Network Modeling of Complex Data Sets

Piet Jones; Deborah Weighill; Manesh B Shah; Sharlee Climer; Jeremy Schmutz; Avinash Sreedasyam; Gerald A. Tuskan; Daniel Jacobson

doi:10.1007/978-1-0716-0195-2_15

Network Modeling of Complex Data Sets

Piet Jones, Deborah Weighill, Manesh B Shah, Sharlee Climer, Jeremy Schmutz, Avinash Sreedasyam, Gerald A. Tuskan, Daniel Jacobson

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Abstract

We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

Original language	American English
Journal	Metabolic Pathway Engineering
DOIs	https://doi.org/10.1007/978-1-0716-0195-2_15
State	Published - 2020

Keywords

Differential analysis
Enrichment
FCROS
Fisher exact test
Machine learning
Random forests
Similarity network

Disciplines

Molecular Biology

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10.1007/978-1-0716-0195-2_15License: Unspecified

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title = "Network Modeling of Complex Data Sets",

abstract = " We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas. ",

keywords = "Differential analysis, Enrichment, FCROS, Fisher exact test, Machine learning, Random forests, Similarity network",

author = "Piet Jones and Deborah Weighill and Shah, \{Manesh B\} and Sharlee Climer and Jeremy Schmutz and Avinash Sreedasyam and Tuskan, \{Gerald A.\} and Daniel Jacobson",

note = "We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.",

year = "2020",

doi = "10.1007/978-1-0716-0195-2\_15",

language = "American English",

journal = "Metabolic Pathway Engineering",

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TY - JOUR

T1 - Network Modeling of Complex Data Sets

AU - Jones, Piet

AU - Weighill, Deborah

AU - Shah, Manesh B

AU - Climer, Sharlee

AU - Schmutz, Jeremy

AU - Sreedasyam, Avinash

AU - Tuskan, Gerald A.

AU - Jacobson, Daniel

N1 - We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

PY - 2020

Y1 - 2020

N2 - We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

AB - We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

KW - Differential analysis

KW - Enrichment

KW - FCROS

KW - Fisher exact test

KW - Machine learning

KW - Random forests

KW - Similarity network

UR - https://doi.org/10.1007/978-1-0716-0195-2_15

U2 - 10.1007/978-1-0716-0195-2_15

DO - 10.1007/978-1-0716-0195-2_15

M3 - Article

JO - Metabolic Pathway Engineering

JF - Metabolic Pathway Engineering

ER -

Network Modeling of Complex Data Sets

Abstract

Keywords

Disciplines

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