Littérature scientifique sur le sujet « Network science Computer science Systems biology protein interaction networks »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Network science Computer science Systems biology protein interaction networks ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Network science Computer science Systems biology protein interaction networks"
CHEN, JAKE Y., ZHONG YAN, CHANGYU SHEN, DAWN P. G. FITZPATRICK et MU WANG. « A SYSTEMS BIOLOGY APPROACH TO THE STUDY OF CISPLATIN DRUG RESISTANCE IN OVARIAN CANCERS ». Journal of Bioinformatics and Computational Biology 05, no 02a (avril 2007) : 383–405. http://dx.doi.org/10.1142/s0219720007002606.
Texte intégralDESAI, KAUSHAL, DAVID BROTT, XIAOHUA HU et ANASTASIA CHRISTIANSON. « A SYSTEMS BIOLOGY APPROACH FOR DETECTING TOXICITY-RELATED HOTSPOTS INSIDE PROTEIN INTERACTION NETWORKS ». Journal of Bioinformatics and Computational Biology 09, no 05 (octobre 2011) : 647–62. http://dx.doi.org/10.1142/s0219720011005707.
Texte intégralMatsubara, Teppei, Tomoshiro Ochiai, Morihiro Hayashida, Tatsuya Akutsu et Jose C. Nacher. « Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles ». Journal of Bioinformatics and Computational Biology 17, no 03 (juin 2019) : 1940007. http://dx.doi.org/10.1142/s0219720019400079.
Texte intégralWU, YONGHUI, et STEFANO LONARDI. « A LINEAR-TIME ALGORITHM FOR PREDICTING FUNCTIONAL ANNOTATIONS FROM PPI NETWORKS ». Journal of Bioinformatics and Computational Biology 06, no 06 (décembre 2008) : 1049–65. http://dx.doi.org/10.1142/s0219720008003916.
Texte intégralPatra, Sabyasachi, et Anjali Mohapatra. « Motif discovery in biological network using expansion tree ». Journal of Bioinformatics and Computational Biology 16, no 06 (décembre 2018) : 1850024. http://dx.doi.org/10.1142/s0219720018500245.
Texte intégralChang, Shen, Jian-You Chen, Yung-Jen Chuang et Bor-Sen Chen. « Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications ». International Journal of Molecular Sciences 22, no 1 (26 décembre 2020) : 166. http://dx.doi.org/10.3390/ijms22010166.
Texte intégralZHU, ZHENGWEI, ANDREY TOVCHIGRECHKO, TATIANA BARONOVA, YING GAO, DOMINIQUE DOUGUET, NICHOLAS O'TOOLE et ILYA A. VAKSER. « LARGE-SCALE STRUCTURAL MODELING OF PROTEIN COMPLEXES AT LOW RESOLUTION ». Journal of Bioinformatics and Computational Biology 06, no 04 (août 2008) : 789–810. http://dx.doi.org/10.1142/s0219720008003679.
Texte intégralFulnečková, Jana, Ladislav Dokládal, Karolína Kolářová, Martina Nešpor Dadejová, Klára Procházková, Sabina Gomelská, Martin Sivčák et al. « Telomerase Interaction Partners–Insight from Plants ». International Journal of Molecular Sciences 23, no 1 (29 décembre 2021) : 368. http://dx.doi.org/10.3390/ijms23010368.
Texte intégralKohutyuk, Oksana, Fadi Towfic, M. Heather West Greenlee et Vasant Honavar. « BioNetwork Bench : Database and Software for Storage, Query, and Analysis of Gene and Protein Networks ». Bioinformatics and Biology Insights 6 (janvier 2012) : BBI.S9728. http://dx.doi.org/10.4137/bbi.s9728.
Texte intégralCaetano-Anollés, Gustavo, M. Fayez Aziz, Fizza Mughal, Frauke Gräter, Ibrahim Koç, Kelsey Caetano-Anollés et Derek Caetano-Anollés. « Emergence of Hierarchical Modularity in Evolving Networks Uncovered by Phylogenomic Analysis ». Evolutionary Bioinformatics 15 (janvier 2019) : 117693431987298. http://dx.doi.org/10.1177/1176934319872980.
Texte intégralThèses sur le sujet "Network science Computer science Systems biology protein interaction networks"
SCARDONI, Giovanni. « Computational Analysis of Biological networks ». Doctoral thesis, Università degli Studi di Verona, 2010. http://hdl.handle.net/11562/343983.
Texte intégralThis thesis, treating both topological and dynamic points of view, concerns several aspects of biological networks analysis. Regarding the topological analysis of biological networks, the main contribution is the node-oriented point of view of the analysis. It means that instead of concentrating on global properties of the networks, we analyze them in order to extract properties of single nodes. An excellent method to face this problem is to use node centralities. Node centralities allow to identify nodes in a network having a relevant role in the network structure. This can not be enough if we are dealing with a biological network, since the role of a protein depends also on its biological activity that can be detected with lab experiments. Our approach is to integrate centralities analysis and data from biological experiments. A protocol of analysis have been produced, and the CentiScaPe tool for computing network centralities and integrating topological analysis with biological data have been designed and implemented. CentiScaPe have been applied to a human kino-phosphatome network and according to our protocol, kinases and phosphatases with highest centralities values have been extracted creating a new subnetwork of most central kinases and phosphatases. A lab experiment established which of this proteins presented high activation level and through CentiScaPe the proteins with both high centrality values and high activation level have been easily identified. The notion of node centralities interference have also been introduced to deal with central role of nodes in a biological network. It allow to identify which are the nodes that are more affected by the remotion of a particular node measuring the variation on their centralities values when such a node is removed from the network. The application of node centralities interference to the human kino-phosphatome revealed that different proteins affect centralities values of different nodes. Similarly to node centralities interference, the notion of centrality robustness of a node is introduced. This notion reveals if the central role of a node depends on other particular nodes in the network or if the node is ``robust'' in the sense that even if we remove or add other nodes the central role of the node remains almost unchanged. The dynamic aspects of biological networks analysis have been treated from an abstract interpretation point of view. Abstract interpretation is a powerful framework for the analysis of software and is excellent in deriving numerical properties of programs. Dealing with pathways, abstract interpretation have been adapted to the analysis of pathways simulation. Intervals domain and constants domain have been succesfully used to automatically extract information about reactants concentration. The intervals domain allow to determine the range of concentration of the proteins, and the constants domain have been used to know if a protein concentration become constant after a certain time. The other domain of analysis used is the congruences domain that, if applied to pathways simulation can easily identify regular oscillating behaviour in reactants concentration. The use of abstract interpretation allows to execute thousands of simulation and to completely and automatically characterize the behaviour of the pathways. In such a way it can be used also to solve the problem of parameters estimation where missing parameters can be detected with a brute force algorithm combined with the abstract interpretation analysis. The abstract interpretation approach have been succesfully applied to the mitotic oscillator pathway, characterizing the behaviour of the pathway depending on some reactants. To help the analysis of relation between reactants in the network, the notions of variables interference and variables abstract interference have been introduced and adapted to biological pathways simulation. They allow to find relations between properties of different reactants of the pathway. Using the abstract interference techniques we can say, for instance, which range of concentration of a protein can induce an oscillating behaviour of the pathway.
Ayati, Marzieh. « Algorithms to Integrate Omics Data for Personalized Medicine ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1527679638507616.
Texte intégralChapitres de livres sur le sujet "Network science Computer science Systems biology protein interaction networks"
Amos, Martyn, et Gerald Owenson. « An Introduction to Cellular Computing ». Dans Cellular Computing. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195155396.003.0005.
Texte intégral