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JournalISSN: 0973-2063

Bioinformation 

Biomedical Informatics
About: Bioinformation is an academic journal published by Biomedical Informatics. The journal publishes majorly in the area(s): Medicine & Docking (molecular). It has an ISSN identifier of 0973-2063. Over the lifetime, 2069 publications have been published receiving 20815 citations.


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Journal ArticleDOI
TL;DR: An R package termed Mfuzz is constructed implementing soft clustering tools for microarray data analysis, which can overcome shortcomings of conventional hard clustering techniques and offer further advantages.
Abstract: For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constructed an R package termed Mfuzz implementing soft clustering tools for microarray data analysis. The additional package Mfuzzgui provides a convenient TclTk based graphical user interface. Availability The R package Mfuzz and Mfuzzgui are available at http://itb1.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/index.html. Their distribution is subject to GPL version 2 license.

828 citations

Journal ArticleDOI
TL;DR: A simple to use web software, called WebSat, for microsatellite molecular marker prediction and development, which allows full control of parameters and the easy export of the resulting data, thus facilitating the development of micros satellite markers.
Abstract: UNLABELLED Simple sequence repeats (SSR), also known as microsatellites, have been extensively used as molecular markers due to their abundance and high degree of polymorphism. We have developed a simple to use web software, called WebSat, for microsatellite molecular marker prediction and development. WebSat is accessible through the Internet, requiring no program installation. Although a web solution, it makes use of Ajax techniques, providing a rich, responsive user interface. WebSat allows the submission of sequences, visualization of microsatellites and the design of primers suitable for their amplification. The program allows full control of parameters and the easy export of the resulting data, thus facilitating the development of microsatellite markers. AVAILABILITY The web tool may be accessed at http://purl.oclc.org/NET/websat/

305 citations

Journal Article
TL;DR: Better discrimination between exon areas and non-coding areas of a number of genomes when the sequences are mapped to EIIP indicator sequences and the power spectra of the same are taken in a sliding Kaiser window, compared to the existing method using a rectangular window which utilizes binary indicator sequences.
Abstract: In this paper, a revision for the existing method of locating exons by genomic signal processing technique employing four binary indicator sequences is presented. The existing method relies on the pronounced period three peaks observed in the Fourier power spectrum of the exon regions which are absent in non-coding regions. The authors have abandoned the four sequences all together and adopted a single 'EIIP indicator sequence' which is formed by substituting the electron-ion interaction pseudopotentials (EIIP) of the nucleotides A, G, C and T in the DNA sequence, reducing the computational overhead by 75%. The power spectrum of this sequence reveals period three peaks for exon regions. Also a number of exons have been identified which exhibit period three peaks when mapped to 'EIIP indicator sequence' and which do not show the same when the binary indicator sequences are employed. We could get better discrimination between exon areas and non-coding areas of a number of genomes when the sequences are mapped to EIIP indicator sequences and the power spectra of the same are taken in a sliding Kaiser window, compared to the existing method using a rectangular window which utilizes binary indicator sequences.

186 citations

Journal ArticleDOI
TL;DR: The DAVID Gene ID Conversion Tool (DICT), a web-based application, is able to convert user's input gene or gene product identifiers from one type to another in a more comprehensive and high-throughput manner with a uniquely enhanced ID-ID mapping database.
Abstract: UNLABELLED Our current biological knowledge is spread over many independent bioinformatics databases where many different types of gene and protein identifiers are used. The heterogeneous and redundant nature of these identifiers limits data analysis across different bioinformatics resources. It is an even more serious bottleneck of data analysis for larger datasets, such as gene lists derived from microarray and proteomic experiments. The DAVID Gene ID Conversion Tool (DICT), a web-based application, is able to convert user's input gene or gene product identifiers from one type to another in a more comprehensive and high-throughput manner with a uniquely enhanced ID-ID mapping database. AVAILABILITY http://david.abcc.ncifcrf.gov/conversion.jsp.

172 citations

Journal ArticleDOI
TL;DR: A simple but effective web application creating Venn diagrams from two or three gene lists, each gene in the group list has link to the related information in NCBI's Entrez Nucleotide database.
Abstract: Numerous methods are available to compare results of multiple microarray studies. One of the simplest but most effective of these procedures is to examine the overlap of resulting gene lists in a Venn diagram. Venn diagrams are graphical ways of representing interactions among sets to display information that can be read easily. Here we propose a simple but effective web application creating Venn diagrams from two or three gene lists. Each gene in the group list has link to the related information in NCBI's Entrez Nucleotide database. Availability GeneVenn is available for free at http://mcbc.usm.edu/genevenn/

168 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202365
2022211
202146
2020108
2019111
201875