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Data Mining Techniques for the Life Sciences

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Cover of 'Data Mining Techniques for the Life Sciences'

Table of Contents

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    Book Overview
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    Chapter 1 Data Mining Techniques for the Life Sciences
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    Chapter 2 Protein Structure Databases.
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    Chapter 3 The MIntAct Project and Molecular Interaction Databases.
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    Chapter 4 Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
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    Chapter 5 Classification and Exploration of 3D Protein Domain Interactions Using Kbdock.
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    Chapter 6 Data Mining of Macromolecular Structures.
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    Chapter 7 Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.
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    Chapter 8 Homology-Based Annotation of Large Protein Datasets.
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    Chapter 9 Data Mining Techniques for the Life Sciences
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    Chapter 10 Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps of Protein Assemblies Using Evolutionary Information from Aligned Homologous Proteins.
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    Chapter 11 Systematic Exploration of an Efficient Amino Acid Substitution Matrix: MIQS.
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    Chapter 12 Promises and Pitfalls of High-Throughput Biological Assays.
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    Chapter 13 Data Mining Techniques for the Life Sciences
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    Chapter 14 Predicting Conformational Disorder.
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    Chapter 15 Classification of Protein Kinases Influenced by Conservation of Substrate Binding Residues.
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    Chapter 16 Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence.
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    Chapter 17 Protein Crystallizability.
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    Chapter 18 Data Mining Techniques for the Life Sciences
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    Chapter 19 Data Mining Techniques for the Life Sciences
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    Chapter 20 Functional Analysis of Metabolomics Data.
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    Chapter 21 Data Mining Techniques for the Life Sciences
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    Chapter 22 A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.
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    Chapter 23 Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
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    Chapter 24 Protein Residue Contacts and Prediction Methods.
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    Chapter 25 The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.
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    Chapter 26 Data Mining Techniques for the Life Sciences
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    Chapter 27 Data Mining Techniques for the Life Sciences
Attention for Chapter 12: Promises and Pitfalls of High-Throughput Biological Assays.
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Chapter title
Promises and Pitfalls of High-Throughput Biological Assays.
Chapter number 12
Book title
Data Mining Techniques for the Life Sciences
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3572-7_12
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Greg Finak, Raphael Gottardo

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

This chapter discusses some of the pitfalls encountered when performing biomedical research involving high-throughput "omics" data and presents some strategies and guidelines that researchers should follow when undertaking such studies. We discuss common errors in experimental design and data analysis that lead to irreproducible and non-replicable research and provide some guidelines to avoid these common mistakes so that researchers may have confidence in study outcomes, even if the results are negative. We discuss the importance of ranking and prespecifying hypotheses, performing power analysis, careful experimental design, and preplanning of statistical analyses in order to avoid the "fishing expedition" data analysis strategy, which is doomed to fail. The impact of multiple testing on false-positive rates is discussed, particularly in the context of the analysis of high-throughput data, and methods to correct for it are presented, as well as approaches to detect and correct for experimental biases and batch effects, which often plague high-throughput assays. We highlight the importance of sharing data and analysis code to facilitate reproducibility and present tools and software that are appropriate for this purpose.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 35%
Lecturer 2 12%
Student > Ph. D. Student 2 12%
Professor 1 6%
Student > Doctoral Student 1 6%
Other 2 12%
Unknown 3 18%
Readers by discipline Count As %
Computer Science 3 18%
Chemistry 3 18%
Agricultural and Biological Sciences 2 12%
Biochemistry, Genetics and Molecular Biology 2 12%
Nursing and Health Professions 1 6%
Other 4 24%
Unknown 2 12%