scholarly journals A holistic miRNA-mRNA module discovery

2021 ◽  
Author(s):  
Ghada Shommo ◽  
Bruno Apolloni
Keyword(s):  
Module Discovery

scholarly journals Gene Set-Based Module Discovery Decodes cis-Regulatory Codes Governing Diverse Gene Expression across Human Multiple Tissues

PLoS ONE ◽  
2010 ◽  
Vol 5 (6) ◽  
pp. e10910 ◽  
Author(s):  
Atsushi Niida ◽  
Seiya Imoto ◽  
Rui Yamaguchi ◽  
Masao Nagasaki ◽  
Satoru Miyano
Keyword(s):  
Gene Expression ◽  
Gene Set ◽  
Module Discovery

scholarly journals Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Paola Paci ◽  
Giulia Fiscon ◽  
Federica Conte ◽  
Rui-Sheng Wang ◽  
Lorenzo Farina ◽  
...  
Keyword(s):  
Network Analysis ◽  
Interaction Network ◽  
Integrated Approach ◽  
State Transitions ◽  
Disease Genes ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Interactome Network ◽  
Module Discovery ◽  
Specific Disorders

AbstractIn this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

scholarly journals The effect of statistical normalisation on network propagation scores

2020 ◽  
Author(s):  
Sergio Picart-Armada ◽  
Wesley K Thompson ◽  
Alfonso Buil ◽  
Alexandre Perera-Lluna
Keyword(s):  
Protein Function ◽  
Diffusion Processes ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Mean Value ◽  
Statistical Properties ◽  
Label Propagation ◽  
Supplementary Information ◽  
Module Discovery ◽  
Permutation Analysis

Abstract Motivation Network diffusion and label propagation are fundamental tools in computational biology, with applications like gene-disease association, protein function prediction and module discovery. More recently, several publications have introduced a permutation analysis after the propagation process, due to concerns that network topology can bias diffusion scores. This opens the question of the statistical properties and the presence of bias of such diffusion processes in each of its applications. In this work, we characterised some common null models behind the permutation analysis and the statistical properties of the diffusion scores. We benchmarked seven diffusion scores on three case studies: synthetic signals on a yeast interactome, simulated differential gene expression on a protein-protein interaction network and prospective gene set prediction on another interaction network. For clarity, all the datasets were based on binary labels, but we also present theoretical results for quantitative labels. Results Diffusion scores starting from binary labels were affected by the label codification, and exhibited a problem-dependent topological bias that could be removed by the statistical normalisation. Parametric and non-parametric normalisation addressed both points by being codification-independent and by equalising the bias. We identified and quantified two sources of bias -mean value and variance- that yielded performance differences when normalising the scores. We provided closed formulae for both and showed how the null covariance is related to the spectral properties of the graph. Despite none of the proposed scores systematically outperformed the others, normalisation was preferred when the sought positive labels were not aligned with the bias. We conclude that the decision on bias removal should be problem and data-driven, i.e. based on a quantitative analysis of the bias and its relation to the positive entities. Availability The code is publicly available at https://github.com/b2slab/diffuBench Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Yu Song ◽  
Jin-Xing Liu ◽  
Chun-Hou Zheng ◽  
Sha-Sha Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Network ◽  
Principal Component ◽  
Graph Laplacian ◽  
Component Analysis ◽  
Network Module ◽  
Tumor Sample ◽  
Lp Norm ◽  
Module Discovery

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

scholarly journals PENGEMBANGAN MODUL PRAKTIKUM SERLI (DISCOVERY LEARNING) UNTUK PEMBELAJARAN SAINS DI SEKOLAH DASAR

2020 ◽  
Vol 7 (1) ◽  
pp. 53-64
Author(s):  
Azizah Thalib ◽  
Puji Winarti ◽  
Nurul Kami Sani
Keyword(s):  
Elementary School ◽  
Elementary Schools ◽  
Research And Development ◽  
Science Learning ◽  
Discovery Learning ◽  
Development Model ◽  
Development Research ◽  
Is Research ◽  
Module Discovery

This study aims to develop a Serli Practicum Module (Discovery Learning) for Science learning in Class VI Elementary Schools that has met valid, practical, and effective criteria for use in Science learning in elementary schools. This type of research is research and development that uses the Peffers et al development model. The type of development research includes six phases, namely: (1) identifying problems that motivate research, (2) describing research objectives, (3) designing and developing products, (4) testing products, (5) evaluating trial results, and (6) ) communicating results. The results of the study show that the Serli Practicum Module (Discovery Learning) for Science learning in Class VI Elementary School has been valid, practical and effective. Validity values obtained are 61 with very valid criteria. Practical value of 65.73 or very practical. And the acquisition of effectiveness value is 0.70 with effective criteria.

scholarly journals The effect of statistical normalisation on network propagation scores

2020 ◽  
Author(s):  
Sergio Picart-Armada ◽  
Wesley K. Thompson ◽  
Alfonso Buil ◽  
Alexandre Perera-Lluna
Keyword(s):  
Protein Function ◽  
Diffusion Processes ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Mean Value ◽  
Statistical Properties ◽  
Label Propagation ◽  
Protein Protein Interaction ◽  
Module Discovery ◽  
Permutation Analysis

AbstractMotivationNetwork diffusion and label propagation are fundamental tools in computational biology, with applications like gene-disease association, protein function prediction and module discovery. More recently, several publications have introduced a permutation analysis after the propagation process, due to concerns that network topology can bias diffusion scores. This opens the question of the statistical properties and the presence of bias of such diffusion processes in each of its applications. In this work, we characterised some common null models behind the permutation analysis and the statistical properties of the diffusion scores. We benchmarked seven diffusion scores on three case studies: synthetic signals on a yeast interactome, simulated differential gene expression on a protein-protein interaction network and prospective gene set prediction on another interaction network. For clarity, all the datasets were based on binary labels, but we also present theoretical results for quantitative labels.ResultsDiffusion scores starting from binary labels were affected by the label codification, and exhibited a problem-dependent topological bias that could be removed by the statistical normalisation. Parametric and non-parametric normalisation addressed both points by being codification-independent and by equalising the bias. We identified and quantified two sources of bias -mean value and variance- that yielded performance differences when normalising the scores. We provided closed formulae for both and showed how the null covariance is related to the spectral properties of the graph. Despite none of the proposed scores systematically outperformed the others, normalisation was preferred when the sought positive labels were not aligned with the bias. We conclude that the decision on bias removal should be problem and data-driven, i.e. based on a quantitative analysis of the bias and its relation to the positive entities.AvailabilityThe code is publicly available at https://github.com/b2slab/[email protected]

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