Genomic Signal Processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

2022

• DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data, Grigoriadis, D., Perdikopanis, N., Georgakilas, G.K. et al., BMC Bioinformatics, 23(Suppl 2):395, https://doi.org/10.1186/s12859-022-04945-y, 2022 (G) (I)
• Natural Hierarchical Cluster Analysis by Nearest Neighbors with Near-Linear Time Complexity, K Gokcesu, H Gokcesu, preprint arXiv:2203.08027, 2022 (G)

• Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms, MZU Rahman, S Putluri, CRC Press, Taylor & Francis Group, 2021 (G)

• Volume 2 of Tecnicas de biologia molecular II, Milos Pawlowski, in Herramientas de biología molecular V, Cambridge stanford books, 2020 (G)

• Data analysis and presentation in flow cytometry, L Khowawisetsut, K Sukapirom, Kovit Pattanapanyasat, Science Asia, 44S:19–27, doi:10.2306/scienceasia1513-1874.2018.44S.019, 2018 (G) (I) (S)
• An Improved and More Scalable Evolutionary Approach to Multiobjective Clustering, Mario Garza-Fabre, Julia Handl, and Joshua Knowles, IEEE Transactions on Evolutionary Computation, 22(4):515-535, 2018 (G) (I) (S)

• An adaptive wavelet denoising and classification of multiclass cancer gene expression data, Navya S. Bhaskar ; T. Aswathy Devi, Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017 International Conference on, DOI: 10.1109/ICICICT1.2017.8342663, 2017 (G) (I) (S)
• Cloud-Scale Genomic Signals Processing for Robust Large-Scale Cancer Genomic Microarray Data Analysis, B Harvey, SY Ji, IEEE Journal of Biomedical and Health Informatics: 21(1):238-245, 2017 (G) (I) (S)
• Complex-Valued L1-Norm Principal Component Analysis for Signal Processing and Machine Learning, Nikolaos Tsagkarakis, PhD Thesis, State University of New York, 2017 (G)

• The Future of m-Health: Progress or Retrogression?, Robert S.H. Istepanian, Bryan Woodward, DOI: 10.1002/9781119302889.ch8, 2016 (G)
• New Prognostic Index to Detect the Severity of Asthma Automatically Using Signal Processing Techniques of Capnogram, Mohsen Kazemi, Aik Howe, Journal of Intelligent Procedures in Electrical Technology (JIPET) 7(26):53-63, 2016 (G)
• m-Health Computing: m-Health 2.0, Social Networks, Health Apps, Cloud, and Big Health Data, Chapter in book M-Health: Fundamentals and Applications: Fundamentals and Applications, Robert S.H. Istepanian, Bryan Woodward, DOI: 10.1002/9781119302889.ch3, 2016 (G) (S)
• Gene Expression and Profiling, Yu Zhou, Chao Xu, Jigang Zhang, Hong-Wen Deng, Chapter in Application of Clinical Bioinformatics, Volume 11 of the series Translational Bioinformatics, pp 59-82, 2016 (G)

• Implementation for prognostication of cancer using gene expression data, S. Sivakumar D. Viji, International Journal of Applied Engineering Research, 10(7):17519-17532, 2015 (S)

• An efficient approach for cancer prediction using genomic signal processing, T.M. Inbamalar, R. Sivakumar, International Review on Computers and Software 9(3):585-591, 2014 (S)
• EpiMiner: a three-stage co-information based method for detecting and visualizing epistatic interactions J Shang, J Zhang, Y Sun, Y Zhang, Digital Signal Processing, 24:1-13, 2014 (G) (I) (S)
• Cloud-scale genomic signals processing classification analysis for gene expression microarray data B Harvey, SY Ji, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014 (G) (I) (S)
• What can we learn with Signal Analysis about Genomic information?, Amitabh Chaturvedi, Archana Tiwari, IOSR Journal of Pharmacy and Biological Sciences (IOSR-JPBS), Volume 9, Issue 4 Ver. II, PP 45-60, 2014 (G)

• De-Noising, Clustering, Classification, and Representation of Microarray Data for Disease Diagnostics, Nitin Bharadwaj, Sheena Wadhwa, Pragya Goel, Isha Sethi, Chanpreet Singh Arora, Aviral Goel, Sonika Bhatnagar, Netaji Subhas, Harish Parthasarathy, Chapter In book: Research Developments in Computer Vision and Image Processing: Methodologies and Applications, Publisher: IGI global, Editors: Rajeev Srivastava, S. K. Singh and K. K. Shukla, pp.149-174, 2013 (S)

• Dynamic clustering of gene expression, L An, RW Doerge, International Scholarly Research Network, ISRN Bioinformatics, Volume 2012, Article ID 537217, doi:10.5402/2012/537217, 2012 (G)