Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Evolutionary Intelligence
Published in: Evolutionary Intelligence 2/2021

10-01-2020 | Special Issue

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

Authors: S. Karthik, M. Sudha

Published in: Evolutionary Intelligence | Issue 2/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article A cognitive mechanism for mitigating DDoS attacks using the artificial immune system in a cloud environment
next article RETRACTED ARTICLE: Automatic alert generation in a surveillance systems for smart city environment using deep learning algorithm

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Patel KR, Cherian J, Gohil K, Atkinson D (2014) Schizophrenia: overview and treatment options. Pharm Therap 39(9):638
2.
Picchioni MM, Murray RM (2007) Schizophrenia. BMJ (Clin Res ed.) 335(7610):91–95
3.
Owen MJ, Sawa A, Mortensen PB (2016) Schizophrenia. Lancet (London, England) 388(10039):86–97
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):20–29
5.
Kerner B (2014) Genetics of bipolar disorder. Appl Clin Genet 7:33
6.
7.
Geddes JR, Miklowitz DJ (2013) Treatment of bipolar disorder. The Lancet 381(9878):1672–1682
8.
Hilty DM, Leamon MH, Lim RF, Kelly RH, Hales RE (2006) A review of bipolar disorder in adults. Psychiatry (Edgmont) 3(9):43
9.
Phillips ML, Kupfer DJ (2013) Bipolar disorder diagnosis: challenges and future directions. The Lancet 381(9878):1663–1671
10.
Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucl Acids Res 30(1):207–210
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):241–253
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):R80
13.
Gautier L, Cope L, Bolstad BM, Irizarry RA (2004) affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307–315
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):e47
15.
16.
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558MathSciNetMATH
17.
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386
18.
Werbos PJ (1982) Applications of advances in nonlinear sensitivity analysis. System modeling and optimization. Springer, Berlin, pp 762–770
19.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
20.
Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5(4):373–401
21.
Sordo M (2002) Introduction to neural networks in healthcare. Open Clin Knowl Manag Med Care
22.
Sawicki MP, Samara G, Hurwitz M, Passaro E (1993) Human genome project. Am J Surg 165(2):258–264
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):991–1006
24.
Dwivedi AK (2018) Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput Appl 29(12):1545–1554
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 47:13–21
26.
Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17
27.
Garro BA, Rodríguez K, Vázquez RA (2016) Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl Soft Comput 38:548–560
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 48:1–7
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):673
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):6883–6891
31.
Cho SB, Won HH (2007) Cancer classification using ensemble of neural networks with multiple significant gene subsets. Appl Intell 26(3):243–250MATH
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):5
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):210–218
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):5
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):00106
36.
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):47–52
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):30
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):2947–2960
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):791–800
41.
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 16:77–87
43.
Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14:177–184
44.
Li S, Todor A, Luo R (2016) Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 14:1–7
45.
Lin X, Zhao Y, Song WM, Zhang B (2015) Molecular classification and prediction in gastric cancer. Comput Struct Biotechnol J 13:448–458
46.
Rapisuwon S, Vietsch EE, Wellstein A (2016) Circulating biomarkers to monitor cancer progression and treatment. Comput Struct Biotechnol J 14:211–222
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 15:104–116
48.
Bartel J, Krumsiek J, Theis FJ (2013) Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J 4(5):e201301009
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 15:26–47
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):178
51.
Razmjooy N, Sheykhahmad FR, Ghadimi N (2018) A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Med 13(1):9–16
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 5:1365–1374
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):49
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):2987–2997
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):419–440
Metadata
Title
Predicting bipolar disorder and schizophrenia based on non-overlapping genetic phenotypes using deep neural network
Authors
S. Karthik
M. Sudha
Publication date
10-01-2020
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 2/2021
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-019-00346-y

Other articles of this Issue 2/2021

Evolutionary Intelligence 2/2021 Go to the issue

Special Issue

Particle swarm optimization with adaptive inertia weight based on cumulative binomial probability

Special Issue

Optimal feature selection through a cluster-based DT learning (CDTL) in heart disease prediction

Special Issue

Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN

Special Issue

Electroencephalogram for epileptic seizure detection using stacked bidirectional LSTM_GAP neural network

Special Issue

A novel weather prediction model using a hybrid mechanism based on MLP and VAE with fire-fly optimization algorithm

Special Issue

Multi-objective based deployment of throwboxes in Delay Tolerant Networks for the Internet of Things environment

Premium Partner

    Image Credits