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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniq

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas;

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Statistical Modelling of Molecular Descriptors in QSAR/QSPR

processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Statistical Bioinformatics with R

examples aid teaching and learning presented at the right levelBayesian methods and the modern multiple testing principles in one;

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Machine Learning Theoretical Foundations and Practical Applications

in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;

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Introduction to Machine Learning and Bioinformatics

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and;

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Introduction to Machine Learning and Bioinformatics

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and;

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Statistical and Machine Learning Approaches for Network Analysis

weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross;

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Machine Learning in Bioinformatics

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are;

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Data Analytics in Bioinformatics

becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines;

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Understanding Machine Learning

Machine learning is a branch of computer science concerned with the application of statistical techniques to improve performance;

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Introduction to Machine Learning with R

learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear;

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Integrated Uncertainty in Knowledge Modelling and Decision Making

and decision support; econometrics; machine learning; machine learning applications; and statistical methods.;

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Statistical Modelling in Biostatistics and Bioinformatics

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The;

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Statistical Modelling in Biostatistics and Bioinformatics

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The;

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Integrated Uncertainty in Knowledge Modelling and Decision Making

uncertainty management and decision support; machine learning; machine learning applications; econometric applications; and statistical methods;

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Machine Learning Approaches To Bioinformatics

This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key;

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Applied Directional Statistics

This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more;

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Immunoinformatics of Cancers

Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R takes a bioinformatics approach to understanding and;

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Bioinformatics Research and Applications

interaction; HPC and CryoEM; machine and deep learning; data analysis and methodology; analysis and visualization tools; and RNA-Seq data analysis.;

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Evolutionary Computation Machine Learning and Data Mining in Bioinformatics

This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining;

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Training Systems Using Python Statistical Modeling

, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive;

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Data Analytics, Bioinformatics, and Machine Learning

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Applied Soft Computing

, hybrid models, machine learning, meta heuristics, neuro fuzzy system, and optimization principles. The book acts as a reference book for AI;

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The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the;

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Bioinformatics

Bioinformatics is an interdisciplinary science that develops on the methods and principles of statistics, computing, mathematics and;

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Applications of Machine Learning

interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.;

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