Genomic signal processing (GSP) is a functioning exploration area of recent times and a settled technique of digital signal processing for gathering information from genomic sequences. The recognition and identification of biological signals and analysis of sequences are the fundamental objectives of using GSP. Microarray data are typically used in GSP; microarray study decides genes that cause a specific disease and helps in anticipating and diagnosing a disease, and characterization of diseases. Microarray information is incredible innovation where information handled to an enormous number with plenty of genes. Recent research works show that microarray handling will be helpful for the classification of cancer genes. Different machine learning and artificial intelligence techniques are likewise used to distinguish the tumours and cancer cells. In this examination, the genomic signal processing is carried out utilizing cluster-fuzzy adaptive networking techniques. The major purpose of this research work is to evaluate the microarray data sets for recognizing the cancer genes. The microarray data set is generated using leukaemia, colon, prostate, breast cancer and lymphoma. Initially, the noise in the microarray is filtered and smoothened by utilizing a Kalman filter followed by an optimal clustering technique such as grid density-based clustering that is applied for clustering the microarray data sets. The clustered data of microarray are classified by adaptive neuro fuzzy interference system (ANFIS) for gene sequencing process of cancer identification. The adaptive network systems are developed based on autonomous networking concepts to change the static system into a dynamic. The efficiency of clustering is evaluated in terms of cluster indexes namely partition entropy, partition coefficient, Xie and Beni. The presented ANFIS is assessed in terms of precision, accuracy, recall, sensitivity, F-score and specificity. The proposed initiated methodology is mathematically designed and executed in the MATLAB platform and run for various test runs. During the implementation, the performance of cluster and classification efficiency of proposed techniques are compared with the existing strategies like fuzzy c-means with ANN and density-based clustering with ANN, respectively. Ultimately, the performance outcomes demonstrated that the proposed method can provide effective and optimal classification and identification of microarray cancer genes through genomic signal processing than the conventional methods, respectively.
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Abdullah M, Eldin H, Al-Moshadak T, Alshaik R, Al-Anesi I (2015) Density grid-based clustering for wireless sensors networks. Procedia Comput Sci 65:35–47
Ahmadlou M et al (2018) Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int 34:1–21
Akhlaghi S, Zhou N and Huang Z (2017) Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation. In: 2017 IEEE power and energy society general meeting
Amini A, Wah T (2012) On density-based clustering algorithms over evolving data streams: a summarization paradigm. Appl Mech Mater 263–266:2234–2237
Analytical BS, Barretos CH, Cancer Genome Atlas Research Network (2017) Integrated genomic and molecular characterization of cervical cancer. Nature 543(7645):378
Aravanis A, Lee M, Klausner R (2017) Next-generation sequencing of circulating tumor DNA for early cancer detection. Cell 168(4):571–574
Borrayo E, Mendizabal-Ruiz E, Vélez-Pérez H, Romo-Vázquez R, Mendizabal A, Morales J (2014) Genomic signal processing methods for computation of alignment-free distances from DNA sequences. PLoS ONE 9(11):e110954
Boyacioglu AM, Avci D (2010) An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Syst Appl 37(12):7908–7912
Chandrakar N (2016) Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers. Korean J Chem Eng 33(4):1318–1324
Chauhan N, Cho B-J (2019) Performance analysis of classification techniques of human brain MRI images. Int J Fuzzy Log Intell Syst 19(4):315–322
Chen D, Lin Y, Zhou Y, Chen M, Wen D (2017) Dislocation substructures evolution and an adaptive-network-based fuzzy inference system model for constitutive behaviour of a Ni-based superalloy during hot deformation. J Alloys Compd 708:938–946
Chinnaswamy A, Srinivasan R (2015) Hybrid feature selection using correlation coefficient and particle swarm optimization on microarray gene expression data. Adv Intell Syst Comput 1(1):229–239
Choudhry M, Kapoor R (2016) Performance analysis of fuzzy C-means clustering methods for MRI image segmentation. Procedia Comput Sci 89:749–758
Garro BA, Rodríguez K, Vázquez RA (2016) Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl Soft Comput 38:548–560
Harvey Simeon B, Ji S-Y (2017) Cloud-scale genomic signals processing for robust large-scale cancer genomic microarray data analysis. IEEE J Biomed Health Inf 21(1):238–245
Hira Z, Gillies D (2015) A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinform 2015:1–13
Li Y et al (2018) The p53–Mdm2 regulation relationship under different radiation doses based on the continuous–discrete extended Kalman filter algorithm. Neurocomputing 273:230–236
Libbrecht MW, Noble WS (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16(6):321–332
Mel’nikov SM, ten Hoorn JWMS, Eijkelenboom APAM (2004) Effect of phytosterols and phytostanols on the solubilization of cholesterol by dietary mixed micelles: an in vitro study. Chem Phys Lipids 127(2):121–141
Min SLB, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851–869
Mishra P, Bhoi N (2019) Microarray filtering-based fuzzy C-means clustering and classification in genomic signal processing. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-03945-0
Nanda JS, Panda G (2015) Design of computationally efficient density-based clustering algorithms. Data Knowl Eng 95:23–38
Naseem TM et al (2017) Preprocessing and signal processing techniques on genomic data sequences. Biomed Res 28:1
Nino-Ruiz ED, Sandu A (2017) Efficient parallel implementation of DDDAS inference using an ensemble Kalman filter with shrinkage covariance matrix estimation. Clust Comput 22:1–11
Podolsky M, Barchuk A, Kuznetcov V, Gusarova N, Gaidukov V, Tarakanov S (2016) Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels. Asian Pac J Cancer Prev 17(2):835–838
Raza K, Alam M (2016) Recurrent neural network based hybrid model for reconstructing gene regulatory network. Comput Biol Chem 64:322–334
Rebollo J et al (2017) Gene expression profiling of tumors from heavily pretreated patients with metastatic cancer for the selection of therapy: a pilot study. Am J Clin Oncol 40(2):140–145
Saito T, Rehmsmeier M (2017) Precrec: fast and accurate precision–recall and ROC curve calculations in R. Bioinformatics 33(1):145–147
Sasikala S, Balamurugan S, Geetha S (2015) A novel feature selection technique for improved survivability diagnosis of breast cancer. Procedia Comput Sci 50:16–23
Sharma M (2012) Brain tumor segmentation using hybrid genetic algorithm and artificial neural network fuzzy inference system (ANFIS). Int J Fuzzy Log Syst 2(4):31–42
Tirumala S, Narayanan A (2016) Attribute selection and classification of prostate cancer gene expression data using artificial neural networks. In: Cao H, Li J, Wang R (eds) Lecture notes in computer science. Springer, Cham, pp 26–34
Wang L, Wang Y, Chang Q (2016) Feature selection methods for big data bioinformatics: a survey from the search perspective. Methods 111:21–31
Wiharto ES, Susilo M (2019) The hybrid method of SOM artificial neural network and median thresholding for segmentation of blood vessels in the retina image fundus. Int J Fuzzy Log Intell Syst 19(4):323–331
Xu X, Ding S, Du M, Xue Y (2016) DPCG: an efficient density peaks clustering algorithm based on grid. Int J Mach Learn Cybern 9(5):743–754
Xue B, Zhang M, Browne W, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626
Yue S et al (2016) A new validity index for evaluating the clustering results by partitional clustering algorithms. Soft Comput 20(3):1127–1138
Zhang L et al (2017) Cancer progression prediction using gene interaction regularized elastic net. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 14(1):145–154
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Authors Purnendu Mishra and Dr. Nilamani Bhoi declare that they have no conflict of interest.
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Mishra, P., Bhoi, N. Genomic signal processing of microarrays for cancer gene expression and identification using cluster-fuzzy adaptive networking. Soft Comput (2020). https://doi.org/10.1007/s00500-020-05068-3
- Genomic signal processing
- Microarray data
- Kalman filter
- Grid density-based clustering
- Fuzzy interference system and partition coefficient