Statistics

Statistical Genetics & Bioinformatics

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Gene Expression Studies Using Affymetrix Microarrays

Hinrich Gohlmann, Willem Talloen
June 30, 2017

The Affymetrix GeneChip® system is one of the most widely adapted microarray platforms. However, due to the overwhelming amount of information available, many Affymetrix users tend to stick to the default analysis settings and may end up drawing sub-optimal conclusions. Written by a molecular...

Microarray Image Analysis: An Algorithmic Approach

Karl Fraser, Zidong Wang, Xiaohui Liu
June 14, 2017

To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to...

Meta-analysis and Combining Information in Genetics and Genomics

Rudy Guerra, Darlene R. Goldstein
June 14, 2017

Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-Analysis With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal...

Asymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems

Jiming Jiang
June 08, 2017

Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are extensively used in practice....

Algorithms for Next-Generation Sequencing

Wing-Kin Sung
May 24, 2017

Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before – as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data? Algorithms for...

Statistical Modeling and Machine Learning for Molecular Biology

Alan Moses
January 04, 2017

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts...

Applied Biclustering Methods for Big and High-Dimensional Data Using R

Adetayo Kasim, Ziv Shkedy, Sebastian Kaiser, Sepp Hochreiter, Willem Talloen
October 04, 2016

Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering...

Mixture Model-Based Classification

Paul D. McNicholas
August 19, 2016

"This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails...

Crop Breeding: Bioinformatics and Preparing for Climate Change

Santosh Kumar
July 15, 2016

This title includes a number of Open Access chapters. Climate change will severely impact the world’s food supply unless steps are taken to increase crop resilience. Otherwise, the negative effects on both the yield and the quality of crop plants are predicted to be immense. Plant genomics is a...

Understanding Enzymes: Function, Design, Engineering, and Analysis

Allan Svendsen
May 17, 2016

Understanding Enzymes: Function, Design, Engineering, and Analysis focuses on the understanding of enzyme function and optimization gained in the past decade, past enzyme function analysis, enzyme engineering, and growing insights from the simulation work and nanotechnology measurement of enzymes...

Gene-Environment Interaction Analysis: Methods in Bioinformatics and Computational Biology

Sumiko Anno
April 06, 2016

Gene–environment (G × E) interaction analysis is a statistical method for clarifying G × E interactions applicable to a phenotype or a disease that is the result of interactions between genes and the environment. This book is the first to deal with the theme of G × E interaction analysis. It...

Next-Generation Sequencing Data Analysis

Xinkun Wang
February 24, 2016

A Practical Guide to the Highly Dynamic Area of Massively Parallel Sequencing The development of genome and transcriptome sequencing technologies has led to a paradigm shift in life science research and disease diagnosis and prevention. Scientists are now able to see how human diseases and...

Conferences

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