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Predicting bipolar disorder and schizophrenia based on non-overlapping genetic phenotypes using deep neural network

  • S. Karthik
  • M. SudhaEmail author
Special Issue

Abstract

Computational Psychiatry is an emerging field of science. It focuses on identifying the complex relationship between the brain’s neurobiology. Mental illness has recently become an important problem to be addressed as the number of people affected is increasing over time. Schizophrenia and Bipolar Disorder are two major types of psychiatric disorders. Most of the people are experienced these illness in their lifetime. But, diagnosing psychiatric disorders is even more a complex problem. Genetic factors play a vital role in developing mental illness. Interestingly, few psychiatric disorders have common genetic overlapping between each other. It causes detrimental effect on diagnosing the illness accurately. To overcome this existing issue, a Rank based Gene Biomarker Identification and Classification framework is proposed to identify the overlapping and non-overlapping gene patterns of bipolar disorder and schizophrenia. The dataset used in this experiment is obtained from Gene Expression Omnibus database. As an outcome of this experiment, seven biomarkers are identified as the overlapping genes. Also, 60 and 68 informative gene biomarkers are identified on bipolar disorder and schizophrenia dataset as feature subsets to discriminate the samples. Overlapping genes are eliminated to increase the diagnostic accuracy of the disorders. The performance of the proposed system is evaluated with standard existing machine learning algorithms. This proposed framework attained 97.01% and 95.65% accuracy on bipolar disorder and schizophrenia dataset with Deep Neural Network model outperformed other benchmarked algorithms and proved its efficacy.

Keywords

Biomarkers Computational genomics Machine learning Microarray Neural networks Pattern recognition 

Notes

References

  1. 1.
    Patel KR, Cherian J, Gohil K, Atkinson D (2014) Schizophrenia: overview and treatment options. Pharm Therap 39(9):638Google Scholar
  2. 2.
    Picchioni MM, Murray RM (2007) Schizophrenia. BMJ (Clin Res ed.) 335(7610):91–95CrossRefGoogle Scholar
  3. 3.
    Owen MJ, Sawa A, Mortensen PB (2016) Schizophrenia. Lancet (London, England) 388(10039):86–97CrossRefGoogle Scholar
  4. 4.
    Jauhar S, McKenna PJ, Radua J, Fung E, Salvador R, Laws KR (2014) Cognitive—behavioral therapy for the symptoms of schizophrenia: systematic review and meta-analysis with examination of potential bias. Br J Psychiatry 204(1):20–29CrossRefGoogle Scholar
  5. 5.
    Kerner B (2014) Genetics of bipolar disorder. Appl Clin Genet 7:33CrossRefGoogle Scholar
  6. 6.
    Culpepper L (2014) The diagnosis and treatment of bipolar disorder: decision-making in primary care. Prim Care Companion CNS Disord.  https://doi.org/10.4088/PCC.13r01609 CrossRefGoogle Scholar
  7. 7.
    Geddes JR, Miklowitz DJ (2013) Treatment of bipolar disorder. The Lancet 381(9878):1672–1682CrossRefGoogle Scholar
  8. 8.
    Hilty DM, Leamon MH, Lim RF, Kelly RH, Hales RE (2006) A review of bipolar disorder in adults. Psychiatry (Edgmont) 3(9):43Google Scholar
  9. 9.
    Phillips ML, Kupfer DJ (2013) Bipolar disorder diagnosis: challenges and future directions. The Lancet 381(9878):1663–1671CrossRefGoogle Scholar
  10. 10.
    Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucl Acids Res 30(1):207–210CrossRefGoogle Scholar
  11. 11.
    Iwamoto K, Bundo M, Kato T (2004) Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis. Hum Mol Genet 14(2):241–253CrossRefGoogle Scholar
  12. 12.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80CrossRefGoogle Scholar
  13. 13.
    Gautier L, Cope L, Bolstad BM, Irizarry RA (2004) affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307–315CrossRefGoogle Scholar
  14. 14.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl Acids Res 43(7):e47CrossRefGoogle Scholar
  15. 15.
    McCulloch WS, Pitts W (1943) Bull Math Biophys 5:115.  https://doi.org/10.1007/BF02478259 MathSciNetCrossRefGoogle Scholar
  16. 16.
    Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386CrossRefGoogle Scholar
  18. 18.
    Werbos PJ (1982) Applications of advances in nonlinear sensitivity analysis. System modeling and optimization. Springer, Berlin, pp 762–770CrossRefGoogle Scholar
  19. 19.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436CrossRefGoogle Scholar
  20. 20.
    Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5(4):373–401CrossRefGoogle Scholar
  21. 21.
    Sordo M (2002) Introduction to neural networks in healthcare. Open Clin Knowl Manag Med CareGoogle Scholar
  22. 22.
    Sawicki MP, Samara G, Hurwitz M, Passaro E (1993) Human genome project. Am J Surg 165(2):258–264CrossRefGoogle Scholar
  23. 23.
    Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J et al (2010) Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 16(5):991–1006CrossRefGoogle Scholar
  24. 24.
    Dwivedi AK (2018) Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput Appl 29(12):1545–1554CrossRefGoogle Scholar
  25. 25.
    Vanitha CDA, Devaraj D, Venkatesulu M (2015) Gene expression data classification using support vector machine and mutual information-based gene selection. Proc Comput Sci 47:13–21CrossRefGoogle Scholar
  26. 26.
    Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17CrossRefGoogle Scholar
  27. 27.
    Garro BA, Rodríguez K, Vázquez RA (2016) Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl Soft Comput 38:548–560CrossRefGoogle Scholar
  28. 28.
    Chen YC, Ke WC, Chiu HW (2014) Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput Biol Med 48:1–7CrossRefGoogle Scholar
  29. 29.
    Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Meltzer PS (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(6):673CrossRefGoogle Scholar
  30. 30.
    Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Chen QR, Catchpoole D (2004) Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma. Can Res 64(19):6883–6891CrossRefGoogle Scholar
  31. 31.
    Cho SB, Won HH (2007) Cancer classification using ensemble of neural networks with multiple significant gene subsets. Appl Intell 26(3):243–250zbMATHCrossRefGoogle Scholar
  32. 32.
    Pal NR, Aguan K, Sharma A, Amari SI (2007) Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering. BMC Bioinform 8(1):5CrossRefGoogle Scholar
  33. 33.
    Takahashi M, Hayashi H, Watanabe Y, Sawamura K, Fukui N, Watanabe J, Hori T (2010) Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures. Schizophr Res 119(1–3):210–218CrossRefGoogle Scholar
  34. 34.
    Huang X, Liu H, Li X, Guan L, Li J, Tellier LCAM, Zhang J (2018) Revealing Alzheimer’s disease genes spectrum in the whole-genome by machine learning. BMC Neurol 18(1):5CrossRefGoogle Scholar
  35. 35.
    Logotheti M, Pilalis E, Venizelos N, Kolisis F, Chatziioannou A (2016) Studying microarray gene expression data of schizophrenic patients for derivation of a diagnostic signature through the aid of machine learning. Biom Biostat Int J 4(5):00106Google Scholar
  36. 36.
    Zhang H, Xie Z, Yang Y, Zhao Y, Zhang B, Fang J (2017) The correlation-base-selection algorithm for diagnostic schizophrenia based on blood-based gene expression signatures. BioMed Res Int 2017, Article ID 7860506.  https://doi.org/10.1155/2017/7860506 Google Scholar
  37. 37.
    Oh DH, Kim IB, Kim SH, Ahn DH (2017) Predicting autism spectrum disorder using blood-based gene expression signatures and machine learning. Clin Psychopharmacol Neuro Sci Off Sci J Korean Coll Neuropsychopharmacol 15(1):47–52Google Scholar
  38. 38.
    Karstoft KI, Galatzer-Levy IR, Statnikov A, Li Z, Shalev AY (2015) Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry 15(1):30CrossRefGoogle Scholar
  39. 39.
    Deeb SJ, Tyanova S, Hummel M, Schmidt-Supprian M, Cox J, Mann M (2015) Machine learning based classification of diffuse large B-cell lymphoma patients by their protein expression profiles. Mol Cell Proteom 14(11):2947–2960CrossRefGoogle Scholar
  40. 40.
    Cai Z, Xu D, Zhang Q, Zhang J, Ngai SM, Shao J (2015) Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol BioSyst 11(3):791–800CrossRefGoogle Scholar
  41. 41.
    Alshamlan H, Badr G, Alohali Y (2015). mRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. BioMed Res Int 2015, Article ID 604910.  https://doi.org/10.1155/2015/604910 CrossRefGoogle Scholar
  42. 42.
    Zafeiris D, Rutella S, Ball GR (2018) An artificial neural network integrated pipeline for biomarker discovery using Alzheimer’s disease as a case study. Comput Struct Biotechnol J 16:77–87CrossRefGoogle Scholar
  43. 43.
    Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14:177–184CrossRefGoogle Scholar
  44. 44.
    Li S, Todor A, Luo R (2016) Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 14:1–7CrossRefGoogle Scholar
  45. 45.
    Lin X, Zhao Y, Song WM, Zhang B (2015) Molecular classification and prediction in gastric cancer. Comput Struct Biotechnol J 13:448–458CrossRefGoogle Scholar
  46. 46.
    Rapisuwon S, Vietsch EE, Wellstein A (2016) Circulating biomarkers to monitor cancer progression and treatment. Comput Struct Biotechnol J 14:211–222CrossRefGoogle Scholar
  47. 47.
    Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I (2017) Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 15:104–116CrossRefGoogle Scholar
  48. 48.
    Bartel J, Krumsiek J, Theis FJ (2013) Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J 4(5):e201301009CrossRefGoogle Scholar
  49. 49.
    Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI (2017) Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques. Comput Struct Biotechnol J 15:26–47CrossRefGoogle Scholar
  50. 50.
    Sudha M (2017) Evolutionary and neural computing based decision support system for disease diagnosis from clinical data sets in medical practice. J Med Syst 41(11):178CrossRefGoogle Scholar
  51. 51.
    Razmjooy N, Sheykhahmad FR, Ghadimi N (2018) A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Med 13(1):9–16CrossRefGoogle Scholar
  52. 52.
    Yu D, Wang Y, Liu H, Jermsittiparsert K, Razmjooy N (2019) System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Rep 5:1365–1374CrossRefGoogle Scholar
  53. 53.
    Namadchian A, Ramezani M, Razmjooy N (2016) A new meta-heuristic algorithm for optimization based on variance reduction of guassian distribution. Majlesi J Electr Eng 10(4):49Google Scholar
  54. 54.
    Razmjooy N, Ramezani M (2016) Training wavelet neural networks using hybrid particle swarm optimization and gravitational search algorithm for system identification. Int J Mechatron Electr Comput Technol 6(21):2987–2997Google Scholar
  55. 55.
    Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. J Control Autom Electr Syst 27(4):419–440CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia

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