Our Work
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Tagline Title
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
asdf asdf asdf asdfasd fasdf
July 16, 2018
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
“ScholarGraph – an author publication visualization tool.”
L. Yeganova, G. Balasanov, et al,
In preparation.
Developed ScholarGraph, a dynamic author publication visualization tool, which given an author name, retrieves relevant PubMed publications, clusters them into topics, computes a title for each topic, and visualizes results in a Voronoi diagram. ScholarGraph also provides a capability to link from a topic to documents, as well as to find other authors who work on the same topic.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, et al,
In preparation.
Supported a project for developing a word-embeddings based method for identifying synonymous terms in biomedical literature. Created an interactive and web-interface and dashboard for annotating and collecting statistics on 1000 term pairs for 12 annotators.
“Discovering Themes in Biomedical Literature Using a Projection-Based Algorithm, L.”
Yeganova, S. Kim, G. Balasanov, and W. J. Wilbur
BMC Bioinformatics, 19, 269, 2018.
Created a web interface for visualizing biomedical topics/themes computed by a novel projection-based algorithm. Based on a set of 60K articles on Single Nucleotide Polymorphism in PubMed, one can explore the themes given a query term. In response to a query, the system retrieves themes ranked by the importance of query terms in them and presents to the user by displaying its top 5 scoring terms.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, K. Bennett, H. Liu, and W. J. Wilbur
LREC 2016
Workshop on Cross-Platform Text Mining and Natural Language Processing
Interoperability, pp. 38-41, 2016.
Introduced the new Drug-Drug Interaction corpus DDINCBI as a resource for evaluating and improving the DDI recognition methods. Created a web-based tool for annotating drug-drug interactions in PubMed.
Tagline Title
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
asdf asdf asdf asdfasd fasdf
July 16, 2018
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
“ScholarGraph – an author publication visualization tool.”
L. Yeganova, G. Balasanov, et al,
In preparation.
Developed ScholarGraph, a dynamic author publication visualization tool, which given an author name, retrieves relevant PubMed publications, clusters them into topics, computes a title for each topic, and visualizes results in a Voronoi diagram. ScholarGraph also provides a capability to link from a topic to documents, as well as to find other authors who work on the same topic.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, et al,
In preparation.
Supported a project for developing a word-embeddings based method for identifying synonymous terms in biomedical literature. Created an interactive and web-interface and dashboard for annotating and collecting statistics on 1000 term pairs for 12 annotators.
“Discovering Themes in Biomedical Literature Using a Projection-Based Algorithm, L.”
Yeganova, S. Kim, G. Balasanov, and W. J. Wilbur
BMC Bioinformatics, 19, 269, 2018.
Created a web interface for visualizing biomedical topics/themes computed by a novel projection-based algorithm. Based on a set of 60K articles on Single Nucleotide Polymorphism in PubMed, one can explore the themes given a query term. In response to a query, the system retrieves themes ranked by the importance of query terms in them and presents to the user by displaying its top 5 scoring terms.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, K. Bennett, H. Liu, and W. J. Wilbur
LREC 2016
Workshop on Cross-Platform Text Mining and Natural Language Processing
Interoperability, pp. 38-41, 2016.
Introduced the new Drug-Drug Interaction corpus DDINCBI as a resource for evaluating and improving the DDI recognition methods. Created a web-based tool for annotating drug-drug interactions in PubMed.
Tagline Title
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
asdf asdf asdf asdfasd fasdf
July 16, 2018
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
“ScholarGraph – an author publication visualization tool.”
L. Yeganova, G. Balasanov, et al,
In preparation.
Developed ScholarGraph, a dynamic author publication visualization tool, which given an author name, retrieves relevant PubMed publications, clusters them into topics, computes a title for each topic, and visualizes results in a Voronoi diagram. ScholarGraph also provides a capability to link from a topic to documents, as well as to find other authors who work on the same topic.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, et al,
In preparation.
Supported a project for developing a word-embeddings based method for identifying synonymous terms in biomedical literature. Created an interactive and web-interface and dashboard for annotating and collecting statistics on 1000 term pairs for 12 annotators.
“Discovering Themes in Biomedical Literature Using a Projection-Based Algorithm, L.”
Yeganova, S. Kim, G. Balasanov, and W. J. Wilbur
BMC Bioinformatics, 19, 269, 2018.
Created a web interface for visualizing biomedical topics/themes computed by a novel projection-based algorithm. Based on a set of 60K articles on Single Nucleotide Polymorphism in PubMed, one can explore the themes given a query term. In response to a query, the system retrieves themes ranked by the importance of query terms in them and presents to the user by displaying its top 5 scoring terms.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, K. Bennett, H. Liu, and W. J. Wilbur
LREC 2016
Workshop on Cross-Platform Text Mining and Natural Language Processing
Interoperability, pp. 38-41, 2016.
Introduced the new Drug-Drug Interaction corpus DDINCBI as a resource for evaluating and improving the DDI recognition methods. Created a web-based tool for annotating drug-drug interactions in PubMed.
Tagline Title
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
asdf asdf asdf asdfasd fasdf
July 16, 2018
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
“ScholarGraph – an author publication visualization tool.”
L. Yeganova, G. Balasanov, et al,
In preparation.
Developed ScholarGraph, a dynamic author publication visualization tool, which given an author name, retrieves relevant PubMed publications, clusters them into topics, computes a title for each topic, and visualizes results in a Voronoi diagram. ScholarGraph also provides a capability to link from a topic to documents, as well as to find other authors who work on the same topic.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, et al,
In preparation.
Supported a project for developing a word-embeddings based method for identifying synonymous terms in biomedical literature. Created an interactive and web-interface and dashboard for annotating and collecting statistics on 1000 term pairs for 12 annotators.
“Discovering Themes in Biomedical Literature Using a Projection-Based Algorithm, L.”
Yeganova, S. Kim, G. Balasanov, and W. J. Wilbur
BMC Bioinformatics, 19, 269, 2018.
Created a web interface for visualizing biomedical topics/themes computed by a novel projection-based algorithm. Based on a set of 60K articles on Single Nucleotide Polymorphism in PubMed, one can explore the themes given a query term. In response to a query, the system retrieves themes ranked by the importance of query terms in them and presents to the user by displaying its top 5 scoring terms.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, K. Bennett, H. Liu, and W. J. Wilbur
LREC 2016
Workshop on Cross-Platform Text Mining and Natural Language Processing
Interoperability, pp. 38-41, 2016.
Introduced the new Drug-Drug Interaction corpus DDINCBI as a resource for evaluating and improving the DDI recognition methods. Created a web-based tool for annotating drug-drug interactions in PubMed.
Tagline Title
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
asdf asdf asdf asdfasd fasdf
July 16, 2018
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
“ScholarGraph – an author publication visualization tool.”
L. Yeganova, G. Balasanov, et al,
In preparation.
Developed ScholarGraph, a dynamic author publication visualization tool, which given an author name, retrieves relevant PubMed publications, clusters them into topics, computes a title for each topic, and visualizes results in a Voronoi diagram. ScholarGraph also provides a capability to link from a topic to documents, as well as to find other authors who work on the same topic.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, et al,
In preparation.
Supported a project for developing a word-embeddings based method for identifying synonymous terms in biomedical literature. Created an interactive and web-interface and dashboard for annotating and collecting statistics on 1000 term pairs for 12 annotators.
“Discovering Themes in Biomedical Literature Using a Projection-Based Algorithm, L.”
Yeganova, S. Kim, G. Balasanov, and W. J. Wilbur
BMC Bioinformatics, 19, 269, 2018.
Created a web interface for visualizing biomedical topics/themes computed by a novel projection-based algorithm. Based on a set of 60K articles on Single Nucleotide Polymorphism in PubMed, one can explore the themes given a query term. In response to a query, the system retrieves themes ranked by the importance of query terms in them and presents to the user by displaying its top 5 scoring terms.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, K. Bennett, H. Liu, and W. J. Wilbur
LREC 2016
Workshop on Cross-Platform Text Mining and Natural Language Processing
Interoperability, pp. 38-41, 2016.
Introduced the new Drug-Drug Interaction corpus DDINCBI as a resource for evaluating and improving the DDI recognition methods. Created a web-based tool for annotating drug-drug interactions in PubMed.
Tagline Title
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
asdf asdf asdf asdfasd fasdf
July 16, 2018
Discovering themes in biomedical literature using a projection-based algorithm
BMC Bioinformatics
The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.
“ScholarGraph – an author publication visualization tool.”
L. Yeganova, G. Balasanov, et al,
In preparation.
Developed ScholarGraph, a dynamic author publication visualization tool, which given an author name, retrieves relevant PubMed publications, clusters them into topics, computes a title for each topic, and visualizes results in a Voronoi diagram. ScholarGraph also provides a capability to link from a topic to documents, as well as to find other authors who work on the same topic.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, et al,
In preparation.
Supported a project for developing a word-embeddings based method for identifying synonymous terms in biomedical literature. Created an interactive and web-interface and dashboard for annotating and collecting statistics on 1000 term pairs for 12 annotators.
“Discovering Themes in Biomedical Literature Using a Projection-Based Algorithm, L.”
Yeganova, S. Kim, G. Balasanov, and W. J. Wilbur
BMC Bioinformatics, 19, 269, 2018.
Created a web interface for visualizing biomedical topics/themes computed by a novel projection-based algorithm. Based on a set of 60K articles on Single Nucleotide Polymorphism in PubMed, one can explore the themes given a query term. In response to a query, the system retrieves themes ranked by the importance of query terms in them and presents to the user by displaying its top 5 scoring terms.
“Finding synonymous words for searching biomedical literature: a word embedding study and lessons.”
L. Yeganova, S. Kim, G. Balasanov, K. Bennett, H. Liu, and W. J. Wilbur
LREC 2016
Workshop on Cross-Platform Text Mining and Natural Language Processing
Interoperability, pp. 38-41, 2016.
Introduced the new Drug-Drug Interaction corpus DDINCBI as a resource for evaluating and improving the DDI recognition methods. Created a web-based tool for annotating drug-drug interactions in PubMed.
