Relevant bibliographies by topics / Biological Network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Biological Network'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Biological Network"

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Wang, Yi, Feng Zhan, Cuiyu Huang, and Yiran Huang. "GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search." Applied Sciences 14, no. 21 (2024): 9844. http://dx.doi.org/10.3390/app14219844.

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Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been achieved in biological network similarity searches. However, it remains a challenge to mine the biological network information fully to improve the accuracy of query results without increasing time overheads. In this study, we propose a biological network similarity search method based on graph neural
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Kim, Hyunju, Paul Davies, and Sara Imari Walker. "New scaling relation for information transfer in biological networks." Journal of The Royal Society Interface 12, no. 113 (2015): 20150944. http://dx.doi.org/10.1098/rsif.2015.0944.

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We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 ( doi:10.1371/journal.pone.0001672 )) and that of the budding yeast Saccharomyces cerevisiae (Li et al . 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 ( doi:10.1073/pnas.0305937101 )). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and s
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Shen, Bin, Muwei Zhao, Wei Zhong, and Jieyue He. "An Improved Method for Completely Uncertain Biological Network Alignment." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/253854.

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With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of current approaches, an improved method called completely probabilistic biological network comparison alignm
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PRATAP, ABHAY, SETU TALIYAN, and TIRATHA RAJ SINGH. "NMDB: NETWORK MOTIF DATABASE ENVISAGED AND EXPLICATED FROM HUMAN DISEASE SPECIFIC PATHWAYS." Journal of Biological Systems 22, no. 01 (2014): 89–100. http://dx.doi.org/10.1142/s0218339014500053.

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The study of network motifs for large number of networks can aid us to resolve the functions of complex biological networks. In biology, network motifs that reappear within a network more often than expected in random networks include negative autoregulation, positive autoregulation, single-input modules, feedforward loops, dense overlapping regulons and feedback loops. These network motifs have their different dynamical functions. In this study, our main objective is to examine the enrichment of network motifs in different biological networks of human disease specific pathways. We characteriz
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Milenković, Tijana, Weng Leong Ng, Wayne Hayes, and NatašA PržUlj. "Optimal Network Alignment with Graphlet Degree Vectors." Cancer Informatics 9 (January 2010): CIN.S4744. http://dx.doi.org/10.4137/cin.s4744.

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Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones,
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Kostić, Daniel, Claus C. Hilgetag, and Marc Tittgemeyer. "Unifying the essential concepts of biological networks: biological insights and philosophical foundations." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1796 (2020): 20190314. http://dx.doi.org/10.1098/rstb.2019.0314.

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Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organizational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definitio
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Albi, Giacomo, Marco Artina, Massimo Foransier, and Peter A. Markowich. "Biological transportation networks: Modeling and simulation." Analysis and Applications 14, no. 01 (2016): 185–206. http://dx.doi.org/10.1142/s0219530515400059.

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We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based
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Wang, Pei. "Network biology: Recent advances and challenges." Gene & Protein in Disease 1, no. 2 (2022): 101. http://dx.doi.org/10.36922/gpd.v1i2.101.

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Biological networks have garnered widespread attention. The development of biological networks has spawned the birth of a new interdisciplinary field – network biology. Network biology involves the exploration of complex biological systems through biological networks for better understanding of biological functions. This paper reviews some of the recent development of network biology. On the one hand, various approaches to constructing different types of biological networks are reviewed, and the pros and cons of each approach are discussed; on the other hand, the recent advances of information
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Zaghloul Salem, Mohammad Saad. "Biological Networks: An Introductory Review." Journal of Proteomics and Genomics Research 2, no. 1 (2018): 41–111. http://dx.doi.org/10.14302/issn.2326-0793.jpgr-18-2312.

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All aspects of life activities in living cells are mediated/executed and regulated by a vast number of networks, comprising a wide spectrum of components, starting with simple biomolecules and ending with the whole organism, and functioning within a precisely organized tight framework. Proper mediation of cellular activities necessitates their inclusion within the context of structured and organized network systems capable of regulating/coordinating and synchronizing the countless numbers of biological processes occurring within living cells. The number of biological networks and pathways with
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Didier, Gilles, Christine Brun, and Anaïs Baudot. "Identifying communities from multiplex biological networks." PeerJ 3 (December 22, 2015): e1525. http://dx.doi.org/10.7717/peerj.1525.

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Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multipl
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Dissertations / Theses on the topic "Biological Network"

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McGrane, Martin. "Biological Network Distances." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/17233.

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Networks of interactions are increasingly used to model biological systems. The patterns of these networks capture a larger, more complex, representation of the whole than any single attribute can. Networks allow the modelling of far more complicated systems, at the expense of more computationally complex analysis. The networks of biological entities share common aspects. They mutate, and they mutate in a similar fashion. These mutations can be accurately measured, but accurately measuring the effect of a mutation on the overall network is beyond current understanding. Tools to find similari
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Hall, Kristopher Joseph. "Thwarting Network Stealth Worms in Computer Networks through Biological Epidemiology." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/27726.

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This research developed a system, Rx, to provide early identification and effective control of network stealth worms in digital networks through techniques based on biological epidemiology. Network stealth worms comprise a class of surreptitious, self-propagating code that spread over network connections by exploiting security vulnerabilities in hosts. Past outbreaks due to traditional worms subverted hundreds of thousands of machines. Network stealth worms exacerbate that threat by using clandestine methods to maintain a persistent presence in the network.
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Mina, Marco. "Mining Biological Networks." Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3422641.

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This thesis addresses relevant issues related to the analysis of biological networks. Path redundancy was exploited to denoise currently available data, dominated by high levels of wrong or missing information, and applied to the local alignment of protein-protein interaction networks. On another research direction, regulatory networks were employed to explain master regulators’ ability of modulating cells’ behaviour. In this direction, an existing approach was adapted for the analysis of miRNAs’ role in Glioblastoma Multiforme cancer cells. The methodological aspects of this work represent a
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Gollapudi, Venkata Lakshmi Sirisha. "Services for biological network feature detection." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/13022/.

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The complex environment of a living cell contains many molecules interacting in a variety of ways. Examples include the physical interaction between two proteins, or the biochemical interaction between an enzyme and its substrate. A challenge of systems biology is to understand the network of interactions between biological molecules, derived experimentally or computationally. Sophisticated dynamic modelling approaches provide detailed knowledge about single processes or individual pathways. However such methods are far less tractable for holistic cellular models, which are instead represented
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Nguyen, Viet-Anh. "Bayesian approaches to biological network predictions." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610000.

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Dunn, Nathan A. "A Novel Neural Network Analysis Method Applied to Biological Neural Networks." Thesis, view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1251892251&sid=2&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2006.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 122- 131). Also available for download via the World Wide Web; free to University of Oregon users.
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Radu, Alexandru Sorin. "Biological Network Analysis through Global Pairwise and Multi-way Network Alignment." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/13629.

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Recent developments into high throughput sequencing technologies have created a large influx of biological data, with a reasonable portion consisting of interaction data. This interaction data has been compiled into several network types. Protein-protein interaction networks that outline the interactions between proteins, gene regulatory networks which describe the regulation of expression of genes by other genes, and transcription networks that model how a collection of regulatory proteins associate with genes across a genome. These networks permit the analysis of biological systems from a di
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Yates, Phillip. "An Inferential Framework for Network Hypothesis Tests: With Applications to Biological Networks." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2200.

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The analysis of weighted co-expression gene sets is gaining momentum in systems biology. In addition to substantial research directed toward inferring co-expression networks on the basis of microarray/high-throughput sequencing data, inferential methods are being developed to compare gene networks across one or more phenotypes. Common gene set hypothesis testing procedures are mostly confined to comparing average gene/node transcription levels between one or more groups and make limited use of additional network features, e.g., edges induced by significant partial correlations. Ignoring the ge
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BONOMO, Mariella. "Knowledge Extraction from Biological and Social Graphs." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/576508.

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Many problems from real life deal with the generation of enormous, varied, dynamic, and interconnected datasets coming from different and heterogeneous sources. Analysing large volumes of data makes it possible to generate new knowledge useful for making more informed decisions, in business and beyond. From personalising customer communication to streamlining production processes, via flow and emergency management, Big Data Analytics has an impact on all processes. The potential uses of Big Data go much further: two of the largest sources of data are including individual traders’ purchasin
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Sachs, Karen Ph D. Massachusetts Institute of Technology. "Bayesian network models of biological signaling pathways." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38865.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006.<br>Includes bibliographical references (p. 153-165).<br>Cells communicate with other cells, and process cues from their environment, via signaling pathways, in which extracellular cues trigger a cascade of information flow, causing signaling molecules to become chemically, physically or locationally modified, gain new functional capabilities, and affect subsequent molecules in the cascade, culminating in a phenotypic cellular response. Mapping the influence connections among biomolecules in a signali
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Books on the topic "Biological Network"

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Rathore, Heena. Mapping Biological Systems to Network Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29782-8.

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Yoon, Byung-Jun, and Xiaoning Qian, eds. Recent Advances in Biological Network Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57173-3.

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Treur, Jan. Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31445-3.

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Geological Survey (U.S.). USGS response to Congress on the National Biological Information Infrastructure. U.S. Dept. of the Interior, U.S. Geological Survey, 2005.

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Horvath, Steve. Weighted Network Analysis: Applications in Genomics and Systems Biology. Springer Science+Business Media, LLC, 2011.

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Geological Survey (U.S.). Strategic plan for the USGS National Biological Information Infrastructure (NBII). U.S. Dept, of the Interior, U.S. Geological Survey, 2005.

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Claire, Losos Elizabeth, and Leigh Egbert Giles, eds. Tropical forest diversity and dynamism: Findings from a large-scale plot network. University of Chicago Press, 2004.

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Lamoreux, M. Lynn. The colors of mice: A model genetic network. Wiley-Blackwell, 2010.

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Lynn, Lamoreux M., ed. The colors of mice: A model genetic network. Wiley-Blackwell, 2010.

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Vidyasagar, Mathukumalli. Computational Cancer Biology: An Interaction Network Approach. Springer London, 2012.

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Book chapters on the topic "Biological Network"

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Kim, Ju Han. "Biological Network Analysis." In Genome Data Analysis. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_13.

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Mohammadi, Shahin, and Ananth Grama. "Biological Network Alignment." In Functional Coherence of Molecular Networks in Bioinformatics. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0320-3_5.

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Kemp, Melissa L. "Biological Network Model." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1245.

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Yue, Zongliang, Da Yan, Guimu Guo, and Jake Y. Chen. "Biological Network Mining." In Modeling Transcriptional Regulation. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_8.

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Ibrahim, Zina M., Alioune Ngom, and Ahmed Y. Tawfik. "Gene Regulatory Network Identification with Qualitative Probabilistic Networks." In Biological Knowledge Discovery Handbook. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch12.

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Bile, Alessandro. "Introduction to Neural Networks: Biological Neural Network." In Solitonic Neural Networks. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48655-5_1.

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Janowski, Sebastian Jan, Barbara Kaltschmidt, and Christian Kaltschmidt. "Biological Network Modeling and Analysis." In Approaches in Integrative Bioinformatics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41281-3_8.

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Ferraro, Nicola, Luigi Palopoli, Simona Panni, and Simona E. Rombo. "“Master-Slave” Biological Network Alignment." In Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13078-6_24.

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Gkigkitzis, Ioannis, Ioannis Haranas, and Ilias Kotsireas. "Biological Relevance of Network Architecture." In Advances in Experimental Medicine and Biology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56246-9_1.

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Zhang, Minlu, and Long Jason Lu. "Biological Applications of Network Modules." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_558.

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Conference papers on the topic "Biological Network"

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De Luca, Riccardo, Manuela Petti, Pietro H. Guzzi, and Paolo Tieri. "SHELLEY: Exploring Learning-Based Network Alignment on Biological Data." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821759.

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Reilly, Rosemarie, Xiaoshu Xu, and Jerald Jones. "Neural Network Application to Acoustic Emission Signal Processing." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92242.

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Abstract Artificial neural systems, also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, non-linear, mathematical relationship or transform. These constructs have two significant properties that have proven usefu
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Rock, Alan, Xiaoshu Xu, and Jerald E. Jones. "Typical Neural Network Applications in Signal Processing and Process Modelling." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92265.

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Abstract Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, non-linear, mathematical relationship or transform. These constructs have two significant properties that have prove
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Hasan, Md Mahmudul, and Tamer Kahveci. "Incremental network querying in biological networks." In BCB '14: ACM-BCB '14. ACM, 2014. http://dx.doi.org/10.1145/2649387.2660845.

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Layton, Astrid, John Reap, and Bert Bras. "A Correlation Between Thermal Efficiency and Biological Network Cyclicity." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54787.

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This article investigates a correlation between the thermal efficiency of ideal power cycles and a structural measure of the degree of interactions in networks known as cyclicity. Efficient design of networks that reuse materials and energy motivates the work. Corporate “take-back” plans, multi-company industrial symbioses and public recycling programs recover products, components and materials using partially closed loop networks. As resources become scarcer and more expensive, the prevalence of these networks is likely to increase, and the importance of designing efficient networks grows. Mu
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Saribatir, Behcet Melih, and Kayhan Erciyes. "A Parallel Network Alignment Algorithm for Biological Networks." In 2022 3rd International Informatics and Software Engineering Conference (IISEC). IEEE, 2022. http://dx.doi.org/10.1109/iisec56263.2022.9998245.

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Jo, Yousang, Doheon Lee, and Kwang H. Lee. "Context-specific biological network construction." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044682.

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Souza, Alfredo Guilherme da Silva, and Adenilso Simao. "Investigation of the performance of driver mutation identification methods using biological networks and enriched biological networks." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbcas.2022.222457.

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Several computational methods allow identifying genes related to cancer (driver mutation) through patient mutation data and biological networks. Usually, networks are not built focusing on biological activities associated with cancer because they are designed for general use. In this study, we investigate the performance of methods for identifying driver mutations using biological networks and enriched biological networks, applying a gene prioritization method to classify genes associated with cancer understudy in the biological network. The results indicated that employing the enrichment meth
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Zheng, Shengjie, Lang Qian, Pingsheng Li, Chenggang He, Xiaoqi Qin, and Xiaojian Li. "An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121010.

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Stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review summarizes the basic properties of artificial neural networks as well as spiking neural networks. Our focus is on the biological background and theoretical basis of s
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Alterovitz, Gil, and Marco F. Ramon. "Biological network epitomes via topological compression." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353142.

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Reports on the topic "Biological Network"

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Balaji, V., Tom Boden, Dave Cowley, et al. Biological and Environmental Research Network Requirements. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1165120.

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Thompson, Richard F. A Biological Neural Network Analysis of Learning and Memory. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada241837.

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Zurawski, Jason, eli Dart, Zach Harlan, et al. Biological and Environmental Research Network Requirements Review Final Report. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1996500.

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Lincoln, Patrick, and Charles J. Pedersen. A Problem-Solving Environment for Biological Network Informatics: Bio-Spice. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada471395.

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Thompson, Richard F. A Biological Neural Network Analysis of Learning and Memory: The Cerebellum and Sensory Motor Conditioning. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada304568.

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Kurtz, Deborah, Samuel Roberts, Taylor Schmidt, et al. Northern Colorado Plateau Network landbird monitoring report 2024. National Park Service, 2024. https://doi.org/10.36967/2306877.

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In 2024, the University of Delaware, in cooperation with the National Park Service, completed the nineteenth year of a habitat-based landbird monitoring program in park units of the Northern Colorado Plateau Network. This program is designed to provide rigorous population trend data for most diurnal, regularly occurring breeding landbird species throughout the network. Using data from 2005 to 2024, density trends were estimated for 61 species in at least one of three habitats surveyed: low-elevation riparian, pinyon-juniper, and sage shrubland. Eighteen of these trends were significant, with s
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Wang, Ying yuan, Zechang Chen, Luxin Zhang, et al. A systematic review and network meta-analysis: Role of SNPs in predicting breast carcinoma risk. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.2.0092.

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Review question / Objective: P: Breast cancer patient; I: Single nucleotide polymorphisms associated with breast cancer risk; C: Healthy person; O: By comparing the proportion of SNP mutations in the tumor group and the control group, the effect of BREAST cancer risk-related SNP was investigated; S: Case-control study. Condition being studied: Breast cancer (BC) is one of the most common cancers among women, and its morbidity and mortality have continued to increase worldwide in recent years, reflecting the strong invasiveness and metastasis characteristics of this cancer. BC is a complex dise
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Kristofferson, D., and D. Mack. The BIOSCI electronic newsgroup network for the biological sciences. Final report, October 1, 1992--June 30, 1996. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/376397.

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Cowles, Timothy J. Real-time Integration of Biological, Optical and Physical Oceanographic Data from Multiple Vessels and Nearshore Sites using a Wireless Network. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada627905.

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Shen, Yu, Tinghan Yang, Yaru Li, Wenjian Meng, and Ziqiang Wang. Pelvic floor reconstruction after abdominoperineal resection: a network meta-analysis comparing primary closure, biological mesh reconstruction, omentoplasty, and myocutaneous flap closure. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.7.0009.

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