Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms

Li, Wenyuan; Hu, Haiyan; Huang, Yu; Li, Haifeng; Nunez-Iglesias, Juan; Xu, Min; Yan, Xifeng; Zhou, Xianghong Jasmine

doi:10.1007/s12561-011-9047-0

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…How to establish a pattern in biological tissues is one of the most hot topics in recent developmental biology, though its interest has been shown from several decades from both experimental [2,8,[30][31][32][33][34] and theoretical perspectives [35][36][37][38][39][40][41][42][43]. Several mechanisms for pattern formation have been proposed and this research is still ongoing with many unanswered questions.…”

Section: Discussionmentioning

confidence: 99%

Core regulatory network motif underlies the ocellar complex patterning in Drosophila melanogaster

Aguilar-Hidalgo

Lemos

Córdoba

2015

Physica D: Nonlinear Phenomena

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During organogenesis, developmental programs governed by Gene Regulatory Networks (GRN) define the functionality, size and shape of the different constituents of living organisms. Robustness, thus, is an essential characteristic that GRNs need to fulfill in order to maintain viability and reproducibility in a species. In the present work we analyze the robustness of the patterning for the ocellar complex formation in Drosophila melanogaster fly. We have systematically pruned the GRN that drives the development of this visual system to obtain the minimum pathway able to satisfy this pattern. We found that the mechanism underlying the patterning obeys to the dynamics of a 3-nodes network motif with a double negative feedback loop fed by a morphogenetic gradient that triggers the inhibition in a French flag problem fashion. A Boolean modeling of the GRN confirms robustness in the patterning mechanism showing the same result for different network complexity levels. Interestingly, the network provides a steady state solution in the interocellar part of the patterning and an oscillatory regime in the ocelli. This theoretical result predicts that the ocellar pattern may underlie oscillatory dynamics in its genetic regulation.

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Section: Discussionmentioning

confidence: 99%

Core regulatory network motif underlies the ocellar complex patterning in Drosophila melanogaster

Aguilar-Hidalgo

Lemos

Córdoba

2015

Physica D: Nonlinear Phenomena

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“…The expression values of isoforms from different RNA-seq datasets are not directly comparable, being easily biased by platforms and protocols. However, the correlations between expression profiles are comparable across datasets [24,25]. Therefore, we adopted coexpression networks as the data modeling and analysis paradigm [26] for protein isoforms.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we adopted coexpression networks as the data modeling and analysis paradigm [26] for protein isoforms. This framework has been successfully used to predict gene functions [27][28][29][30] and in many other integrative analyses [24][25][26][31][32][33], including functional module discovery. This paper reviews two computational methods that we recently developed to systematically annotate the functions of protein isoforms [34,35].…”

Section: Introductionmentioning

confidence: 99%

Pushing the annotation of cellular activities to a higher resolution: Predicting functions at the isoform level

Liu

Kang

et al. 2016

Methods

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In past decades, the experimental determination of protein functions was expensive and time-consuming, so numerous computational methods were developed to speed up and guide the process. However, most of these methods predict protein functions at the gene level and do not consider the fact that protein isoforms (translated from alternatively spliced transcripts), not genes, are the actual function carriers. Now, high-throughput RNA-seq technology is providing unprecedented opportunities to unravel protein functions at the isoform level. In this article, we review recent progress in the high-resolution functional annotations of protein isoforms, focusing on two methods developed by the authors. Both methods can integrate multiple RNA-seq datasets for comprehensively characterizing functions of protein isoforms.

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“…However, integrative analysis is a challenging task. Most of such analysis requires extensive analysis and development of complicated algorithms such as network integration [ 1 ], statistical association and regression [ 2 ], and partial least square analysis [ 3 ]. Since many of the algorithms are still in the development and testing stage, users are often required to have extensive preparation in quantitative analysis, algorithm development and computational methods.…”

Section: Introductionmentioning

confidence: 99%

iGPSe: A visual analytic system for integrative genomic based cancer patient stratification

Ding

Wang²,

Huang

et al. 2014

BMC Bioinformatics

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BackgroundCancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes.ResultsIn this paper we present a visual analytic system called Interactive Genomics Patient Stratification explorer (iGPSe) which significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. Our system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, we are able to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction.ConclusionsVisualization plays an important role in the process of stratifying given population patients. Visual tools allowed for the selection of possibly features across various datasets for the given patient population. We essentially made a case for visualization for a very important problem in translational informatics.

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Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms

Cited by 5 publications

References 32 publications

Core regulatory network motif underlies the ocellar complex patterning in Drosophila melanogaster

Core regulatory network motif underlies the ocellar complex patterning in Drosophila melanogaster

Pushing the annotation of cellular activities to a higher resolution: Predicting functions at the isoform level

iGPSe: A visual analytic system for integrative genomic based cancer patient stratification

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