A decision tree–based integrated testing strategy for tailor-made carcinogenicity evaluation of test substances using genotoxicity test results and chemical spaces

Mutagenesis ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Yurika Fujita ◽  
Hiroshi Honda ◽  
Masayuki Yamane ◽  
Takeshi Morita ◽  
Tomonari Matsuda ◽  
...  
Keyword(s):  
Decision Tree ◽  
Test Results ◽  
Testing Strategy ◽  
Genotoxicity Test ◽  
Integrated Testing

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays

2016 ◽  
Vol 76 ◽  
pp. 30-38 ◽  
Author(s):  
Donna S. Macmillan ◽  
Steven J. Canipa ◽  
Martyn L. Chilton ◽  
Richard V. Williams ◽  
Christopher G. Barber
Keyword(s):  
Decision Tree ◽  
In Silico ◽  
In Vitro Assays ◽  
Testing Strategy ◽  
In Silico Model ◽  
Skin Sensitisation ◽  
Integrated Testing

Bayesian integrated testing strategy to assess skin sensitization potency: from theory to practice

2013 ◽  
pp. n/a-n/a ◽  
Author(s):  
Joanna Jaworska ◽  
Yuri Dancik ◽  
Petra Kern ◽  
Frank Gerberick ◽  
Andreas Natsch
Keyword(s):  
Skin Sensitization ◽  
Testing Strategy ◽  
Theory To Practice ◽  
Integrated Testing

scholarly journals Animal use replacement, reduction, and refinement: Development of an integrated testing strategy for bioconcentration of chemicals in fish

2007 ◽  
Vol 3 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Watze de Wolf ◽  
Mike Comber ◽  
Peter Douben ◽  
Sylvia Gimeno ◽  
Martin Holt ◽  
...  
Keyword(s):  
Testing Strategy ◽  
Integrated Testing ◽  
Animal Use

scholarly journals Influenza colloidal gold method and blood routine tests combination for rapid diagnosis of influenza: a decision tree-based analysis

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Xiaoguang Li ◽  
Jing Chen ◽  
Fei Lin ◽  
Wei Wang ◽  
Jie Xu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Value ◽  
Colloidal Gold ◽  
Influenza A ◽  
Clinical Manifestations ◽  
Rapid Test ◽  
Test Results ◽  
Tree Model ◽  
Routine Blood ◽  
Test Result

AbstractRapid influenza diagnosis can facilitate targeted treatment and reduce antibiotic misuse. However, diagnosis efficacy remains unclear. This study examined the efficacy of a colloidal gold rapid test for rapid influenza diagnosis. Clinical characteristics of 520 patients with influenza-like illness presenting at a fever outpatient clinic during two influenza seasons (2017–2018; 2018–2019) were evaluated. The clinical manifestations and results of routine blood, colloidal gold, and nucleic acid tests were used to construct a decision tree with three layers, nine nodes, and five terminal nodes. The combined positive predictive value of a positive colloidal gold test result and monocyte level within 10.95–12.55% was 88.2%. The combined negative predictive value of a negative colloidal gold test result and white blood cell count > 9.075 × 109/L was 84.9%. The decision-tree model showed the satisfactory accuracy of an early influenza diagnosis based on colloidal gold and routine blood test results.

Weight of evidence skin sensitization safety assessment using the bayesian net integrated testing strategy

2018 ◽  
Vol 295 ◽  
pp. S70-S71
Author(s):  
P.S. Kern ◽  
C.A. Ryan ◽  
E. Deconinck ◽  
J. Jaworska ◽  
G. Dameron
Keyword(s):  
Safety Assessment ◽  
Weight Of Evidence ◽  
Skin Sensitization ◽  
Testing Strategy ◽  
Integrated Testing

scholarly journals Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up

2021 ◽  
Vol 6 (3) ◽  
pp. 178-188
Author(s):  
Adhitya Prayoga Permana ◽  
Kurniyatul Ainiyah ◽  
Khadijah Fahmi Hayati Holle
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Test Results ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Start Up ◽  
The Many ◽  
Start Ups

Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.

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