Chun Tang

Applied Filters

Chun Tang
AuthorRemove filter

Clear all

People

Colleagues

Aidong Zhang (12)
Chun Tang (12)
Murali Krishna Ramanathan (6)
Daxin Jiang (4)
Jian Pei (4)
Chuan Lin (2)
L Zhang (1)
Li Zhang (1)
Li Zhang (1)
Yuqing Song (1)

Publication

Journal/Magazine Names

Knowledge and Information Systems (2)
Bioinformatics (1)
Distributed and Parallel Databases (1)
IEEE Transactions on Knowledge and Data Engineering (1)

Proceedings/Book Names

Automatic phenotype structure mining underlying gene expression profiles (1)
BIBE '01: Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering (1)
CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management (1)
CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management (1)
HICSS '02: Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 6 - Volume 6 (1)
KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (1)
KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (1)

All Publications

Proceedings (6)
Other periodicals (5)
Doctoral Theses (1)

Publisher

Association for Computing Machinery (4)
IEEE Computer Society (2)
Springer-Verlag (2)
IEEE Educational Activities Department (1)
Kluwer Academic Publishers (1)
Oxford University Press, Inc. (1)
State University of New York at Buffalo (1)

Publication Date

Upon changing this filter the page will automatically refresh

Export Citations

Select Citation format

Please download or close your previous search result export first before starting a new bulk export.
Preview is not available.
By clicking download,a status dialog will open to start the export process. The process may takea few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress.
Download
- Download citation
- Copy citation

Publications

12 Results for: Author: Chun TangEdit SearchSave SearchFailed to save your search, try again laterSearch has been saved (My Saved Searches)

Save this search

Please login to be able to save your searches and receive alerts for new content matching your search criteria.

Searched The ACM Guide to Computing Literature (3,796,935 records)|Limit your search to The ACM Full-Text Collection (767,791 records)

Results

Showing 1 - 12of12 Results

Filters

Select All

Export Citations Save to Binder

Save to Binder

Create a New Binder

Name

per page:

Sort by: Latest

Recency
Earliest
Most Downloaded
Most Cited

article
Mining gene–sample–time microarray data: a coherent gene cluster discovery approach
- Daxin Jiang
  Nanyang Technological University, School of Computing Engineering, Blk N4, 2a-32, 639798, Nanyang Avenue, Singapore
  ,
- Jian Pei
  Simon Fraser University, Blk N4, 2a-32, 639798, Burnaby, Canada
  ,
- Murali Ramanathan
  State University of New York at Buffalo, Blk N4, 2a-32, 639798, Buffalo, New York, USA
  ,
- Chuan Lin
  State University of New York at Buffalo, Blk N4, 2a-32, 639798, Buffalo, New York, USA
  ,
- Chun Tang
  State University of New York at Buffalo, Blk N4, 2a-32, 639798, Buffalo, New York, USA
  ,
- Aidong Zhang
  State University of New York at Buffalo, Blk N4, 2a-32, 639798, Buffalo, New York, USA
Knowledge and Information Systems, Volume 13, Issue 3•November 2007, pp 305-335

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of ...
7Citation
Metrics
Total Citations7
Export Citations
Save to Binder
article
Mining gene---sample---time microarray data: a coherent gene cluster discovery approach
- Daxin Jiang
  School of Computing Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798
  ,
- Jian Pei
  Simon Fraser University, Burnaby, Canada
  ,
- Murali Ramanathan
  State University of New York at Buffalo, Buffalo, USA
  ,
- Chuan Lin
  State University of New York at Buffalo, Buffalo, USA
  ,
- Chun Tang
  State University of New York at Buffalo, Buffalo, USA
  ,
- Aidong Zhang
  State University of New York at Buffalo, Buffalo, USA
Knowledge and Information Systems, Volume 13, Issue 3•November 2007, pp 305-335

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene---sample---time microarray data sets that records the expression levels of ...
6Citation
Metrics
Total Citations6
Export Citations
Save to Binder
Doctoral Theses
Automatic phenotype structure mining underlying gene expression profiles
- Chun Tang
  State University of New York at Buffalo
  ,
- Aidong Zhang
  State University of New York at Buffalo
Recently introduced DNA microarray technology permits rapid, large-scale screening for patterns of gene expression and gives simultaneous, semi-quantitative readouts on the level of expression of thousands of genes for samples. The raw microarray data (...
0Citation
Metrics
Total Citations0
Export Citations
Save to Binder
research-article
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering, Volume 16, Issue 11•November 2004, pp 1370-1386 • https://doi.org/10.1109/TKDE.2004.68

DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression ...
196Citation
Metrics
Total Citations196
Export Citations
Save to Binder
Article
Published By ACM
Published By ACM
Mining coherent gene clusters from gene-sample-time microarray data
- Daxin Jiang
  State University of New York at Buffalo, NY
  ,
- Jian Pei
  State University of New York at Buffalo, NY and Simon Fraser University, Canada
  ,
- Murali Ramanathan
  State University of New York at Buffalo, NY
  ,
- Chun Tang
  State University of New York at Buffalo, NY
  ,
- Aidong Zhang
  State University of New York at Buffalo, NY
KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining•August 2004, pp 430-439• https://doi.org/10.1145/1014052.1014101

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene-sample-time microarray data sets, which records the expression levels of ...
56Citation
1,077
Downloads
Metrics
Total Citations56
Total Downloads1,077
Last 12 Months2
Last 6 weeks1
Export Citations
Save to Binder
Get Access
article
ESPD: a pattern detection model underlying gene expression profiles
- Chun Tang
  Department of Computer Science and Engineering
  ,
- Aidong Zhang
  Department of Computer Science and Engineering
  ,
- Murali Ramanathan
  Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA
Bioinformatics, Volume 20, Issue 6•April 2004, pp 829-838 • https://doi.org/10.1093/bioinformatics/btg486

Motivation: DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in ...
1Citation
Metrics
Total Citations1
Export Citations
Save to Binder
Article
Published By ACM
Published By ACM
Mining multiple phenotype structures underlying gene expression profiles
- Chun Tang
  State University of New York at Buffalo, Buffalo, NY
  ,
- Aidong Zhang
  State University of New York at Buffalo, Buffalo, NY
CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management•November 2003, pp 418-425• https://doi.org/10.1145/956863.956942

DNA microarray technology is now widely used in basic biomedical research for mRNA expression profiling and are increasingly being used to explore patterns of gene expression in clinical research. Automatically detecting phenotype structures from gene ...
2Citation
713
Downloads
Metrics
Total Citations2
Total Downloads713
Last 12 Months1
Export Citations
Save to Binder
Get Access
Article
Published By ACM
Published By ACM
Mining phenotypes and informative genes from gene expression data
- Chun Tang
  State University of New York at Buffalo, Buffalo, NY
  ,
- Aidong Zhang
  State University of New York at Buffalo, Buffalo, NY
  ,
- Jian Pei
  State University of New York at Buffalo, Buffalo, NY
KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining•August 2003, pp 655-660• https://doi.org/10.1145/956750.956835

Mining microarray gene expression data is an important research topic in bioinformatics with broad applications. While most of the previous studies focus on clustering either genes or samples, it is interesting to ask whether we can partition the ...
11Citation
698
Downloads
Metrics
Total Citations11
Total Downloads698
Last 12 Months2
Last 6 weeks1
Export Citations
Save to Binder
Get Access
article
VizCluster and its Application on Classifying Gene Expression Data
- Li Zhang
  Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. [email protected]
  ,
- Chun Tang
  Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. [email protected]
  ,
- Yuqing Song
  Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. [email protected]
  ,
- Aidong Zhang
  Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. [email protected]
  ,
- Murali Ramanathan
  Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA. [email protected]
Distributed and Parallel Databases, Volume 13, Issue 1•January 2003, pp 73-97 • https://doi.org/10.1023/A:1021517806825

Visualization enables us to find structures, features, patterns, and relationships in a dataset by presenting the data in various graphical forms with possible interactions. A visualization can provide a qualitative overview of large and complex ...
4Citation
Metrics
Total Citations4
Export Citations
Save to Binder
Article
Published By ACM
Published By ACM
An iterative strategy for pattern discovery in high-dimensional data sets
- Chun Tang
  State University of New York at Buffalo, Buffalo, NY
  ,
- Aidong Zhang
  State University of New York at Buffalo, Buffalo, NY
CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management•November 2002, pp 10-17• https://doi.org/10.1145/584792.584798

High-dimensional data representation in which each data item (termed target object) is described by many features, is a necessary component of many applications. For example, in DNA microarrays, each sample (target object) is represented by thousands of ...
9Citation
827
Downloads
Metrics
Total Citations9
Total Downloads827
Last 12 Months1
Export Citations
Save to Binder
Get Access
Article
Interactive Visualization and Analysis for Gene Expression Data
HICSS '02: Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 6 - Volume 6•January 2002, pp 143

HCDMG03Currently, the cDNA and genomic sequence projects are processing at such a rapid rate that more and more gene data become available. New methods are needed to efficiently and effectively analyze and visualize this data. In this paper, we present ...
3Citation
Metrics
Total Citations3
Export Citations
Save to Binder
Article
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
BIBE '01: Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering•March 2001, pp 41

DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Currently most research focuses on the interpretation of the meaning of the data. However, majority methods are supervised-based, less attention has been paid ...
16Citation
Metrics
Total Citations16
Export Citations
Save to Binder

Author Profile Pages

Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples:
History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:
- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.

Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
Future Direction: The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.

A direct search interface for Author Profiles will be built.

An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.

It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.

Bibliometrics

The ACM DL is a comprehensive repository of publications from the entire field of computing.

It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner

Chun Tang

Chun Tang

Applied Filters

People

Colleagues

Publication

Journal/Magazine Names

Proceedings/Book Names

All Publications

Content Type

Media Formats

Publisher

Publication Date

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach

Mining gene---sample---time microarray data: a coherent gene cluster discovery approach

Automatic phenotype structure mining underlying gene expression profiles

Cluster Analysis for Gene Expression Data: A Survey

Mining coherent gene clusters from gene-sample-time microarray data

ESPD: a pattern detection model underlying gene expression profiles

Mining multiple phenotype structures underlying gene expression profiles

Mining phenotypes and informative genes from gene expression data

VizCluster and its Application on Classifying Gene Expression Data

An iterative strategy for pattern discovery in high-dimensional data sets

Interactive Visualization and Analysis for Gene Expression Data

Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis