Special Issue "Fuzzy Systems in Bioinformatics and Computational Biology"
A special issue of Biology (ISSN 2079-7737).
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 818
Interests: Internet of Things; big data analytics; machine learning; health data analytics
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Interests: blockchain technology; cryptography; big data; data mining; social networks; security and privacy; anonymity; graphs
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A fuzzy system can be defined as a mathematical system that takes analog values as input and analyzes the input values as logical entities which take continuous values between 0 and 1. Unlike digital logic, which is discrete, fuzzy logic is continuous in nature. In other words, there is no logic for detecting absolute true and absolute false values. In general, fuzzy logic architecture comprises a rule base which is a set of rules and conditions, fuzzy logic which converts the input analog entries into fuzzy sets, an inference engine which determines which rule from the rule set should be adopted, and de-fuzzy logic to decide the absolute value pertaining to the previously applied fuzzy logic. A fuzzy system works successfully even with noisy and distorted data. Fuzzy systems are predominantly deployed to provide precise solutions to complex problems which involve automatic decision-making capabilities. Fuzzy systems are deployed across several domains, ranging from aerospace engineering for satellite and aircraft control, automobiles for speed and traffic control, in the chemical industry for distillation processes, in artificial-intelligence-based applications such as natural language processing, in video analytics and processing, in expert systems for decision making, and also in the field of bioinformatics and computational biology.
Bioinformatics is a multidisciplinary field which involves genetics, biology, mathematics, computer science, and statistics. A bioinformatics solution is comprised of developing a computational algorithm for modeling and analyzing biological processes at a molecular level. This solution is reached by collecting the statistical data from raw biological data, using which a conceptual model is built. This is followed by solving a computational problem and testing the solution with different strategies. Computational Biology is the study of how computational models can be used to study biological systems using different types of experiments. The most important step in computational biology is to frame the biological problem. Several bioinformatics and computational-biology-based applications such as gene expression analysis, cellular reconstruction, medical image processing, protein structure analysis, and medical data classification use fuzzy systems to provide appropriate solutions and decisions.
Though fuzzy systems are considered as one of the better approaches to provide the best solutions to biologically inspired problems, the main challenge is that there is no systematic manner to explain the solution to a problem. Similarly, arriving at a proof for a problem is again difficult to show and requires solving mathematical expressions to back the results. Additionally, since fuzzy systems work with inconsistent data, the results produced by these systems may also be considered as inconsistent.
This Special Issue on “Fuzzy Systems in Bioinformatics and Computational Biology” provides a perfect platform for sharing novel and innovative ideas related to the design and development of fuzzy systems that aid in providing bioinformatics solutions to biological problem statements. It also serves as a perfect forum to discuss the possibilities of analyzing the need and exploring new opportunities for using fuzzy systems to solve new biological problem statements. The following topics are welcome, but the scope is not restricted to these:
- Fuzzy systems for sequence analysis in bioinformatics;
- Fuzzy systems for protein structure analysis;
- Fuzzy systems for structure prediction and three-dimensional structure analysis;
- Gene expression analysis using fuzzy systems;
- DNA and protein cell function analysis using fuzzy systems;
- Modelling of biological networks using fuzzy systems;
- Gene regulatory network analysis using fuzzy systems;
- Fuzzy-system-based graphical modeling for biological systems;
- Fuzzy-system-based data modeling;
- Fuzzy system in cancer computational biology;
- Fuzzy-system-based computational neuropsychiatry;
- Fuzzy-system-based computational neuroscience;
- Fuzzy-system-based anatomy;
- Fuzzy-system-based computational genetics and behavioral study;
- Fuzzy-system-based patient monitoring;
- Fuzzy-system-based organ functionality study and analysis;
- Fuzzy-system-based brain model development;
- Fuzzy-system-based tools and software analysis for bioinformatics;
- Fuzzy-system-based mathematical modeling for biological problem statements;
- Fuzzy-system-based molecular study.
Dr. Gautam Srivastava
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