Angielski
The volume and speed of scientific data has transformed the life sciences, leading to an acceleration in bioinformatics, computational biology, and quantitative biology. In turn, researchers in bioinformatics and computational biology now require a suite of computational, mathematical, and statistical skills in order to handle large amounts of data. Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognising protein interaction sites, and identifying protein drug targets. The book emphasizes novel approaches to protein-ligand interactions, including machine learning and deep learning, presenting a state-of-the-art suite of skills for researchers. The volume represents a resource for scientists, detailing the fundamentals of computational methods, showing how to use computational algorithms to study protein interaction data, and giving scientific explanations for biological data through computational intelligence. Fourteen chapters offer a comprehensive guide to protein interaction data, and computational intelligence methods for protein-ligand interactions, demonstrating ways computational tools and techniques can be applied for effective research on protein-ligand interaction. Presents a guide to computational techniques for protein-ligand interaction analysisGuides researchers in developing advanced computational intelligence methods for the protein-ligand problemIdentifies appropriate computational tools for various problemsDemonstrates the use of advanced techniques such as vector machine, neural networks, and machine learningOffers the computational, mathematical and statistical skills researchers need
