Screening features to improve the class prediction of acute myeloid leukemia and myelodysplastic syndrome

Li, Kaishi; Yang, Meixue; Sablok, Gaurav; Fan, Jianping; Zhou, Fengfeng

doi:10.1016/j.gene.2012.09.123

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…Because of its nature of repetitive structure, a simple measurement may not be sufficiently accurate to evaluate a CRISPR detection program. Some classification algorithms may be evaluated by sensitivity, specificity or area under the ROC curve353637, but these performance measurements do not reflect the detection accuracy of the detailed CRISPR structures. Consequently a few novel performance measurements have been defined for this purpose.…”

Section: Resultsmentioning

confidence: 99%

CRISPRdigger: detecting CRISPRs with better direct repeat annotations

Mai

Wang

et al. 2016

Sci Rep

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Clustered regularly interspaced short palindromic repeats (CRISPRs) are important genetic elements in many bacterial and archaeal genomes, and play a key role in prokaryote immune systems’ fight against invasive foreign elements. The CRISPR system has also been engineered to facilitate target gene editing in eukaryotic genomes. Using the common features of mis-annotated CRISPRs in prokaryotic genomes, this study proposed an accurate de novo CRISPR annotation program CRISPRdigger, which can take a partially assembled genome as its input. A comprehensive comparison with the three existing programs demonstrated that CRISPRdigger can recover more Direct Repeats (DRs) for CRISPRs and achieve a higher accuracy for a query genome. The program was implemented by Perl and all the parameters had default values, so that a user could annotate CRISPRs in a query genome by supplying only a genome sequence in the FASTA format. All the supplementary data are available at http://www.healthinformaticslab.org/supp/.

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Section: Resultsmentioning

confidence: 99%

CRISPRdigger: detecting CRISPRs with better direct repeat annotations

Mai

Wang

et al. 2016

Sci Rep

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“…This research used gene expression data to discriminate two types of nearly similar cancers Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The best classification results had achieved when feature selection methods used [6] .…”

Section: Related Workmentioning

confidence: 99%

A Comparison Study of Data Mining Algorithms for blood Cancer Prediction

Tayfor

Mohammed

2019

Passer Journal of Basic and Applied Sciences

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Cancer is a common disease that threats the life of one of every three people. This dangerous disease urgently requires early detection and diagnosis. The recent progress in data mining methods, such as classification, has proven the need for machine learning algorithms to apply to large datasets. This paper mainly aims to utilise data mining techniques to classify cancer data sets into blood cancer and non-blood cancer based on pre-defined information and post-defined information obtained after blood tests and CT scan tests. This research conducted using the WEKA data mining tool with 10-fold cross-validation to evaluate and compare different classification algorithms, extract meaningful information from the dataset and accurately identify the most suitable and predictive model. This paper depicted that the most suitable classifier with the best ability to predict the cancerous dataset is Multilayer perceptron with an accuracy of 99.3967%.

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CD3D and PRKCQ work together to discriminate between B-cell and T-cell acute lymphoblastic leukemia

Zhong

Liu

et al. 2016

Computers in Biology and Medicine

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Screening features to improve the class prediction of acute myeloid leukemia and myelodysplastic syndrome

Cited by 8 publications

References 40 publications

CRISPRdigger: detecting CRISPRs with better direct repeat annotations

CRISPRdigger: detecting CRISPRs with better direct repeat annotations

A Comparison Study of Data Mining Algorithms for blood Cancer Prediction

CD3D and PRKCQ work together to discriminate between B-cell and T-cell acute lymphoblastic leukemia

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