Gene relevance based on multiple evidences in complex networks

机译：基于复杂网络中的多证据的基因相关性

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Noemi Di Nanni; Matteo Gnocchi; Marco Moscatelli; Luciano Milanesi; Ettore Mosca;
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年度 2019
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1. Gene relevance based on multiple evidences in complex networks [J] . Di Nanni Noemi, Gnocchi Matteo, Moscatelli Marco, Bioinformatics . 2020,第3期

机译：基于复杂网络中的多证据的基因相关性
2. Convolutional neural networks for relevance feedback in content based image retrieval A Content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback [J] . Lorenzo Putzu, Luca Piras, Giorgio Giacinto Multimedia Tools and Applications . 2020,第37a38期

机译：基于内容的图像检索的相关反馈的卷积神经网络基于内容的图像检索系统，用于利用特征提取和相关性反馈的卷积神经网络
3. Wireless visual sensor networks for smart city applications: A relevance-based approach for multiple sinks mobility [J] . Joao Paulo Just Peixoto, Daniel G. Costa Future generation computer systems . 2017,第nova期

机译：适用于智慧城市应用的无线视觉传感器网络：基于相关性的多汇点移动性方法
4. Marine Multiple Time Series Relevance Discovery Based on Complex Network [C] . Lei Wang, Zongwen Huang, Suixiang Shi, International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society . 2018

机译：基于复杂网络的海洋多时间序列相关性发现
5. Understanding the genetic basis of complex polygenic traits through Bayesian model selection of multiple genetic models and network modelling of family-based genetic data. [D] . Bae, Harold T. 2014

机译：通过多种遗传模型的贝叶斯模型选择和基于家庭的遗传数据的网络建模，了解复杂的多基因性状的遗传基础。
6. Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks [O] . Fang Zheng, Le Wei, Liang Zhao, -1

机译：基于多种生物网络的复杂疾病路径网络分析
7. We develop a model of scientific creativity and test it in the field of rare diseases. Our model is based on the results of an in-depth case study of the Rett syndrome. Archival analysis, bibliometric techniques and expert surveys are combined with network analysis to identify the most creative scientists. First, alternative measures of generative and combinatorial creativity are compared. Then, we generalize our results and present a stochastic model of socio-semantic network evolution. The model predictions are tested with multiple networks of rare disease specialties. We find that new scientific collaborations among experts in a field enhance combinatorial creativity. Instead, high entry rates of novices are negatively related to generative creativity. By extending the set of useful concepts, creative scientists gain in centrality. At the same time, by increasing their centrality in the scientific community, scientists can replicate and generalize their results, thus contributing to a scientific paradigm. [O] . Massimo Riccaboni, Maria Laura Frigotto 100

机译：我们开发了一种科学创造力模型，并在稀有疾病领域进行测试。我们的模型基于对Rett综合征的深入案例研究的结果。档案分析，文献计量技术和专家调查与网络分析相结合，以确定最具创造力的科学家。首先，比较生成和组合创造力的替代措施。然后，我们推广了我们的结果，并提出了社会语义网络演化的随机模型。模型预测用多个罕见疾病专业网络进行测试。我们发现，一个领域的专家之间的新的科学合作增强了组合创造力。相反，新手的高入门率与生成创造力负相关。通过扩展这组有用的概念，创造性的科学家获得了中心地位。同时，通过增加科学界的中心地位，科学家们可以复制和推广他们的结果，从而促进科学范式的发展。
8. Complexation of Cp2MCl2 in a Chloroaluminate Molten Salt: Relevance toHomogeneous Ziegler-Natta Catalysis [R] . Carlin, R. T., Wilkes, J. S. 1990

机译：Cp2mCl2在氯铝酸盐熔盐中的络合：与均相齐格勒 - 纳塔催化的相关性

Gene relevance based on multiple evidences in complex networks

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