Arrowsmith two-node search interface: A tutorial on finding meaningful links between two disparate sets of articles in MEDLINE

Neil R. Smalheiser, Vetle Ingvald Torvik, Wei Zhou

Research output: Contribution to journalArticle

Abstract

The Arrowsmith two-node search is a strategy that is designed to assist biomedical investigators in formulating and assessing scientific hypotheses. More generally, it allows users to identify biologically meaningful links between any two sets of articles A and C in PubMed, even when these share no articles or authors in common and represent disparate topics or disciplines. The key idea is to relate the two sets of articles via title words and phrases (B-terms) that they share. We have created a free, public web-based version of the two-node search tool (http://arrowsmith.psych.uic.edu), have described its development and implementation, and have presented analyses of individual two-node searches. In this paper, we provide an updated tutorial intended for end-users, that covers the use of the tool for a variety of potential scientific use case scenarios. For example, one can assess a recent experimental, clinical or epidemiologic finding that connects two disparate fields of inquiry-identifying likely mechanisms to explain the finding, and choosing promising follow-up lines of investigation. Alternatively, one can assess whether the existing scientific literature lends indirect support to a hypothesis posed by the user that has not yet been investigated. One can also employ two-node searches to search for novel hypotheses. Arrowsmith provides a service that cannot be carried out feasibly via standard PubMed searches or by other available text mining tools.

Original languageEnglish (US)
Pages (from-to)190-197
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Volume94
Issue number2
DOIs
StatePublished - May 1 2009
Externally publishedYes

Fingerprint

PubMed
MEDLINE
Literature
Data Mining
Research Personnel

Keywords

  • Hypothesis
  • Literature-based discovery
  • Text mining
  • Web server

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics

Cite this

Arrowsmith two-node search interface : A tutorial on finding meaningful links between two disparate sets of articles in MEDLINE. / Smalheiser, Neil R.; Torvik, Vetle Ingvald; Zhou, Wei.

In: Computer Methods and Programs in Biomedicine, Vol. 94, No. 2, 01.05.2009, p. 190-197.

Research output: Contribution to journalArticle

@article{006430f800114314bbd590bf9b7d155e,
title = "Arrowsmith two-node search interface: A tutorial on finding meaningful links between two disparate sets of articles in MEDLINE",
abstract = "The Arrowsmith two-node search is a strategy that is designed to assist biomedical investigators in formulating and assessing scientific hypotheses. More generally, it allows users to identify biologically meaningful links between any two sets of articles A and C in PubMed, even when these share no articles or authors in common and represent disparate topics or disciplines. The key idea is to relate the two sets of articles via title words and phrases (B-terms) that they share. We have created a free, public web-based version of the two-node search tool (http://arrowsmith.psych.uic.edu), have described its development and implementation, and have presented analyses of individual two-node searches. In this paper, we provide an updated tutorial intended for end-users, that covers the use of the tool for a variety of potential scientific use case scenarios. For example, one can assess a recent experimental, clinical or epidemiologic finding that connects two disparate fields of inquiry-identifying likely mechanisms to explain the finding, and choosing promising follow-up lines of investigation. Alternatively, one can assess whether the existing scientific literature lends indirect support to a hypothesis posed by the user that has not yet been investigated. One can also employ two-node searches to search for novel hypotheses. Arrowsmith provides a service that cannot be carried out feasibly via standard PubMed searches or by other available text mining tools.",
keywords = "Hypothesis, Literature-based discovery, Text mining, Web server",
author = "Smalheiser, {Neil R.} and Torvik, {Vetle Ingvald} and Wei Zhou",
year = "2009",
month = "5",
day = "1",
doi = "10.1016/j.cmpb.2008.12.006",
language = "English (US)",
volume = "94",
pages = "190--197",
journal = "Computer Methods and Programs in Biomedicine",
issn = "0169-2607",
publisher = "Elsevier Ireland Ltd",
number = "2",

}

TY - JOUR

T1 - Arrowsmith two-node search interface

T2 - A tutorial on finding meaningful links between two disparate sets of articles in MEDLINE

AU - Smalheiser, Neil R.

AU - Torvik, Vetle Ingvald

AU - Zhou, Wei

PY - 2009/5/1

Y1 - 2009/5/1

N2 - The Arrowsmith two-node search is a strategy that is designed to assist biomedical investigators in formulating and assessing scientific hypotheses. More generally, it allows users to identify biologically meaningful links between any two sets of articles A and C in PubMed, even when these share no articles or authors in common and represent disparate topics or disciplines. The key idea is to relate the two sets of articles via title words and phrases (B-terms) that they share. We have created a free, public web-based version of the two-node search tool (http://arrowsmith.psych.uic.edu), have described its development and implementation, and have presented analyses of individual two-node searches. In this paper, we provide an updated tutorial intended for end-users, that covers the use of the tool for a variety of potential scientific use case scenarios. For example, one can assess a recent experimental, clinical or epidemiologic finding that connects two disparate fields of inquiry-identifying likely mechanisms to explain the finding, and choosing promising follow-up lines of investigation. Alternatively, one can assess whether the existing scientific literature lends indirect support to a hypothesis posed by the user that has not yet been investigated. One can also employ two-node searches to search for novel hypotheses. Arrowsmith provides a service that cannot be carried out feasibly via standard PubMed searches or by other available text mining tools.

AB - The Arrowsmith two-node search is a strategy that is designed to assist biomedical investigators in formulating and assessing scientific hypotheses. More generally, it allows users to identify biologically meaningful links between any two sets of articles A and C in PubMed, even when these share no articles or authors in common and represent disparate topics or disciplines. The key idea is to relate the two sets of articles via title words and phrases (B-terms) that they share. We have created a free, public web-based version of the two-node search tool (http://arrowsmith.psych.uic.edu), have described its development and implementation, and have presented analyses of individual two-node searches. In this paper, we provide an updated tutorial intended for end-users, that covers the use of the tool for a variety of potential scientific use case scenarios. For example, one can assess a recent experimental, clinical or epidemiologic finding that connects two disparate fields of inquiry-identifying likely mechanisms to explain the finding, and choosing promising follow-up lines of investigation. Alternatively, one can assess whether the existing scientific literature lends indirect support to a hypothesis posed by the user that has not yet been investigated. One can also employ two-node searches to search for novel hypotheses. Arrowsmith provides a service that cannot be carried out feasibly via standard PubMed searches or by other available text mining tools.

KW - Hypothesis

KW - Literature-based discovery

KW - Text mining

KW - Web server

UR - http://www.scopus.com/inward/record.url?scp=61749095126&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=61749095126&partnerID=8YFLogxK

U2 - 10.1016/j.cmpb.2008.12.006

DO - 10.1016/j.cmpb.2008.12.006

M3 - Article

C2 - 19185946

AN - SCOPUS:61749095126

VL - 94

SP - 190

EP - 197

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

SN - 0169-2607

IS - 2

ER -