scholarly journals IndeCut evaluates performance of network motif discovery algorithms

2017 ◽  
Vol 34 (9) ◽  
pp. 1514-1521 ◽  
Author(s):  
Mitra Ansariola ◽  
Molly Megraw ◽  
David Koslicki
Keyword(s):  
Motif Discovery ◽  
Network Motif ◽  
Discovery Algorithms

scholarly journals IndeCut evaluates performance of network motif discovery algorithms

2017 ◽  
Author(s):  
Mitra Ansariola ◽  
Molly Megraw ◽  
David Koslicki
Keyword(s):  
Motif Discovery ◽  
Network Motif ◽  
Practical Method ◽  
Algorithm Performance ◽  
Complex Processes ◽  
Open Source Software Package ◽  
Discovery Algorithms ◽  
Motif Discovery Algorithm ◽  
Motif Finding Algorithm

AbstractGenomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no sound practical method to numerically evaluate whether any network motif discovery algorithm performs as intended—thus it was not possible to assess the validity of resulting network motifs. In this work, we present IndeCut, the first and only method that allows characterization of network motif finding algorithm performance on any network of interest. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut estimates the minimally required number of samples that each network motif discovery tool needs in order to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the most accurate network motif discovery tool for their network of interest among many available options. IndeCut is an open source software package and is available at https://github.com/megrawlab/IndeCut.

scholarly journals MODA: An efficient algorithm for network motif discovery in biological networks

2009 ◽  
Vol 84 (5) ◽  
pp. 385-395 ◽  
Author(s):  
Saeed Omidi ◽  
Falk Schreiber ◽  
Ali Masoudi-Nejad
Keyword(s):  
Biological Networks ◽  
Efficient Algorithm ◽  
Motif Discovery ◽  
Network Motif

scholarly journals An Efficient Network Motif Discovery Approach for Co-Regulatory Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 14151-14158 ◽  
Author(s):  
Jiawei Luo ◽  
Lv Ding ◽  
Cheng Liang ◽  
Nguyen Hoang Tu
Keyword(s):  
Regulatory Networks ◽  
Motif Discovery ◽  
Network Motif

Motif discovery algorithms in static and temporal networks: A survey

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Ali Jazayeri ◽  
Christopher C Yang
Keyword(s):  
Complex Systems ◽  
Motif Discovery ◽  
Temporal Networks ◽  
Frequent Subgraph Mining ◽  
The Core ◽  
The Past ◽  
Frequent Subgraph ◽  
Social Media Platforms ◽  
Discovery Algorithms ◽  
Big Data Technology

Abstract Motifs are the fundamental components of complex systems. The topological structure of networks representing complex systems and the frequency and distribution of motifs in these networks are intertwined. The complexities associated with graph and subgraph isomorphism problems, as the core of frequent subgraph mining, directly impact the performance of motif discovery algorithms. Researchers have adopted different strategies for candidate generation and enumeration and frequency computation to cope with these complexities. Besides, in the past few years, there has been an increasing interest in the analysis and mining of temporal networks. In contrast to their static counterparts, these networks change over time in the form of insertion, deletion or substitution of edges or vertices or their attributes. In this article, we provide a survey of motif discovery algorithms proposed in the literature for mining static and temporal networks and review the corresponding algorithms based on their adopted strategies for candidate generation and frequency computation. As we witness the generation of a large amount of network data in social media platforms, bioinformatics applications and communication and transportation networks and the advance in distributed computing and big data technology, we also conduct a survey on the algorithms proposed to resolve the CPU-bound and I/O bound problems in mining static and temporal networks.

scholarly journals An Entropy-Based Position Projection Algorithm for Motif Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yipu Zhang ◽  
Ping Wang ◽  
Maode Yan
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
Optimal Solution ◽  
Training Model ◽  
Projection Algorithm ◽  
Local Optimum ◽  
The Past ◽  
Local Optimal Solution ◽  
And Function ◽  
Discovery Algorithms

Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms.

Comparative Analysis of DNA Motif Discovery Algorithms: A Systemic Review

2019 ◽  
Vol 15 (1) ◽  
pp. 4-26
Author(s):  
Fatma A. Hashim ◽  
Mai S. Mabrouk ◽  
Walid A.L. Atabany
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
Probabilistic Approach ◽  
Biological Data ◽  
Systemic Review ◽  
Local Optimum ◽  
Dna Motif ◽  
Functional Features ◽  
Dna Motif Discovery ◽  
Discovery Algorithms

Background: Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery. Methods: Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach. Results: Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum. Conclusion: Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

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