Biological Network Inference from Microarray Data, Current Solutions, and Assessments

Biological network inference is based on a series of studies and computational approaches to the deduction of the connectivity of chemical species, the reaction pathway, and the reaction kinetics of complex reaction systems from experimental measurements. Inference for network structure and reaction kinetics parameters governing the dynamics of a biological system is currently an active area of research. In the era of post-genomic biology, it is a common opinion among scientists that living systems (cells, tissues, organs and organisms) can be understood in terms of their network structure as well as in term of the evolution in time of this network structure. In this chapter, the authors make a survey of the recent methodologies proposed for the structure inference and for the parameter estimation of a system of interacting biological entities. Furthermore, they present the recent works of the authors about model identification and calibration.

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Evaluation of Time Series Microarray Data for Dynamic Gene Regulatory Network Inference

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 - IFMBE Proceedings ◽

10.1007/978-3-319-32703-7_110 ◽

2016 ◽

pp. 565-568

Author(s):

Panagiotis Xenitidis ◽

I. Seimenis ◽

S. Kakolyris ◽

A. Adamopoulos

Keyword(s):

Time Series ◽

Gene Regulatory Network ◽

Microarray Data ◽

Regulatory Network ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Gene Regulatory ◽

Time Series Microarray

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The feasibility of genome-scale biological network inference using Graphics Processing Units

Algorithms for Molecular Biology ◽

10.1186/s13015-017-0100-5 ◽

2017 ◽

Vol 12 (1) ◽

Cited By ~ 4

Author(s):

Raghuram Thiagarajan ◽

Amir Alavi ◽

Jagdeep T. Podichetty ◽

Jason N. Bazil ◽

Daniel A. Beard

Keyword(s):

Graphics Processing Units ◽

Network Inference ◽

Biological Network ◽

Graphics Processing ◽

Genome Scale

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TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data

Nucleic Acids Research ◽

10.1093/nar/gkt529 ◽

2013 ◽

Vol 41 (W1) ◽

pp. W562-W568 ◽

Cited By ~ 8

Author(s):

Alexander L. R. Lubbock ◽

Elad Katz ◽

David J. Harrison ◽

Ian M. Overton

Keyword(s):

Survival Analysis ◽

Tissue Microarray ◽

Microarray Data ◽

Network Inference ◽

Patient Stratification

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Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories

Microarrays ◽

10.3390/microarrays5030023 ◽

2016 ◽

Vol 5 (3) ◽

pp. 23 ◽

Cited By ~ 4

Author(s):

Sriram Chockalingam ◽

Maneesha Aluru ◽

Srinivas Aluru

Keyword(s):

Data Processing ◽

Microarray Data ◽

Network Inference ◽

Scale Network ◽

Public Repositories ◽

Processing Techniques ◽

Genome Scale

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GPseudoRank: a permutation sampler for single cell orderings

10.1101/211417 ◽

2017 ◽

Cited By ~ 2

Author(s):

Magdalena E Strauß ◽

John E Reid ◽

Lorenz Wernisch

Keyword(s):

Single Cell ◽

Microarray Data ◽

Network Inference ◽

Biological Process ◽

Supplementary Information ◽

Established Method ◽

Data Set ◽

Point Estimates ◽

Biological Development ◽

Selection Of

AbstractMotivationA number of pseudotime methods have provided point estimates of the ordering of cells for scRNA-seq data. A still limited number of methods also model the uncertainty of the pseudotime estimate. However, there is still a need for a method to sample from complicated and multi-modal distributions of orders, and to estimate changes in the amount of the uncertainty of the order during the course of a biological development, as this can support the selection of suitable cells for the clustering of genes or for network inference.ResultsIn an application to a microarray data set our proposed method, GPseudoRank, identifies two modes of the distribution, each of them corresponding to point estimates of orders obtained by a different established method. In an application to scRNA-seq data we demonstrate the potential of GPseudoRank to identify phases of lower and higher pseudotime uncertainty during a biological process. GPseudoRank also correctly identifies cells precocious in their antiviral response.Availability and implementationOur method is available on github: https://github.com/magStra/GPseudoRank.Contactmagdalena.strauss@mrc-bsu.cam.ac.ukSupplementary informationSupplementary materials are available.

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