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Networks analysis

Section edited by Jean-Philippe Vert

This section incorporates all aspects of network analysis including but not limited to: methods for predicting, analyzing and visualizing networks, and applications to systems biology.

Page 1 of 10

  1. Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential pro...

    Authors: Shiyuan Li, Zhen Zhang, Xueyong Li, Yihong Tan, Lei Wang and Zhiping Chen

    Citation: BMC Bioinformatics 2021 22:430

    Content type: Research article

    Published on:

  2. In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of ...

    Authors: Victor Bernal, Rainer Bischoff, Peter Horvatovich, Victor Guryev and Marco Grzegorczyk

    Citation: BMC Bioinformatics 2021 22:424

    Content type: Research article

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  3. Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biologic...

    Authors: Cindy Perscheid

    Citation: BMC Bioinformatics 2021 22:401

    Content type: Software

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  4. StrongestPath is a Cytoscape 3 application that enables the analysis of interactions between two proteins or groups of proteins in a collection of protein–protein interaction (PPI) network or signaling network...

    Authors: Zaynab Mousavian, Mehran Khodabandeh, Ali Sharifi-Zarchi, Alireza Nadafian and Alireza Mahmoudi

    Citation: BMC Bioinformatics 2021 22:352

    Content type: Software

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  5. Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression dat...

    Authors: Jiancheng Zhong, Chao Tang, Wei Peng, Minzhu Xie, Yusui Sun, Qiang Tang, Qiu Xiao and Jiahong Yang

    Citation: BMC Bioinformatics 2021 22:248

    Content type: Research article

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  6. Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, an...

    Authors: Andreas Krämer, Jean-Noël Billaud, Stuart Tugendreich, Dan Shiffman, Martin Jones and Jeff Green

    Citation: BMC Bioinformatics 2021 22:229

    Content type: Software

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  7. Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are perform...

    Authors: Nicola Licheri, Vincenzo Bonnici, Marco Beccuti and Rosalba Giugno

    Citation: BMC Bioinformatics 2021 22:209

    Content type: Methodology article

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  8. The Metabolic Network Explorer is a new addition to the BioCyc.org website and the Pathway Tools software suite that supports the interactive exploration of metabolic networks. Any metabolic network visualizat...

    Authors: Suzanne Paley and Peter D. Karp

    Citation: BMC Bioinformatics 2021 22:208

    Content type: Software

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  9. Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell ...

    Authors: Andrew J. Kavran and Aaron Clauset

    Citation: BMC Bioinformatics 2021 22:157

    Content type: Research article

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  10. Currently, no proven effective drugs for the novel coronavirus disease COVID-19 exist and despite widespread vaccination campaigns, we are far short from herd immunity. The number of people who are still vulne...

    Authors: Giulia Fiscon and Paola Paci

    Citation: BMC Bioinformatics 2021 22:150

    Content type: Software

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  11. Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds—namely the r and the associated...

    Authors: David Toubiana and Helena Maruenda

    Citation: BMC Bioinformatics 2021 22:116

    Content type: Methodology article

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  12. Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been deve...

    Authors: Julia Åkesson, Zelmina Lubovac-Pilav, Rasmus Magnusson and Mika Gustafsson

    Citation: BMC Bioinformatics 2021 22:58

    Content type: Methodology article

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  13. Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Current methods for the esti...

    Authors: Tobias Hepp, Jakob Zierk, Manfred Rauh, Markus Metzler and Andreas Mayr

    Citation: BMC Bioinformatics 2020 21:524

    Content type: Methodology article

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  14. High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight ab...

    Authors: Umair Ayub, Imran Haider and Hammad Naveed

    Citation: BMC Bioinformatics 2020 21:500

    Content type: Methodology article

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  15. Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying ne...

    Authors: Viola Fanfani, Fabio Cassano and Giovanni Stracquadanio

    Citation: BMC Bioinformatics 2020 21:476

    Content type: Software

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  16. Phenotypes such as height and intelligence, are thought to be a product of the collective effects of multiple phenotype-associated genes and interactions among their protein products. High/low degree of intera...

    Authors: Mikhail G. Dozmorov, Kellen G. Cresswell, Silviu-Alin Bacanu, Carl Craver, Mark Reimers and Kenneth S. Kendler

    Citation: BMC Bioinformatics 2020 21:473

    Content type: Methodology article

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  17. Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, s...

    Authors: Pasan C. Fernando, Paula M. Mabee and Erliang Zeng

    Citation: BMC Bioinformatics 2020 21:442

    Content type: Methodology article

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  18. Precise disease module is conducive to understanding the molecular mechanism of disease causation and identifying drug targets. However, due to the fragmentization of disease module in incomplete human interac...

    Authors: Bingbo Wang, Jie Hu, Yajun Wang, Chenxing Zhang, Yuanjun Zhou, Liang Yu, Xingli Guo, Lin Gao and Yunru Chen

    Citation: BMC Bioinformatics 2020 21:433

    Content type: Methodology article

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  19. The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the ...

    Authors: Bihai Zhao, Zhihong Zhang, Meiping Jiang, Sai Hu, Yingchun Luo and Lei Wang

    Citation: BMC Bioinformatics 2020 21:355

    Content type: Research article

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  20. While technological advances have made it possible to profile the immune system at high resolution, translating high-throughput data into knowledge of immune mechanisms has been challenged by the complexity of...

    Authors: Michelle B. Atallah, Varun Tandon, Kamir J. Hiam, Hunter Boyce, Michelle Hori, Waleed Atallah, Matthew H. Spitzer, Edgar Engleman and Parag Mallick

    Citation: BMC Bioinformatics 2020 21:346

    Content type: Research article

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  21. Melanoma phenotype and the dynamics underlying its progression are determined by a complex interplay between different types of regulatory molecules. In particular, transcription factors (TFs), microRNAs (miRN...

    Authors: Nivedita Singh, Martin Eberhardt, Olaf Wolkenhauer, Julio Vera and Shailendra K. Gupta

    Citation: BMC Bioinformatics 2020 21:329

    Content type: Research article

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  22. During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. “co-binding changes”...

    Authors: Jing Zhang, Jason Liu, Donghoon Lee, Shaoke Lou, Zhanlin Chen, Gamze Gürsoy and Mark Gerstein

    Citation: BMC Bioinformatics 2020 21:281

    Content type: Methodology article

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  23. The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. Howe...

    Authors: Shaima Belhechmi, Riccardo De Bin, Federico Rotolo and Stefan Michiels

    Citation: BMC Bioinformatics 2020 21:277

    Content type: Methodology article

    Published on:

  24. An important process for plant survival is the immune system. The induced systemic resistance (ISR) triggered by beneficial microbes is an important cost-effective defense mechanism by which plants are primed ...

    Authors: Tania Timmermann, Bernardo González and Gonzalo A. Ruz

    Citation: BMC Bioinformatics 2020 21:142

    Content type: Research Article

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  25. mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation ...

    Authors: Anna M. Nia, Tianlong Chen, Brooke L. Barnette, Kamil Khanipov, Robert L. Ullrich, Suresh K. Bhavnani and Mark R. Emmett

    Citation: BMC Bioinformatics 2020 21:118

    Content type: Methodology article

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  26. The systems-scale analysis of cellular metabolites, “metabolomics,” provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Fl...

    Authors: Robert A. Dromms, Justin Y. Lee and Mark P. Styczynski

    Citation: BMC Bioinformatics 2020 21:93

    Content type: Methodology article

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  27. In order to improve the accuracy of constraint-based metabolic models, several approaches have been developed which intend to integrate additional biological information. Two of these methods, MOMENT and GECKO...

    Authors: Pavlos Stephanos Bekiaris and Steffen Klamt

    Citation: BMC Bioinformatics 2020 21:19

    Content type: Methodology article

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  28. The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several...

    Authors: Sarah M. Kim, Matthew I. Peña, Mark Moll, George N. Bennett and Lydia E. Kavraki

    Citation: BMC Bioinformatics 2020 21:13

    Content type: Methodology Article

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  29. High-throughput technologies have brought tremendous changes to biological domains, and the resulting high-dimensional data has also posed enormous challenges to computational science. A Bayesian network is a ...

    Authors: Jiajin Chen, Ruyang Zhang, Xuesi Dong, Lijuan Lin, Ying Zhu, Jieyu He, David C. Christiani, Yongyue Wei and Feng Chen

    Citation: BMC Bioinformatics 2019 20:711

    Content type: Software

    Published on:

  30. In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. However, most of th...

    Authors: Jiechen Li, Xueyong Li, Xiang Feng, Bing Wang, Bihai Zhao and Lei Wang

    Citation: BMC Bioinformatics 2019 20:626

    Content type: Research article

    Published on:

  31. Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small m...

    Authors: M. Shaffer, K. Thurimella, K. Quinn, K. Doenges, X. Zhang, S. Bokatzian, N. Reisdorph and C. A. Lozupone

    Citation: BMC Bioinformatics 2019 20:614

    Content type: Methodology article

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  32. The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a m...

    Authors: Yonghyun Nam, Dong-gi Lee, Sunjoo Bang, Ju Han Kim, Jae-Hoon Kim and Hyunjung Shin

    Citation: BMC Bioinformatics 2019 20:576

    Content type: Methodology article

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  33. Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most ...

    Authors: Milagros Marín, Francisco J. Esteban, Hilario Ramírez-Rodrigo, Eduardo Ros and María José Sáez-Lara

    Citation: BMC Bioinformatics 2019 20:565

    Content type: Methodology article

    Published on:

  34. An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Infer...

    Authors: Yahui Long and Jiawei Luo

    Citation: BMC Bioinformatics 2019 20:541

    Content type: Research Article

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  35. At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of exper...

    Authors: S. Ha, E. Dimitrova, S. Hoops, D. Altarawy, M. Ansariola, D. Deb, J. Glazebrook, R. Hillmer, H. Shahin, F. Katagiri, J. McDowell, M. Megraw, J. Setubal, B. M. Tyler and R. Laubenbacher

    Citation: BMC Bioinformatics 2019 20:508

    Content type: Software

    Published on:

  36. Metabolic networks reflect the relationships between metabolites (biomolecules) and the enzymes (proteins), and are of particular interest since they describe all chemical reactions of an organism. The metabol...

    Authors: Adèle Weber Zendrera, Nataliya Sokolovska and Hédi A. Soula

    Citation: BMC Bioinformatics 2019 20:499

    Content type: Research Article

    Published on:

  37. Determining the association between tumor sample and the gene is demanding because it requires a high cost for conducting genetic experiments. Thus, the discovered association between tumor sample and gene fur...

    Authors: Mohan Timilsina, Haixuan Yang, Ratnesh Sahay and Dietrich Rebholz-Schuhmann

    Citation: BMC Bioinformatics 2019 20:462

    Content type: Research Article

    Published on:

Annual Journal Metrics

  • Speed
    70 days to first decision for reviewed manuscripts only
    44 days to first decision for all manuscripts
    163 days from submission to acceptance
    36 days from acceptance to publication

    Citation Impact
    3.169 - 2-year Impact Factor
    3.629 - 5-year Impact Factor
    1.276 - Source Normalized Impact per Paper (SNIP)
    1.567 - SCImago Journal Rank (SJR)

    Usage 
    5,167,186 Downloads
    5089 Altmetric Mentions