GUEST EDITORS:

* Dr. S. Arumugam (Lead Guest Editor), Kalasalingam Academy of Research and Education, India
* Dr. Mehrdad Jalali, Karlsruhe Institute of Technology, Germany
* Prof. Ing. Vincenzo Moscato, University of Naples "Federico II", Italy

DESCRIPTION:

The emerging research area that deals with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery have led to an emerging field of research called network medicine, also termed biological networks. Analyzing such an arbitrary network gives an innovative understanding of basic mechanisms in controlling normal cellular systems and disease pathologies.
Every heap of data in its natural form embodies ambiguous patterns. These data patterns can be categorized or classified by using pattern recognition (PR) technology. This PR is a machine-learning approach that automatically detects patterns from datasets, and then distinguishes groups to characterize new data. The algorithm for this PR technology needs to be developed with the capability to deal with various data types, along with scalability and robustness for the missing and available data. Moreover, these algorithms need to work in limited storage and bandwidth based on compressed or summarized data.

Though there are many predefined libraries of image-processing algorithms available, still pattern recognition pretends to be a more complex process in data analysis. Its cost of development, implementation, maintenance, and fidelity pose to be more complicated with its inherent recognition problems. Such limitations can be overcome by incorporating fuzzy logic systems in the pattern recognition process. This Fuzzy logic system which comes under the broad classification of artificial intelligence plays a major role in approximation mechanisms and inference. A fuzzy system rationalizes data using a collection of fuzzy membership functions and rules. It is more powerful in handling non-linear, time-varying, adaptive systems. This system helps in the efficient and prompt diagnosis of health and biological networks. Many researchers and developers are working on developing such a system.

Topics of interest include, but are not limited to:
- Real-time pattern recognition of medical imaging
- Residual learning CNN model for medical image pattern recognition
- Minimum spanning tree (MST) and clustering algorithm in biological networks
- Application of network hierarchy structure and artificial neural network in biological networks
- Efficient deep learning algorithm in the mining of biological networks data information
- Role of network topology and protein attribute information with fuzzy-based clustering framework
- Backpropagation neural network-fuzzy classifier for diagnosis in secure connected biological networks
- Limitations associated with analytical methods in biological networks
- Network medicine applications in drug repurposing algorithms
- Fuzzy logic algorithms for intelligent decision-making and cyber security analysis
- Multivariate pattern analysis for connected biological networks

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