Institute of Food Research, Norwich, U.K. Methods in Molecular Biology Series, Volume 24. First of a two-part practical aid to the researcher who uses computers for the acquisition, storage, or analysis of nucleic acid or protein sequences. Plastic comb binding. 11 contributors, 3 U.S.
Computer Analysis of Sequence Data Part II (Methods in Molecular Biology)
โ Scribed by Annette M. Griffin, Hugh G. Griffin
- Publisher
- Humana Press
- Year
- 1994
- Tongue
- English
- Leaves
- 424
- Series
- Methods in Molecular Biology 025
- Edition
- 1st
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Institute of Food Research, Norwich, U.K. Methods in Molecular Biology Series, Volume 25. Second volume completing a practical aid for nucleic acid sequence researchers who use computers to acquire, store, or analyze their data. Plastic comb binding. 15 contributors, 5 U.S.
๐ SIMILAR VOLUMES
<p><span>This second edition provides new and updated chapters from expert researchers in the field detailing methods used to study the multi-facet deep sequencing data field. Chapters guide readers through techniques for processing RNA-seq data, microbiome analysis, deep learning methodologies, and
<span>This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volum
This guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumption
This guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumption