Overleaf用にpubmedからcitation 情報を取り出す

  • Overleaf

qiita.comは、オンライン上でLaTex文書を作るサイト

  • テンプレートもたくさんあるので便利
  • LaTexなので引用文献も
\cite{Yamada2020}
  • と文中に書き込んでおいて、引用文献を書きたい場所に
%This is where your bibliography is generated. Make sure that your .bib file is actually called library.bib
\bibliography{library}

%This defines the bibliographies style. Search online for a list of available styles.
\bibliographystyle{abbrv}
  • のようにしておき、library.bibなるファイルを置いておき、Yamada2020に対応する情報を、然るべき体裁で張り付けて置けばよい
  • そのしかるべき体裁をpubmedからひょいっと取って来るには、pubmedIDを確認し、このサイト

www.bioinformatics.orgのクエリにpubmedIDを入れて検索の上、 incl. abstract オプションで exportすればよい

% 32275673 
@Article{pmid32275673,
   Author="Okada, D.  and Yamada, R. ",
   Title="{{D}ecomposition of a set of distributions in extended exponential family form for distinguishing multiple oligo-dimensional marker expression profiles of single-cell populations and visualizing their dynamics}",
   Journal="PLoS One",
   Year="2020",
   Volume="15",
   Number="4",
   Pages="e0231250",
   Abstract={Single-cell expression analysis is an effective tool for studying the dynamics of cell population profiles. However, the majority of statistical methods are applied to individual profiles and the methods for comparing multiple profiles simultaneously are limited. In this study, we propose a nonparametric statistical method, called Decomposition into Extended Exponential Family (DEEF), that embeds a set of single-cell expression profiles of several markers into a low-dimensional space and identifies the principal distributions that describe their heterogeneity. We demonstrate that DEEF can appropriately decompose and embed sets of theoretical probability distributions. We then apply DEEF to a cytometry dataset to examine the effects of epidermal growth factor stimulation on an adult human mammary gland. It is shown that DEEF can describe the complex dynamics of cell population profiles using two parameters and visualize them as a trajectory. The two parameters identified the principal patterns of the cell population profile without prior biological assumptions. As a further application, we perform a dimensionality reduction and a time series reconstruction. DEEF can reconstruct the distributions based on the top coordinates, which enables the creation of an artificial dataset based on an actual single-cell expression dataset. Using the coordinate system assigned by DEEF, it is possible to analyze the relationship between the attributes of the distribution sample and the features or shape of the distribution using conventional data mining methods.}
}
  • のように返って来るので、識別子の行 "% 32275673 "を削り、
@Article{pmid32375029,
  • の部分を、LaTex本文の記載に合わせて
@Article{Yamada2020,
  • と書き換えれば出来上がり
  • もちろん、書き換えずに、pubmedIDのまま、LaTex本文で引用しておけば、その書き換えの手間も不要