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Statistical Modeling and Machine Learning for Molecular Biology

book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big;

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Statistical Modeling and Machine Learning for Molecular Biology

book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big;

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Gene Expression Data Analysis

for generating gene expression data. Background knowledge on statistical modeling and machine learning techniques. Detailed methodology;

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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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Signal Processing in Medicine and Biology

statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their;

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Introduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and;

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Computational Intelligence Methods for Bioinformatics and Biostatistics

molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for;

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Machine Learning for Vision-Based Motion Analysis: Theory and Techniques

application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions;

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Time Series Algorithms Recipes

. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems;

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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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Bioconductor Case Studies

; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis. Each chapter of this book describes;

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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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Machine Learning in Finance

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and;

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Machine Learning in Finance

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and;

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Intelligent Computing Applications for COVID-19

Modeling applications. This book provides insight into the recent advances of applications statistical methods and mathematical modeling for the;

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Advances in Bioinformatics and Computational Biology

; computational systems biology; computational proteomics; statistical analysis of molecular sequences; algorithms for problems in computational biology;

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Modeling and Analysis of Bio molecular Networks

modeling and analysis of bio-molecular networks, the book presents various sophisticated mathematical and statistical approaches. The life system;

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Modeling and Analysis of Bio molecular Networks

modeling and analysis of bio-molecular networks, the book presents various sophisticated mathematical and statistical approaches. The life system;

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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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Mathematical and Computational Oncology

organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research;

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Road Traffic Modeling and Management

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing;

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Case Applications of Statistical Models in Ecology & Evolution

using statistical and mathematical functions. In particular, statistical and mathematical models are a necessity for developing some sub;

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Extreme Value Theory-based Methods for Visual Recognition

in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian;

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Machine Learning and Systems Engineering

reference text for researchers and graduate students, working on machine learning and systems engineering.;

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Molecular Modeling at the Atomic Scale

Although molecular modeling has been around for a while, the groundbreaking advancement of massively parallel supercomputers and novel;

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Machine Learning Modeling for IoUT Networks

transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition;

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Microarray Bioinformatics

biology, computer science, and statistics, the volume explores machine learning methods such as clustering, feature selection, classification;

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