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Data Mining Techniques for the Life Sciences

Overview of attention for book
Cover of 'Data Mining Techniques for the Life Sciences'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Data Mining Techniques for the Life Sciences
  3. Altmetric Badge
    Chapter 2 Protein Structure Databases.
  4. Altmetric Badge
    Chapter 3 The MIntAct Project and Molecular Interaction Databases.
  5. Altmetric Badge
    Chapter 4 Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
  6. Altmetric Badge
    Chapter 5 Classification and Exploration of 3D Protein Domain Interactions Using Kbdock.
  7. Altmetric Badge
    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.
  13. Altmetric Badge
    Chapter 12 Promises and Pitfalls of High-Throughput Biological Assays.
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    Chapter 13 Data Mining Techniques for the Life Sciences
  15. Altmetric Badge
    Chapter 14 Predicting Conformational Disorder.
  16. Altmetric Badge
    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.
  18. Altmetric Badge
    Chapter 17 Protein Crystallizability.
  19. Altmetric Badge
    Chapter 18 Data Mining Techniques for the Life Sciences
  20. Altmetric Badge
    Chapter 19 Data Mining Techniques for the Life Sciences
  21. Altmetric Badge
    Chapter 20 Functional Analysis of Metabolomics Data.
  22. Altmetric Badge
    Chapter 21 Data Mining Techniques for the Life Sciences
  23. Altmetric Badge
    Chapter 22 A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.
  24. Altmetric Badge
    Chapter 23 Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
  25. Altmetric Badge
    Chapter 24 Protein Residue Contacts and Prediction Methods.
  26. Altmetric Badge
    Chapter 25 The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.
  27. Altmetric Badge
    Chapter 26 Data Mining Techniques for the Life Sciences
  28. Altmetric Badge
    Chapter 27 Data Mining Techniques for the Life Sciences
Attention for Chapter 27: Data Mining Techniques for the Life Sciences
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

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3 tweeters

Citations

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7 Dimensions

Readers on

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Chapter title
Data Mining Techniques for the Life Sciences
Chapter number 27
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_27
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Popescu, George V, Noutsos, Christos, Popescu, Sorina C, George V. Popescu, Christos Noutsos, Sorina C. Popescu, Popescu, George V., Popescu, Sorina C.

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 13%
Lecturer 3 10%
Researcher 3 10%
Professor > Associate Professor 3 10%
Student > Master 3 10%
Other 8 27%
Unknown 6 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 40%
Computer Science 4 13%
Biochemistry, Genetics and Molecular Biology 3 10%
Psychology 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 8 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 December 2018.
All research outputs
#9,047,792
of 15,767,803 outputs
Outputs from Methods in molecular biology
#2,372
of 9,213 outputs
Outputs of similar age
#122,524
of 265,795 outputs
Outputs of similar age from Methods in molecular biology
#4
of 20 outputs
Altmetric has tracked 15,767,803 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,213 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 265,795 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.