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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. 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

    Published on:

  2. 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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  3. 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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  4. 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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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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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  15. 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

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  16. 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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  17. 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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  18. 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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  19. 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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  20. 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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  21. 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:

  22. 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

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  23. 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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  24. 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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  25. 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

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  26. 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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  27. 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

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  28. 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

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  29. 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

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  30. Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on t...

    Authors: Lyuba V. Bozhilova, Alan V. Whitmore, Jonny Wray, Gesine Reinert and Charlotte M. Deane

    Citation: BMC Bioinformatics 2019 20:446

    Content type: Research article

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  31. Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between gene...

    Authors: Yang Guo, Zhiman Zhong, Chen Yang, Jiangfeng Hu, Yaling Jiang, Zizhen Liang, Hui Gao and Jianxiao Liu

    Citation: BMC Bioinformatics 2019 20:444

    Content type: Methodology article

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  32. Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signatu...

    Authors: Wei Zhou, Karel K. M. Koudijs and Stefan Böhringer

    Citation: BMC Bioinformatics 2019 20:437

    Content type: Methodology article

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  33. Gene and protein interaction data are often represented as interaction networks, where nodes stand for genes or gene products and each edge stands for a relationship between a pair of gene nodes. Commonly, tha...

    Authors: Joëlle Barido-Sottani, Samuel D. Chapman, Evsey Kosman and Arcady R. Mushegian

    Citation: BMC Bioinformatics 2019 20:435

    Content type: Research article

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  34. Cartilage damage is a crucial feature involved in several pathological conditions characterized by joint disorders, such as osteoarthritis and rheumatoid arthritis. Accumulated evidences showed that Wnt/β-cate...

    Authors: Wei Zhou, Xiaojuan He, Ziyi Chen, Danping Fan, Yonghua Wang, Hui Feng, Ge Zhang, Aiping Lu and Lianbo Xiao

    Citation: BMC Bioinformatics 2019 20:412

    Content type: Research article

    Published on:

  35. The studies of functions of circular RNAs (circRNAs) are heavily focused on the regulation of gene expression through interactions with multiple miRNAs. However, the number of predicted target genes is typical...

    Authors: Ya-Chi Lin, Yueh-Chun Lee, Kai-Li Chang and Kuei-Yang Hsiao

    Citation: BMC Bioinformatics 2019 20:372

    Content type: Software

    Published on:

  36. Detection of central nodes in asymmetrically directed biological networks depends on centrality metrics quantifying individual nodes’ importance in a network. In topological analyses on metabolic networks, var...

    Authors: Eun-Youn Kim, Daniel Ashlock and Sung Ho Yoon

    Citation: BMC Bioinformatics 2019 20:328

    Content type: Research article

    Published on:

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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
    3.242 - 2-year Impact Factor
    3.213 - 5-year Impact Factor
    1.156 - Source Normalized Impact per Paper (SNIP)
    1.626 - SCImago Journal Rank (SJR)

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