PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast

Lai, Fu Jou; Chang, Hong; Wu, Wei

doi:10.1186/1471-2105-16-s18-s2

Cited by 6 publications

(7 citation statements)

References 31 publications

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“…In MVIAeval, we implemented two existing comprehensive performance scores [48, 49] to provide the overall performance comparison results for the selected benchmark microarray datasets and performance indices. The first one, termed the overall ranking score (ORS), is defined as the sum of the rankings of an algorithm for the selected performance indices and benchmark microarray datasets [48, 49].…”

Section: Methodsmentioning

confidence: 99%

“…The first one, termed the overall ranking score (ORS), is defined as the sum of the rankings of an algorithm for the selected performance indices and benchmark microarray datasets [48, 49]. The ranking of an algorithm for a specific performance index and a specific benchmark microarray dataset is d if its performance ranks #d among all the compared algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The other comprehensive performance score, termed the overall normalized score (ONS), is calculated by the sum of the normalized scores for the benchmark microarray datasets and performance indices [48, 49]. The ONS of the algorithm k is calculated like the following:where S ij ( k ) and N ij ( k ) is the original score and the normalized score of the algorithm k for the selected performance index i and benchmark microarray dataset j , respectively; I is the number of the selected indices; J is the number of the selected benchmark microarray datasets and m is the number of the algorithms being compared.…”

Section: Methodsmentioning

confidence: 99%

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MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

Jhou

2017

BMC Bioinformatics

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BackgroundMissing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers’ time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework.ResultsMVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables.ConclusionsMVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

Jhou

2017

BMC Bioinformatics

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“…Another several algorithms integrated more than two high-throughput data sources ( 19–23 ). Previous studies ( 24 , 25 ) have shown that the performance of an algorithm is varied under different evaluation criteria such as the existence of physical/genetic interaction and the overlap with the benchmark set of known cooperative TF pairs.…”

Section: Introductionmentioning

confidence: 99%

CoopTFD: a repository for predicted yeast cooperative transcription factor pairs

Lai

et al. 2016

Database

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In eukaryotic cells, transcriptional regulation of gene expression is usually accomplished by cooperative Transcription Factors (TFs). Therefore, knowing cooperative TFs is helpful for uncovering the mechanisms of transcriptional regulation. In yeast, many cooperative TF pairs have been predicted by various algorithms in the literature. However, until now, there is still no database which collects the predicted yeast cooperative TFs from existing algorithms. This prompts us to construct Cooperative Transcription Factors Database (CoopTFD), which has a comprehensive collection of 2622 predicted cooperative TF pairs (PCTFPs) in yeast from 17 existing algorithms. For each PCTFP, our database also provides five types of validation information: (i) the algorithms which predict this PCTFP, (ii) the publications which experimentally show that this PCTFP has physical or genetic interactions, (iii) the publications which experimentally study the biological roles of both TFs of this PCTFP, (iv) the common Gene Ontology (GO) terms of this PCTFP and (v) the common target genes of this PCTFP. Based on the provided validation information, users can judge the biological plausibility of a PCTFP of interest. We believe that CoopTFD will be a valuable resource for yeast biologists to study the combinatorial regulation of gene expression controlled by cooperative TFs.Database URL: http://cosbi.ee.ncku.edu.tw/CoopTFD/ or http://cosbi2.ee.ncku.edu.tw/CoopTFD/

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“…Since the comprehensive transcription factor (TF)-gene binding data, TF-gene regulation data and mutant phenotype data are available in yeast, this gives us an opportunity to construct the transcriptional regulation association and the mutant phenotype association in yeast. Moreover, we have many experiences in developing databases and web tools [ 6 - 13 ].…”

Section: Introductionmentioning

confidence: 99%

YAGM: a web tool for mining associated genes in yeast based on diverse biological associations

Wang

Jhou

et al. 2015

BMC Systems Biology

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BackgroundInvestigating association between genes can be used in understanding the relations of genes in biological processes. STRING and GeneMANIA are two well-known web tools which can provide a list of associated genes of a query gene based on diverse biological associations such as co-expression, co-localization, co-citation and so on. However, the transcriptional regulation association and mutant phenotype association have not been used in these two web tools. Since the comprehensive transcription factor (TF)-gene binding data, TF-gene regulation data and mutant phenotype data are available in yeast, we developed a web tool called YAGM (Yeast Associated Genes Miner) which constructed the transcriptional regulation association, mutant phenotype association and five commonly used biological associations to mine a list of associated genes of a query yeast gene.DescriptionIn YAGM, we collected seven kinds of datasets including TF-gene binding (TFB) data, TF-gene regulation (TFR) data, mutant phenotype (MP) data, functional annotation (FA) data, physical interaction (PI) data, genetic interaction (GI) data, and literature evidence (LE) data. Then by using the hypergeometric test to calculate the association scores of all gene pairs in yeast, we constructed seven biological associations including two transcriptional regulation associations (TFB association and TFR association), MP association, FA association, PI association, GI association, and LE association. Moreover, the expression profile association from SPELL database was also included in YAGM. When using YAGM, users can input a query gene and choose any possible subsets of the eight biological associations, then a list of associated genes of the query gene will be returned based on the chosen biological associations.ConclusionsIn this study, we presented the YAGM which provides eight biological associations for mining associated genes of a query gene in yeast. Among the eight biological associations constructed in YAGM, three (TFB association, TFR association, and MP association) are novel ones. By comparing the query results of two well-known web tools (STRING and GeneMANIA), we found that YAGM can find out distinct associated genes of a query gene. That is, YAGM can provide alternative candidates of associated genes for biologists to do further experimental investigation. We believe that YAGM will be a useful web tool for yeast biologists. YAGM is available online at http://cosbi3.ee.ncku.edu.tw/yagm/.

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PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast

Cited by 6 publications

References 31 publications

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

CoopTFD: a repository for predicted yeast cooperative transcription factor pairs

YAGM: a web tool for mining associated genes in yeast based on diverse biological associations

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