TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.
TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.
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TrendyGenes是一个计算管道，用于检测学术界和药物发现中的文献趋势。
DOI：
10.1038/s41598-021-94897-9
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发表时间：
2021-08-03
影响因子：
4.6
通讯作者：
Crowther DJ
中科院分区：
文献类型：
--
作者：
Serrano Nájera G;Narganes Carlón D;Crowther DJ
目标识别和优先级排序是现代药物发现的重要第一步。传统上，个别科学家利用他们的专业知识手动解释科学文献并优先考虑机会。然而，不断增加的出版率和更广泛的人类基因组规模研究的常规覆盖面，使其难以保持有意义的概述，从中确定有希望的新趋势。在这里，我们提出了一个自动化但灵活的管道，确定科学语料库中的趋势，这些趋势与研究人员的特定兴趣保持一致，并促进机会的初始优先级。使用基于共引网络和机器学习的程序，首先使用一种新的命名实体识别系统从PubMed文章中解析基因和疾病，并提供出版日期和支持信息。然后训练递归神经网络来预测所有人类基因的发布动态。对于用户定义的治疗焦点，产生更多出版物或引用的基因被鉴定为高兴趣靶标。我们还使用了主题检测程序来帮助理解为什么基因是流行的，并实现了一个系统来为潜在的目标提出最突出的评论文章。这个TrendyGenes管道检测新兴的目标和途径，并为个人研究人员，制药公司和资助机构提供了一种探索文献的新方法。
Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.
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染色体不稳定性通过胞质DNA反应驱动转移。
影响因子：
64.8
作者：
Bakhoum SF;Ngo B;Laughney AM;Cavallo JA;Murphy CJ;Ly P;Shah P;Sriram RK;Watkins TBK;Taunk NK;Duran M;Pauli C;Shaw C;Chadalavada K;Rajasekhar VK;Genovese G;Venkatesan S;Birkbak NJ;McGranahan N;Lundquist M;LaPlant Q;Healey JH;Elemento O;Chung CH;Lee NY;Imielenski M;Nanjangud G;Pe'er D;Cleveland DW;Powell SN;Lammerding J;Swanton C;Cantley LC
通讯作者：
Cantley LC
影响因子：
5.8
作者：
Bauer-Mehren, Anna;Rautschka, Michael;Furlong, Laura I.
通讯作者：
Furlong, Laura I.
影响因子：
64.5
作者：
Batra R;Nelles DA;Pirie E;Blue SM;Marina RJ;Wang H;Chaim IA;Thomas JD;Zhang N;Nguyen V;Aigner S;Markmiller S;Xia G;Corbett KD;Swanson MS;Yeo GW
通讯作者：
Yeo GW
影响因子：
3
作者：
Bravo À;Piñero J;Queralt-Rosinach N;Rautschka M;Furlong LI
通讯作者：
Furlong LI
DOI：
10.4049/jimmunol.1400499
发表时间：
2014-06-15
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Berger SB;Kasparcova V;Hoffman S;Swift B;Dare L;Schaeffer M;Capriotti C;Cook M;Finger J;Hughes-Earle A;Harris PA;Kaiser WJ;Mocarski ES;Bertin J;Gough PJ
通讯作者：
Gough PJ