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Estimating mutual information using B-spline functions--an improved similarity measure for analysing gene expression data

Carsten O Daub, Ralf Steuer, Joachim Selbig, Sebastian Kloska
2004 BMC Bioinformatics  
In this work, we propose a method for the numerical estimation of mutual information from continuous data.  ...  The utilisation of mutual information as similarity measure enables the detection of non-linear correlations in gene expression datasets.  ...  If mutual information is indeed to be used for the analysis of gene-expression data, the continuous experimental data need to be partitioned into discrete intervals, or bins.  ... 
doi:10.1186/1471-2105-5-118 pmid:15339346 pmcid:PMC516800 fatcat:j33cqa7w2rfqbihm723mmst6yy

Mutual Information Estimation For Transcriptional Regulatory Network Inference [article]

Jonathan Ish-Horowicz, John Reid
2017 bioRxiv   pre-print
Furthermore, when using an estimator that discretises expression data, using N 1/3 bins for N samples gives the most accurate inferred network.  ...  the expression of pairs of genes.  ...  The B-spline estimator was the best performing mutual information estimator We recommend using the B-spline estimator, supporting the findings of [9, 22] .  ... 
doi:10.1101/132647 fatcat:sl3cazf6wrgphcghivk4zhr3nu

Continuous Representations of Time-Series Gene Expression Data

Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon
2003 Journal of Computational Biology  
We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters.  ...  Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve.  ...  For instance, non-stationary Hidden Markov models with warping parameters have been used for alignment of speech data [5] , and mutual information based methods have been used for registering medical  ... 
doi:10.1089/10665270360688057 pmid:12935332 fatcat:kelmm73d6fgvvmolzxxoqkrdvu

A new approach to analyzing gene expression time series data

Ziv Bar-Joseph, Georg Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon
2002 Proceedings of the sixth annual international conference on Computational biology - RECOMB '02  
We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters.  ...  Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve.  ...  For instance, non-stationary Hidden Markov models with warping parameters have been used for alignment of speech data [5] , and mutual information based methods have been used for registering medical  ... 
doi:10.1145/565196.565202 dblp:conf/recomb/Bar-JosephGGJS02 fatcat:qknablmqxjdw7eaqp7bu3f3tjm

A pipeline to analyse time-course gene expression data

Nelle Varoquaux, Elizabeth Purdom
2020 F1000Research  
This workflow provides a step-by-step tutorial of the methodology used to analyse time-course data: (1) quality control and normalization of the dataset; (2) differential expression analysis using functional  ...  The phenotypic diversity of cells is governed by a complex equilibrium between their genetic identity and their environmental interactions: Understanding the dynamics of gene expression is a fundamental  ...  Acknowledgments The authors thank Karthik Ram and the Ropensci community for valuable feedback.  ... 
doi:10.12688/f1000research.27262.1 fatcat:5upglzognjbgveb6jf7vpr3i5m

A Bayesian approach for structure learning in oscillating regulatory networks

Daniel Trejo Banos, Andrew J. Millar, Guido Sanguinetti
2015 Bioinformatics  
However, while identifying cyclically expressed genes from time series measurements is relatively easy, determining the structure of the interaction network underpinning the oscillation is a far more challenging  ...  Our method is based on projecting the signal onto a set of oscillatory basis functions using a Discrete Fourier Transform.  ...  ACKNOWLEDGEMENT We thank Botond Cseke, Vân-Anh Huynh-Thu and Daniel Seaton for useful discussions. The GENIE3 software adapted for time series data was kindly provided to us by Dr Vân-Anh Huynh-Thu.  ... 
doi:10.1093/bioinformatics/btv414 pmid:26177966 pmcid:PMC4817140 fatcat:hhkhakz33ndxzhq7k4qsdrnpdm

Multi-Block Sparse Functional Principal Components Analysis for Longitudinal Microbiome Multi-Omics Data [article]

Lingjing Jiang, Chris Elrod, Jane J. Kim, Austin D. Swafford, Rob Knight, Wesley K. Thompson
2021 arXiv   pre-print
Mutual information is used to assess the strength of marginal and conditional temporal associations across outcome trajectories.  ...  Although we focus on application of mSFPCA to microbiome data in this paper, the mSFPCA model is of general utility and can be used in a wide range of real-world applications.  ...  number 2 of studies are also collecting transcriptomics data to understand microbial gene expression, proteomics data to study expressed proteins, and metabolomics data to define the functional status  ... 
arXiv:2102.00067v2 fatcat:o27megqwpbalrl4j7oaleh3a2a

Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects

Qi Long, Matthias Chung, Carlos S. Moreno, Brent A. Johnson
2011 Annals of Applied Statistics  
In biomedical studies it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subject to censoring.  ...  We propose to build risk prediction scores through regularized rank estimation in partly linear AFT models, where high-dimensional data such as gene expression data are modeled linearly and important clinical  ...  Acknowledgments We thank Editor Kafadar, an associate editor, and two referees for their helpful suggestions that greatly improved an earlier draft of this manuscript.  ... 
doi:10.1214/11-aoas458 pmid:22081781 pmcid:PMC3212400 fatcat:o23lwbeynzfz5cyq7ns23bwsfq

Comprehensive review of association estimators for the inference of gene networks

Zeyneb KURT, Nizamettin AYDIN, Gökmen ALTAY
2016 Turkish Journal of Electrical Engineering and Computer Sciences  
We performed this main aim by presenting, classifying, comparing, and discussing them to reveal which association estimator is more suitable for use in GNI applications by considering only the information  ...  Gene network inference (GNI) algorithms allow us to explore the vast amount of interactions among the molecules in cells.  ...  They used conditional similarity measures instead of an MI estimator (ARACNE uses KDE for MI estimation).  ... 
doi:10.3906/elk-1312-90 fatcat:rvao7m3eq5efxees4riwaw4fri

Design of a MAPK signalling cascade balances energetic cost versus accuracy of information transmission

Alexander Anders, Bhaswar Ghosh, Timo Glatter, Victor Sourjik
2020 Nature Communications  
Here, we investigate the trade-off between accuracy of information transmission and its energetic cost for a mitogen-activated protein kinase (MAPK) signalling cascade.  ...  Cellular processes are inherently noisy, and the selection for accurate responses in presence of noise has likely shaped signalling networks.  ...  Acknowledgements We thank Pieter Rein ten Wolde, Giulia Malaguti and Sean Murray for insightful comments on the manuscript.  ... 
doi:10.1038/s41467-020-17276-4 pmid:32661402 fatcat:x4yolgzzhzdcbhht4gxi5p6ski

MCPNet : A parallel maximum capacity-based genome-scale gene network construction framework [article]

Tony C Pan, Sriram Ponnambalam Chockalingam, Maneesha Aluru, Srinivas Aluru
2022 bioRxiv   pre-print
Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality  ...  Motivation: Gene regulatory network (GRN) reconstruction from gene expression profiles is a compute- and data-intensive problem.  ...  From the measured gene expression profiles P i1 , . . . , P i|S| and P j1 , . . . , P j|S| , the interaction strength W ij between genes v i and v j can be computed by estimating the mutual information  ... 
doi:10.1101/2022.07.19.500603 fatcat:m744pyzwhzgyflspkfn2w6nxty

GPLEXUS: enabling genome-scale gene association network reconstruction and analysis for very large-scale expression data

Jun Li, Hairong Wei, Tingsong Liu, Patrick Xuechun Zhao
2013 Nucleic Acids Research  
GPLEXUS adopts an ultra-fast estimation for pairwise mutual information computing that is similar in accuracy and sensitivity to the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE  ...  The accurate construction and interpretation of gene association networks (GANs) is challenging, but crucial, to the understanding of gene function, interaction and cellular behavior at the genome level  ...  ACKNOWLEDGEMENTS The authors thank Dr Xinbin Dai for his assistance in the design and deployment of the GPLEXUS system.  ... 
doi:10.1093/nar/gkt983 pmid:24178033 pmcid:PMC3950724 fatcat:5legmdg34rfn7putqvp5xuw5z4

Single-cell mRNA quantification and differential analysis with Census

Xiaojie Qiu, Andrew Hill, Jonathan Packer, Dejun Lin, Yi-An Ma, Cole Trapnell
2017 Nature Methods  
. census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances.  ...  Articles nAture methods | ADVANCE ONLINE PUBLICATION | single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell rnA-seq measurements  ...  Our current implementation of Monocle 2 relies on VGAM's 'smart' spline fitting functionality, hence the use of the sm.ns() function instead of the more widely used ns() function from the splines package  ... 
doi:10.1038/nmeth.4150 pmid:28114287 pmcid:PMC5330805 fatcat:cpgqvpclyvcq7nrozdcuhnvfjy

A Bayesian approach for structure learning in oscillating regulatory networks [article]

D Trejo, AJ Millar, G Sanguinetti
2015 arXiv   pre-print
However, while identifying cyclically expressed genes from time series measurements is relatively easy, determining the structure of the interaction network underpinning the oscillation is a far more challenging  ...  Our method is based on projecting the signal onto a set of oscillatory basis functions using a Discrete Fourier Transform.  ...  Acknowledgement We thank Botond Cseke, Vân-Anh Huynh-Thu and Daniel Seaton for useful discussions. The GENIE3 software adapted for time series data was kindly provided to us by Dr Vân-Anh Huynh-Thu.  ... 
arXiv:1504.06553v1 fatcat:jkepteqzfnadvpcgj6zfp6cev4

dRFEtools: Dynamic recursive feature elimination for omics [article]

Kynon Jade Marius Benjamin, Tarun Katipalli, Apuã CM Paquola
2022 bioRxiv   pre-print
We demonstrate dRFEtools' ability to identify biologically relevant information from genomic data using RNA-Seq and genotype data from the BrainSeq Consortium. dRFEtools provides an interpretable and flexible  ...  tool to gain biological insights from omics data using machine learning.  ...  Acknowledgements We would like to extend our deepest appreciation to our colleagues in the Erwin laboratory group for their comments and suggestions in the development of this software.  ... 
doi:10.1101/2022.07.27.501227 fatcat:2m4rpogc5jhgzn6adqhmzuftti
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