通过共同靶向克服耐药性的两种情况。

Q4 Health Professions
Golnaz Taheri, Marzieh Ayati, Limsoon Wong, Changiz Eslahchi
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引用次数: 2

摘要

去除病原体基本途径上的蛋白质,预计会阻止病原体发挥重要功能。为了破坏这些途径,我们考虑一个简单图G的切集S,其中G代表病原体的PPI网络。去掉S后,如果两个分区的大小差较大,则存在功能通路的概率增大。我们需要将图划分为平衡的分区,并用谱双分区进行近似。我们考虑两种情况:在第一种情况下,我们没有任何关于药物目标的信息;第二,我们考虑有关药物目标的信息。我们的数据库是大肠杆菌和空肠杆菌。在第一种情况下,大肠杆菌和空肠杆菌切割中20%和17%的蛋白质是药物靶点,在第二种情况下,切割中53%和63%的蛋白质分别是药物靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two scenarios for overcoming drug resistance by co-targeting.

Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively.

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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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