Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis


  • Ayan Mukherji Department of Information Technology, Haldia Institute of Technology, India
  • Arindam Mondal Department of Electrical and Electronics Engineering, Pailan College of Management Technology, India
  • Rajib Banerjee Department of Electronics and Communication Engineering, Dr. B.C. Roy Engineering College, India
  • Saurav Mallik Department of Environmental Health, Harvard T H Chan School of Public Health, USA



deep learning, DNA methylation, consecutive clustering, differentially methylated region (DMR), supervised and unsupervised DMR finding algorithm, power


Background: Consecutive Clustering is one type of learning method that is built on neural network. It is frequently used in different domains including biomedical research. It is very useful for consecutive clustering (adjacent clustering). Adjacent clustering is highly used where there are various specific locations or addresses denoting each individual features in the data that need to be grouped consecutively. One of the useful consecutive clustering in the field of biomedical research is differentially methylated region (DMR) finding analysis on various CpG sites (features).

Method: So far, many researches have been carried out on deep learn- ing and consecutive clustering in biomedical domain. But for epigenetics study, very limited survey papers have been published till now where con- secutive clustering has been demonstrated together. Hence, in this study, we contributed a comprehensive survey on several fundamental categories of consecutive clustering, e.g., Convolutional Neural Network(CNN) Auto- Encoder (AE), Restricted Boltzmann Machines (RBM) and Deep Belief Net- work (DBN), Recurrent Neural Network (RNN), Deep Stacking Networks (DSN),  Long  Short  Term  Memory  (LSTM)  /  Gated  Recurrent  Unit  (GRU) Network etc., along with their applications, advantages and disadvantages. Different forms of consecutive clustering algorithms are covered in the second section (viz., supervised and unsupervised DMR finding methods) used for DNA methylation data have been described here along with their advantages, shortcomings and overall performance estimation (power, time).

Conclusion: Our survey paper provides a latest research work that have been done for consecutive clustering algorithms for healthcare purposes. All the usages, benefits and shortcomings along with their performance evaluation of each algorithm has been elaborated in our manuscript by which new biomedical researchers can understand and use those tools and algorithms for their research prospective.


Received: 21 October 2022 | Revised: 13 December 2022 | Accepted: 21 December 2022


Conflict of Interest

Sarauv Mallik is an associate editor for Artificial Intelligence and Applications, and was not involved in the editorial review or the decision to publish this article. The author declares that he/she has no conflicts of interest to this work.


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How to Cite

Mukherji, A. ., Mondal, A. ., Banerjee, R. ., & Mallik, S. . (2022). Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis. Artificial Intelligence and Applications.



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