BioWeka—extending the Weka framework for bioinformatics

Gewehr, Jan E.; Szugat, Martin; Zimmer, Ralf

doi:10.1093/bioinformatics/btl671

Cited by 48 publications

(36 citation statements)

References 14 publications

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“…The results revealed that both algorithms violated some of the necessary properties identified as metamorphic relations indicating faults or unexpected behaviours. Also, some real faults were detected in the open-source machine learning tool Weka [110]. Finally, Jing et al [111] presented a set of metamorphic relations for association rule algorithms and evaluated them using a contact-lenses data set and the Weka tool.…”

Section: Machine Learningmentioning

confidence: 99%

A Survey on Metamorphic Testing

Segura

Fraser

Sánchez

et al. 2016

IIEEE Trans. Software Eng.

405

230

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Abstract-A test oracle determines whether a test execution reveals a fault, often by comparing the observed program output to the expected output. This is not always practical, for example when a program's input-output relation is complex and difficult to capture formally. Metamorphic testing provides an alternative, where correctness is not determined by checking an individual concrete output, but by applying a transformation to a test input and observing how the program output "morphs" into a different one as a result. Since the introduction of such metamorphic relations in 1998, many contributions on metamorphic testing have been made, and the technique has seen successful applications in a variety of domains, ranging from web services to computer graphics. This article provides a comprehensive survey on metamorphic testing: It summarises the research results and application areas, and analyses common practice in empirical studies of metamorphic testing as well as the main open challenges.

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Section: Machine Learningmentioning

confidence: 99%

A Survey on Metamorphic Testing

Segura

Fraser

Sánchez

et al. 2016

IIEEE Trans. Software Eng.

405

230

View full text Add to dashboard Cite

show abstract

“…Weka is a user-friendly machine learning software package with a graphical user interface [27]. Weka is widely utilized in biology, bioinformatics, and biochemistry [28]. For each dataset, we ranked five features (state, migration distance (long or short), and maximum, minimum, and mean temperature) according to their association level with changes in the migration pattern at arrival date, departure date, and duration of stay.…”

Section: Feature Selection Analysismentioning

confidence: 99%

Shifts in Bird Migration Timing in North American Long-Distance and Short-Distance Migrants Are Associated with Climate Change

Zaifman

Shan

et al. 2017

International Journal of Zoology

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Bird migration is a synchronized event that has evolved over thousands of years. Changing temperatures due to climate change threaten the intricacies of migration timing for birds; however, the extent of these changes has only recently begun to be addressed. Utilizing data from the citizen-science website eBird and historical temperature data, we analyzed bird migration timing in two states warming quickly (Alaska and Maine) and one warming gradually (South Carolina). Using linear regressions, we looked at relationships between different temperature indices and year with bird migration timing from 2010 to 2016. Bird migration through all three states, regardless of warming rate, showed similar rates of alterations. Additionally, in every state over half of the birds that had altered migration timing were long-distance migrants. Furthermore, we performed feature selection to determine important factors for changing migration timing of birds. Changes to summer resident and transient bird migration were most influenced by state. In winter resident migration, departure date and length of stay were most influenced by maximum temperature, while arrival date was most associated with minimum temperature. Relationships between changing temperatures and migration timing suggest that global climate change may have consequential effects on all bird migration patterns throughout the United States.

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“…Weka is a collection of state-of-the-art data mining algorithms and data preprocessing methods for a wide range of tasks such as data preprocessing, attribute selection, clustering, and classification. Weka has been used in prior research both in the field of clinical data mining [34] and in bioinformatics [35].…”

Section: Modelingmentioning

confidence: 99%