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Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.


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Proceedings Article
22 Aug 2004
TL;DR: The technique of differential association rule mining is applied to the comparison of protein annotations within an interaction network and between different interaction networks and is able to find rules that explain known properties of protein interaction networks as well as rules that show promise for advanced study.
Abstract: Protein-protein interactions are of great interest to biologists. A variety of high-throughput techniques have been devised, each of which leads to a separate definition of an interaction network. The concept of differential association rule mining is introduced to study the annotations of proteins in the context of one or more interaction networks. Differences among items across edges of a network are explicitly targeted. As a second step we identify differences between networks that are separately defined on the same set of nodes. The technique of differential association rule mining is applied to the comparison of protein annotations within an interaction network and between different interaction networks. In both cases we were able to find rules that explain known properties of protein interaction networks as well as rules that show promise for advanced study.

18 citations

Journal ArticleDOI
TL;DR: DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment.
Abstract: Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases. We developed a Cytoscape app, which allows constructing biological network layouts based on the data from molecular profiles imported as values of node attributes. DeDaL is a Cytoscape 3 app, which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. The combination of all these functionalities facilitates the creation of insightful network layouts representing both structural network features and correlation patterns in multivariate data. We demonstrate the added value of applying DeDaL in several practical applications, including an example of a large protein-protein interaction network. DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment. DeDaL is freely available for downloading at http://bioinfo-out.curie.fr/projects/dedal/ .

18 citations

Journal ArticleDOI
TL;DR: In this special issue, 11 interesting studies were included and several novel computational methods for systems biology were proposed for the first time and some intriguing biological findings were reported in large scale experiments.
Abstract: In the postgenomic era, the large-scale data, such as genome sequences, mRNA sequences, and protein sequences, increase rapidly. It is desired to develop the computational approaches that can derive and analyze useful information from them to promote the development of biomedicine and drug design. Meanwhile, in order to understand how protein-protein interactions and other complex interactions in a living system get integrated in complex nonlinear networks and regulate cell function, a new discipline, called “Systems Biology”, is created. In this special issue, 11 interesting studies were included. Several novel computational methods for systems biology were proposed for the first time and some intriguing biological findings were reported in large scale experiments. J. Cao and L. Xiong studied the protein sequence classification using the single hidden layer feed forward neural network (SLFN). Two algorithms, the basic extreme learning machine (ELM) and the optimal pruned ELM (OP-ELM), were adopted as the learning algorithms for the ensemble based SLFNs. Their methods outperformed back propagation (BP) neural network and support vector machine (SVM). Y. F. Gao et al. proposed a novel prediction method based on drug and compound ontology information extracted from ChEBI to identify drugs target groups, from which the kind of functions of a drug may be deduced. Their overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset. The study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups. Z. Li et al. developed a computational method to predict retinoblastoma (RB) related genes. RB is the most common primary intraocular malignancy usually occurring in childhood. Their method was based on dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The RB and non-RB genes can be classified with Gene Ontology enrichment scores and KEGG enrichment scores. This method can be generalized to predict the other cancer related genes as well. Y. Jiang et al. proposed a method to identify gastric cancer genes by first applying the shortest path algorithm to protein-protein interaction network and then filtering the shortest path genes based permutation betweenness. Many identified candidate genes were involved in gastric cancer related biological processes. Their study gives a new insight for studying gastric cancer. T. Zhang et al. proposed a computational method for gene phenotypes prediction. Their method regarded the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and showed more comprehensive view of the protein's biological effects. The performance of their method was better than dagging, random forest, and sequential minimal optimization (SMO). Q. Zou et al. reviewed the network based disease gene identification methods, such as CIPHER, RWRH, Prince, Meta-path, Katz, Catapult, Diffusion Kernel, and ProDiGe and compared their performance. Some advices about software choosing and parameter setting were provided. They also analyzed the core problems and challenges of these methods and discussed future research direction. G. S.V. McDowell et al. developed a bioinformatics tool, Visualization and Phospholipid Identification (VaLID), to search and visualize the 1,473,168 phospholipids from the VaLID database. Each phospholipid can be generated in skeletal representation. VaLID is freely available and responds to all users through the CTPNL resources website at http://neurolipidomics.com/resources.html and http://neurolipidomics.ca/. X. Lai et al. proposed a systems biology approach combining database-oriented network reconstruction, data-driven modeling, and model-driven experiments to study the regulatory role of miRNAs in coordinating gene expression. They illustrate the method by reconstructing, modeling and simulating the miRNA network regulating p21. Their model can be used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts and phenotypes. P. Cui et al. analyzed the genome-wide relationship between chromatin features and chromatin accessibility in DNase I hypersensitive sites. They found that these features show distinct preference to localize in open chromatin. Their study provides new insights into the true biological phenomena and the combinatorial effects of chromatin features on differential DNase I hypersensitivity. L. Zhu et al. sequenced the transcriptome of Sophora japonica Linn (Chinese scholar tree), a shrub species belonging to the subfamily Faboideae of the pea family Fabaceae. Approximately 86.1 million high-quality reads were generated and assembled de novo into 143010 unique transcripts and 57614 unigenes. The transcriptome data of S. japonica from this study represents first genome-scale investigation of gene expressions in Faboideae plants. Y. Wang et al. characterized the microsatellite pattern in the V. volvacea genome and compared it with microsatellites found in the genomes of four other edible fungi: Coprinopsis cinerea, Schizophyllum commune, Agaricus bisporus, and Pleurotus ostreatus. A total of 1346 microsatellites have been identified with mononucleotides, the most frequent motif. Their analysis suggested a possible relationship between the most frequent microsatellite types and the genetic distance between the five fungal genomes. With the current exponential increase of biological and biomedical high-throughput data generated, in the future we will see how methodologies like the ones described in this special issue become absolutely necessary. But furthermore we need to know how methodologies pertaining data analysis, network reconstruction, and modeling get together (a) to make possible the integration of massive, multiple-type quantitative high-throughput data and (b) to understand how cell phenotypes emerge from large, multilevel and structurally complex biochemical regulatory networks. Yudong Cai Julio Vera Gonzalez Zengrong Liu Tao Huang

18 citations

Proceedings ArticleDOI
06 Sep 2018
TL;DR: In this paper, the authors proposed a multi-view factorization autoencoder with network constraints to integrate multi-omic data with domain knowledge (biological interaction networks) to predict disease progression-free interval (PFI) event.
Abstract: Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology. However, multi-omic data has the “big p, small N” problem (the number of features is large, but the number of samples is small), it is challenging to train a complicated machine learning model from the multi-omic data alone and make it generalize well. Here we propose a framework termed Multi-view Factorization AutoEncoder with network constraints to integrate multi-omic data with domain knowledge (biological interaction networks). Our framework employs deep representation learning to learn feature embeddings and patient embeddings simultaneously, enabling us to integrate feature interaction network and patient view similarity network constraints into the training objective. The whole framework is end-to-end differentiable. We applied our approach to two TCGA datasets and achieved satisfactory results on predicting disease Progression-Free Interval (PFI) event.

18 citations

Journal ArticleDOI
TL;DR: In this article, it is shown that it is possible to reconstruct the whole structure of an interaction network and simultaneously infer the complete time course of activation spreading, relying just on single epoch (i.e. snapshot) or time-scattered observations of a small number of activity cascades.
Abstract: Accessing the network through which a propagation dynamics diffuse is essential for understanding and controlling it. In a few cases, such information is available through direct experiments or thanks to the very nature of propagation data. In a majority of cases however, available information about the network is indirect and comes from partial observations of the dynamics, rendering the network reconstruction a fundamental inverse problem. Here we show that it is possible to reconstruct the whole structure of an interaction network and to simultaneously infer the complete time course of activation spreading, relying just on single epoch (i.e. snapshot) or time-scattered observations of a small number of activity cascades. The method that we present is built on a Belief Propagation approximation, that has shown impressive accuracy in a wide variety of relevant cases, and is able to infer interactions in presence of incomplete time-series data by providing a detailed modeling of the posterior distribution of trajectories conditioned to the observations. Furthermore, we show by experiments that the information content of full cascades is relatively smaller than that of sparse observations or single snapshots.

18 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163