The Resource Network inference in molecular biology : a hands-on framework, Jesse M. Lingeman, Dennis Shasha
Network inference in molecular biology : a hands-on framework, Jesse M. Lingeman, Dennis Shasha
Resource Information
The item Network inference in molecular biology : a hands-on framework, Jesse M. Lingeman, Dennis Shasha represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Network inference in molecular biology : a hands-on framework, Jesse M. Lingeman, Dennis Shasha represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
- Extent
- ix, 100 pages
- Contents
-
- 1. Overview of network inference
- 2. Step 1: clustering data
- 3. Step 2: use steady state data for network inference
- 4. Step 3: using time-series data
- 5. Step 4: pipelines
- Isbn
- 9781461431138
- Label
- Network inference in molecular biology : a hands-on framework
- Title
- Network inference in molecular biology
- Title remainder
- a hands-on framework
- Statement of responsibility
- Jesse M. Lingeman, Dennis Shasha
- Language
- eng
- Summary
- Annotation
- Cataloging source
- NLM
- http://library.link/vocab/creatorName
- Lingeman, Jesse M
- Illustrations
- illustrations
- Index
- index present
- LC call number
- QH324.2
- LC item number
- .L56 2012
- Literary form
- non fiction
- Nature of contents
- bibliography
- NLM call number
-
- 2012 G-275
- QU 450
- http://library.link/vocab/relatedWorkOrContributorName
- Shasha, Dennis Elliott
- Series statement
- SpringerBriefs in electrical and computer engineering,
- http://library.link/vocab/subjectName
-
- Bioinformatics
- Systems biology
- Molecular Biology
- Gene Regulatory Networks
- Genomics
- Systems Biology
- Summary expansion
- Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation.Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable
- Label
- Network inference in molecular biology : a hands-on framework, Jesse M. Lingeman, Dennis Shasha
- Bibliography note
- Includes bibliographical references (pages 97-98) and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- 1. Overview of network inference -- 2. Step 1: clustering data -- 3. Step 2: use steady state data for network inference -- 4. Step 3: using time-series data -- 5. Step 4: pipelines
- Control code
- 767568109
- Dimensions
- 24 cm
- Extent
- ix, 100 pages
- Isbn
- 9781461431138
- Lccn
- 2012939634
- Media category
- unmediated
- Media MARC source
- rdamedia
- Media type code
-
- n
- Other physical details
- illustrations
- System control number
- (OCoLC)767568109
- Label
- Network inference in molecular biology : a hands-on framework, Jesse M. Lingeman, Dennis Shasha
- Bibliography note
- Includes bibliographical references (pages 97-98) and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- 1. Overview of network inference -- 2. Step 1: clustering data -- 3. Step 2: use steady state data for network inference -- 4. Step 3: using time-series data -- 5. Step 4: pipelines
- Control code
- 767568109
- Dimensions
- 24 cm
- Extent
- ix, 100 pages
- Isbn
- 9781461431138
- Lccn
- 2012939634
- Media category
- unmediated
- Media MARC source
- rdamedia
- Media type code
-
- n
- Other physical details
- illustrations
- System control number
- (OCoLC)767568109
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Network-inference-in-molecular-biology--a/tok4J5Yum7M/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Network-inference-in-molecular-biology--a/tok4J5Yum7M/">Network inference in molecular biology : a hands-on framework, Jesse M. Lingeman, Dennis Shasha</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
