Название: Artificial Intelligence in Bioinformatics and ChemoinformaticsАвтор: Yashwant V. Pathak, Surovi Saikia, Sarvadaman Pathak
Издательство: CRC Press
Год: 2024
Страниц: 275
Язык: английский
Формат: pdf (true)
Размер: 14.7 MB
The authors aim to shed light on the practicality of using machine learning in finding complex chemoinformatics and bioinformatics applications as well as identifiying AI in biological and chemical data points. The chapters are designed in such a way that they highlight the important role of Artificial Intelligence (AI) in chemistry and bioinformatics particularly for the classification of diseases, selection of features and compounds, dimensionality reduction and more. In addition, they assist in the organization and optimal use of data points generated from experiments performed using AI techniques. This volume discusses the development of automated tools and techniques to aid in research plans.
Machine Learning (ML) is typically considered to be a branch of Artificial Intelligence. Today computers are able to make decisions on their own without any outside assistance support using ML algorithms. Finding valuable underlying patterns inside complex data allows for the making of such decisions. The importance of ML is the practise of using statistical methods to help computer systems “learn” and “improve” from data in order to make accurate predictions without pattern recognition.
Machine learning techniques teach computers to manage data more productively. Sometimes even after obtaining the data, we are unable to elucidate or extrapolate the results. In that case, we apply ML. With the knowledge gained from these data, a range of application scenarios in the relevant disciplines can be built. For instance, the appropriate data in cybersecurity can be used to create an intelligent and automated cybersecurity system. Using the pertinent mobile data, it is possible to create individualised, situationally smart mobile applications. To lay the groundwork for practical applications, it is imperative to create tools for data management and procedures that can swiftly and shrewdly extract insights or valuable knowledge from data.
Features:
Covers AI applications in bioinformatics and chemoinformatics
Demystifies the involvement of AI in generating biological and chemical data
Provides an Introduction to basic and advanced chemoinformatics computational tools
Presents a chemical biology based toolset for artificial intelligence usage in drug design
Discusses computational methods in cancer, genome mapping, and stem cell research
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