PPTM 1.0 – Literature Mining of Protein Phosphorylation Using Dependency Parse Trees

PPTM 1.0

:: DESCRIPTION

PPTM is a web tool of novel text-mining method for efficiently retrieving and extracting protein phosphorylation information from literature.

::DEVELOPER

HI_Lab @ USTC

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Literature mining of protein phosphorylation using dependency parse trees.
Wang M, Xia H, Sun D, Chen Z, Wang M, Li A.
Methods. 2014 Jun 1;67(3):386-93. doi: 10.1016/j.ymeth.2014.01.008.

GeneCite 3.0 – High-throughput Literature and Pathway Mining

GeneCite 3.0

:: DESCRIPTION

GeneCite is a generalized query application that allows you to specify sophisticated sets of queries and generates a table of the number of bio-related records found for each query. The table can be presented as a Web page or in a standard spreadsheet format that will allow you to view the full output of only those queries that generate an interesting number of records. Currently, GeneCite allows you to submit ‘term’-type queries to web based PubMed database and UniSTS database, as well as PathwayScreen database stored in Microsoft Access software. You provide the application with partial queries in standard ASCII text files. These queries can then be combined in various ways to produce the set of queries that is sent to the databases, called a search. The program stores the number of citations returned for each query. The result of a search is a column (one-dimensional) or a table (two-dimensional) of those counts, depending on the type of search selected.

::DEVELOPER

Walter Reed Army Institute of Research

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 GeneCite

:: MORE INFORMATION

Citation

OMICS. 2007 Summer;11(2):143-51.
GeneCite: a stand-alone open source tool for high-throughput literature and pathway mining.
Hammamieh R, Chakraborty N, Wang Y, Laing M, Liu Z, Mulligan J, Jett M.

BioTextQuest+ – Literature Mining and Concept Discovery

BioTextQuest+

:: DESCRIPTION

BioTextQuest (BTQ) implements an enhanced version of the TextQuest algorithm (proposed by Iliopoulos et al., 2001), providing a user friendly web-based interface. BTQ collects abstracts fromMedline literature and OMIM databases matching a user query. Identification of relevant terms enables the representation of text records in a Vector Space Model and the calculation of pairwise document similarities. Employing suitable clustering algorithms, results are transformed into clusters of records along with their corresponding terms. BTQ, besides the document processing and clustering algorithms, relies on public web services such as NCBI eSearchReflect, and WhatIzIt to query biomedical databases and to annotate and enrich the biomedically significant terms. Data Integration and further bioinformatics analysis related to the tagged bioentities is available through BioCompendium service. Additional added-value features include a variety of clustering, stemming, co-occurence analysis and visualization algorithms/techniques allowing interactive result navigation.

 

::DEVELOPER

Ioannis Iliopoulos’ Bioinformatics & Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BioTextQuest+: A knowledge integration platform for literature mining and concept discovery.
Papanikolaou N, Pavlopoulos GA, Pafilis E, Theodosiou T, Schneider R, Satagopam VP, Ouzounis CA, Eliopoulos AG, Promponas VJ, Iliopoulos I.
Bioinformatics. 2014 Aug 6. pii: btu524.