Artificial Intelligence Technologies for Computational Biology

This text emphasizes the importance of artificial intelligence techniques in biological computation and discusses fundamental principles that can be applied beyond bio-inspired computing. It covers topics such as data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The book is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology.

Format: Hardback
Length: 324 pages
Publication date: 10 November 2022
Publisher: Taylor & Francis Ltd

The importance of artificial intelligence techniques in the field of biological computation cannot be overstated. These techniques have revolutionized the way we analyze and interpret biological data, enabling us to gain insights into complex biological processes and systems. In this comprehensive text, we will explore the fundamental principles that can be applied beyond bio-inspired computing, covering a wide range of important topics.

Data Integration and Mining:

Data integration and mining are critical components of biological computation. They involve the merging and analysis of large datasets from different sources, such as genomics, proteomics, and environmental data. By combining these datasets, researchers can identify patterns, relationships, and trends that would otherwise be hidden. Data integration and mining techniques include techniques such as data warehousing, data mining, and machine learning.

Machine Learning:

Machine learning is a subset of artificial intelligence that involves the use of algorithms to learn from data. It has been widely used in biological computation for tasks such as classification, regression, and prediction. Machine learning algorithms can be trained on large datasets to identify patterns and make predictions based on new data. Some popular machine learning algorithms in biological computation include neural networks, support vector machines, and random forests.

Genetic Algorithms:

Genetic algorithms are optimization algorithms that mimic the process of natural selection. They are used in biological computation for tasks such as gene sequence optimization, protein structure prediction, and evolutionary simulation. Genetic algorithms work by iteratively generating new solutions based on the fitness of existing solutions. They have been successful in solving complex optimization problems in biology.

Evolutionary Computation:

Evolutionary computation is a branch of artificial intelligence that simulates the process of natural selection. It is used in biological computation for tasks such as protein structure prediction, gene sequence optimization, and evolutionary simulation. Evolutionary computation algorithms work by iteratively generating new solutions based on the fitness of existing solutions. They have been successful in solving complex optimization problems in biology.

Evolved Neural Networks:

Evolved neural networks are neural networks that have been trained on a dataset of biological data. They have been used in biological computation for tasks such as image recognition, pattern recognition, and decision-making. Evolved neural networks work by learning from the data and adapting to new situations. They have been successful in solving complex problems in biology.

Nature-Inspired Algorithms:

Nature-inspired algorithms are algorithms that are inspired by natural processes. They have been used in biological computation for tasks such as optimization, search, and pattern recognition. Nature-inspired algorithms work by mimicking the behavior of natural systems, such as biological organisms, ecosystems, and social networks. They have been successful in solving complex problems in biology.

Protein Structure Alignment:

Protein structure alignment is a critical task in biological computation. It involves aligning the structures of two or more proteins to determine their similarities and differences. Protein structure alignment is used in tasks such as drug discovery, protein function prediction, and protein-protein interaction analysis. There are several algorithms used in protein structure alignment, including threading, homology modeling, and structure-based modeling.

Applications of Biological Computation:

Biological computation has numerous applications in various fields, including medicine, agriculture, and environmental science. In medicine, biological computation is used to analyze medical images, such as CT scans and MRI images, to diagnose diseases and predict treatment outcomes. In agriculture, biological computation is used to optimize crop yields, predict pest outbreaks, and develop new crop varieties. In environmental science, biological computation is used to model ecosystems, predict climate change, and develop sustainable land management practices.

Conclusion:

In conclusion, artificial intelligence techniques in biological computation have opened up new avenues for analyzing and interpreting biological data. These techniques have enabled us to gain insights into complex biological processes and systems, and have applications in various fields. As the field of biological computation continues to evolve, we can expect to see new and innovative techniques emerge that will further enhance our understanding of biology and improve our ability to address important societal challenges.

Weight: 800g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032160009