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Arthur Villordon, Christopher Clark, Don Ferrin and Don LaBonte

linear models (GLM) ( Benjamini and Leshno, 2005 ). Several of these methods are being used in data mining (DM) applications. DM involves the use of algorithms that explore data, develop models, and discover previously unknown patterns ( Maimon and Rokach

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Elisa Solis-Toapanta, Andrei Kirilenko and Celina Gómez

( Statista, 2016 ). In addition, there is an almost equal distribution between male and female users ( Statista, 2019 ). Because Reddit has a diverse userbase who regularly ask and/or answer questions, it enables the implementation of data mining and content

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Joseph Postman, Gayle Volk and Herb Aldwinckle

discovery by genomic data mining tools. In addition to using standard phenotype data when comparing different data sets, it is also important to know that cultivars with the same name used in different studies are indeed the same. Standardized genotyping

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Charles Meister

The three IR4 Programs(Food-Use, Ornamentals, Biopesticides) research pest control needs that originate from stakeholders in each state. Pest control needs are documented as Project Clearance Requests. Researchable projects are identified at the National Food UseWorkshop anda research plan is developed at National Headquarters. This year IR4will research magnitude of residue projects to secure labels on 25 pest product and vegetable crop combinations. The list of projects will be distributed. The IR4 Project, Southern Region has augmented this process by establishing the Southern Region Performance Program(SRPP). Research scientists are asked to submit funding proposals to evaluate pest control products. Each proposal is scrutinized to prioritize needs and identify the most appropriate pest control product technologies. Product registrants, IR4 coordinators and stakeholders are consulted before a final decision is made. More than 70 research scientists from all states in the Southern Region will participate in the SRPP in 2005. Research data will be documented by in the IR4 National Data Mining process and many new project requests will be produced and others expanded to provide workshop participants information as they set priorities for IR4 researchin year2006.

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K.S. Lewers, S.M.N. Styan, S.C. Hokanson and N.V. Bassil

Although simple sequence repeat (SSR) markers have been developed for species in the closely related genera Fragaria L. (strawberry) and Rubus L. (raspberry and blackberry), the number of SSRs available is insufficient for genetic mapping. Our objective was to use and compare multiple approaches for developing additional SSRs for Fragaria and Rubus. The approaches included: the development of SSRs from GenBank sequences from species of varied relatedness to Fragaria and Rubus and identified with two different data-mining methods (BLAST and SSRIT); the evaluation of some previously published SSRs designed from related species; and the development of SSRs from a genomic library made from F. ×ananassa Duschene ex Rozier `Earliglow'. When an SSR was developed from a known gene sequence, the location of the repeat in the gene was determined to evaluate the effect on amplification and polymorphism detection. Cross-generic amplification between closely related Fragaria and Rubus as well as transference from species of varied relatedness to Fragaria and Rubus also was evaluated and indicated limited transference within the subfamily Rosoideae. However, development of SSRs for Fragaria and Rubus from Rosa L. (rose) and Rosaceae genera outside Rosoideae was not efficient enough to be practical for new map development. SSRIT was superior to BLAST for identifying GenBank sequences containing repeats. SSRs developed from repeats found in either the 5′UTR (80% polymorphic) or 3′UTR (85% polymorphic) were most likely to detect polymorphisms, compared with those developed from coding regions (30%). SSRs developed from the genomic library were only slightly superior to GenBank-derived SSRs in their ability to detect polymorphisms.

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Xiaoxin Shi, Lili Yang, Guijun Yan and Guoqiang Du

plants by in vitro meristem tip culture Eur. J. Plant Pathol. 130 597 604 Tang, Q. Feng, M. 2007 DPS Data Processing System: Experimental design, statistical analysis, and data mining. Science Press, Beijing, China Tang, Q. Zhang, C. 2013 Data Processing

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Gayle M. Volk

entitled AmiGO provides access to search and browse the GO and the gene product annotations ( Carbon et al., 2009 ). Comparative data mining becomes realistic through the use of ontologies. Large whole-genome sequencing efforts in both model organisms

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Xiaolin Huang and Julieta Trevino Sherk

ratings were conducted. Correlation coefficient was calculated and scores were compared in a scatterplot using Orange, a data mining software suite featuring data analysis and visualization ( Demšar et al., 2004 ) ( Fig. 3 ). Fig. 3. Cluster graph

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Arthur Villordon, Christopher Clark, Tara Smith, Don Ferrin and Don LaBonte

.J. 1993 Machine learning methods for intelligent decision support: An introduction Decis. Support Syst. 10 79 83 Tan, P.N. Steinback, M. Kumar, V. 2005 Introduction to data mining Addison Wesley Boston

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coefficient of variation (CV), linear regression, and data mining approaches. A suitable method with a base and ceiling temperature of 60 and 90 °F, respectively, was identified through a combination of lowered CV, increased adjusted r 2 , and reduced mean