLOWSKI declares that she has no conflict of interest.

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Chebouba, L., Boughaci, D. & Guziolowski, C. Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients’ Classification. J Med Syst 42, 129 (2018). https://doi.org/10.1007/s10916-018-0972-z

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