Moses Statistical Modeling and Machine Learning for Molecular Biology

1. Auflage 2016
ISBN: 978-1-4822-5862-2
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 280 Seiten

Reihe: Chapman & Hall/CRC Mathematical and Computational Biology

The goal of this introductory graduate textbook is to cover the statistical and computational methods needed by molecular biologists in order to analyze their own data without the help of a bioinformatics expert. The book covers several of the major data analysis techniques used to analyze data from high-throughput molecular biology and genomics experiments. It also explains the major concepts behind most of the popular techniques and examines some of the simpler techniques in detail.

Autoren/Hrsg.

Moses, Alan

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften
• Mathematik | Informatik Mathematik Stochastik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie

Inhaltsverzeichnis

Introduction. Statistical modeling. Statistics and probability. Multiple testing. Multivariate statistics and parameter estimation. Clustering. Distance-based. Gaussian mixture models. Simple linear regression. Multiple regression and generalized linear models. Regularization. Linear classification. Non-linear classification. Evaluating classifiers and ensemble methods. Correlated data in one dimension. Hidden-Markov models. Local regression.