A literature search tool for intelligent extraction of disease-associated genes.
A literature search tool for intelligent extraction of disease-associated genes.
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DOI：
10.1136/amiajnl-2012-001563
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发表时间：
2014-05
期刊：
影响因子：
--
通讯作者：
Wall DP
中科院分区：
文献类型：
--
作者：
Jung JY;DeLuca TF;Nelson TH;Wall DP
从PubMed的科学文献中提取疾病相关基因，与现有方法相比，对基于文献的支持具有更高的敏感性。我们开发了一个PubMed查询检索疾病相关的，原始的研究文章。然后，我们应用基于规则的文本挖掘算法与关键字匹配，以提取目标疾病，基因与显着的结果，以及文章所描述的研究类型。我们将我们得到的候选疾病基因和支持参考文献与现有数据库进行了比较。我们证明了我们的候选基因集几乎涵盖了手动管理的数据库中的所有基因，并且支持疾病-基因关联的参考文献比其他通用基因-疾病关联数据库更广泛和准确。我们实施了一种新的出版物搜索工具来查找目标文章，特别关注疾病和基因型之间的联系。通过与黄金标准手动更新的基因紊乱数据库的比较，以及与具有类似功能的自动化数据库的比较，我们表明我们的工具可以搜索整个PubMed，快速准确地提取人类疾病的主要基因发现。
To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods. We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article. We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder–gene link are more extensive and accurate than other general purpose gene-to-disorder association databases. We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene–disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.
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影响因子：
12.3
作者：
Alex B;Grover C;Haddow B;Kabadjov M;Klein E;Matthews M;Tobin R;Wang X
通讯作者：
Wang X
影响因子：
3
作者：
Agarwal S;Yu H;Kohane I
通讯作者：
Kohane I
影响因子：
14.9
作者：
Basu SN;Kollu R;Banerjee-Basu S
通讯作者：
Banerjee-Basu S
影响因子：
3.5
作者：
Anney R;Klei L;Pinto D;Almeida J;Bacchelli E;Baird G;Bolshakova N;Bölte S;Bolton PF;Bourgeron T;Brennan S;Brian J;Casey J;Conroy J;Correia C;Corsello C;Crawford EL;de Jonge M;Delorme R;Duketis E;Duque F;Estes A;Farrar P;Fernandez BA;Folstein SE;Fombonne E;Gilbert J;Gillberg C;Glessner JT;Green A;Green J;Guter SJ;Heron EA;Holt R;Howe JL;Hughes G;Hus V;Igliozzi R;Jacob S;Kenny GP;Kim C;Kolevzon A;Kustanovich V;Lajonchere CM;Lamb JA;Law-Smith M;Leboyer M;Le Couteur A;Leventhal BL;Liu XQ;Lombard F;Lord C;Lotspeich L;Lund SC;Magalhaes TR;Mantoulan C;McDougle CJ;Melhem NM;Merikangas A;Minshew NJ;Mirza GK;Munson J;Noakes C;Nygren G;Papanikolaou K;Pagnamenta AT;Parrini B;Paton T;Pickles A;Posey DJ;Poustka F;Ragoussis J;Regan R;Roberts W;Roeder K;Roge B;Rutter ML;Schlitt S;Shah N;Sheffield VC;Soorya L;Sousa I;Stoppioni V;Sykes N;Tancredi R;Thompson AP;Thomson S;Tryfon A;Tsiantis J;Van Engeland H;Vincent JB;Volkmar F;Vorstman JA;Wallace S;Wing K;Wittemeyer K;Wood S;Zurawiecki D;Zwaigenbaum L;Bailey AJ;Battaglia A;Cantor RM;Coon H;Cuccaro ML;Dawson G;Ennis S;Freitag CM;Geschwind DH;Haines JL;Klauck SM;McMahon WM;Maestrini E;Miller J;Monaco AP;Nelson SF;Nurnberger JI Jr;Oliveira G;Parr JR;Pericak-Vance MA;Piven J;Schellenberg GD;Scherer SW;Vicente AM;Wassink TH;Wijsman EM;Betancur C;Buxbaum JD;Cook EH;Gallagher L;Gill M;Hallmayer J;Paterson AD;Sutcliffe JS;Szatmari P;Vieland VJ;Hakonarson H;Devlin B
通讯作者：
Devlin B
影响因子：
30.8
作者：
Allen, Nicole C.;Bagade, Sachin;Bertram, Lars
通讯作者：
Bertram, Lars