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Machine Learning Approach in Mutation Testing [chapter]

Joanna Strug, Barbara Strug
<span title="">2012</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
This approach should help to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented in this paper.  ...  It is then used to predict whether a given test would detect a mutant or not. The prediction is carried out with the help of a classification algorithm.  ...  In this paper a machine learning approach based on similarity is used to analyse graphs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-34691-0_15">doi:10.1007/978-3-642-34691-0_15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vh5vujdczfe35hziwayxdho5gq">fatcat:vh5vujdczfe35hziwayxdho5gq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190218164457/http://pdfs.semanticscholar.org/0e59/da3348ab67d7542c51b394fd6ed81b7f7caf.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0e/59/0e59da3348ab67d7542c51b394fd6ed81b7f7caf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-34691-0_15"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Understanding cancer breakpoint determinants with omics data

Kseniia Cheloshkina, Maria Poptsova
<span title="">2020</span> <i title="Open Access Text Pvt, Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/72stzxbr4nd65oplwv55dzduwe" style="color: black;">Integrative Cancer Science and Therapeutics</a> </i> &nbsp;
Machine learning approach became an efficient method of predictive modeling because machine learning algorithms are able to consider multiple factors and their interactions and range them in an order of  ...  Machine learning models, that were successfully used to predict cancer point mutations, using the same features, could not achieve high performance in predicting cancer breakpoints.  ...  Machine learning approach is able to reveal weak dependencies that are not detected by statistical tests [29] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15761/icst.1000333">doi:10.15761/icst.1000333</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gxvxydw7wncuvhf2fdaz6tmsmy">fatcat:gxvxydw7wncuvhf2fdaz6tmsmy</a> </span>
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The Threat to the Validity of Predictive Mutation Testing: The Impact of Uncovered Mutants [article]

Alireza Aghamohammadi, Seyed-Hassan Mirian-Hosseinabadi
<span title="2020-05-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Predictive Mutation Testing (PMT) is a technique to predict whether a mutant will be killed by using machine learning approaches.  ...  Researchers have proposed various machine learning methods for PMT under the cross-project setting. However, they did not consider the impact of uncovered mutants.  ...  PMT is an approach based on machine learning techniques that predicts the execution results of mutants, namely killed or survived, without conducting mutation testing.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.11532v1">arXiv:2005.11532v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3xymofknijbxbhu3jj6cnkpqke">fatcat:3xymofknijbxbhu3jj6cnkpqke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200902002602/https://arxiv.org/pdf/2005.11532v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/88/df/88df04054e7a374341c76d2d8d65a53ad42c19a2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.11532v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Efficient Active Automata Learning via Mutation Testing

Bernhard K. Aichernig, Martin Tappler
<span title="2018-10-25">2018</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2ewief65xnegfh2slwulpgsnba" style="color: black;">Journal of automated reasoning</a> </i> &nbsp;
In this paper, we describe a randomised conformance testing approach which we extend with fault-based test selection.  ...  To show its effectiveness we apply the approach in learning experiments and compare its performance to a well-established testing technique, the partial W-method.  ...  During the development of the presented approach, all models used in Sect. 6 have been integrated into this collection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10817-018-9486-0">doi:10.1007/s10817-018-9486-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qgsww6e73ralhovr3ohwepyjoe">fatcat:qgsww6e73ralhovr3ohwepyjoe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505084229/https://link.springer.com/content/pdf/10.1007%2Fs10817-018-9486-0.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/43/1a/431abd627608b089cc9f84f376c789aede2adf04.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10817-018-9486-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC

Silvia Moreno, Mario Bonfante, Eduardo Zurek, Dmitry Cherezov, Dmitry Goldgof, Lawrence Hall, Matthew Schabath
<span title="2021-04-29">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ock7fsjjtrc35bouoqdhvl2zda" style="color: black;">Tomography</a> </i> &nbsp;
For the EGFR mutation, in the machine learning approach, there was an increase in the sensitivity from 0.66 to 0.75, and an increase in AUC from 0.68 to 0.70.  ...  For the KRAS mutation, both in the machine learning models (0.65 to 0.71 AUC) and the deep learning models (0.739 to 0.778 AUC), a significant increase in performance was found.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/tomography7020014">doi:10.3390/tomography7020014</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33946756">pmid:33946756</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p2irx4oa3ff7vnlwwt4sj3frlq">fatcat:p2irx4oa3ff7vnlwwt4sj3frlq</a> </span>
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Rosetta Custom Score Functions Accurately Predict ΔΔG of Mutations at Protein-Protein Interfaces Using Machine Learning [article]

Sumant Shringari, Sam Giannakoulias, John J. Ferrie, E. James Petersson
<span title="2020-03-18">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, accurate predictions of the effects of interfacial mutations to identify hotspots have remained elusive despite the myriad of modeling and machine learning methods tested.  ...  mutations.  ...  are fed into a variety of machine learning protocols.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.03.17.996066">doi:10.1101/2020.03.17.996066</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ms6qh32affcadb7e73vi4xkkma">fatcat:ms6qh32affcadb7e73vi4xkkma</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710095804/https://www.biorxiv.org/content/10.1101/2020.03.17.996066v1.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1c/a2/1ca2c009f2bd2afe6bca6122f12451944adc5e58.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.03.17.996066"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Different mutation and crossover set of genetic programming in an automated machine learning

Suraya Masrom, Masurah Mohamad, Shahirah Mohamed Hatim, Norhayati Baharun, Nasiroh Omar, Abdullah Sani Abd. Rahman
<span title="2020-09-01">2020</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3egptraynbg7xelu7qh6lwtd6i" style="color: black;">IAES International Journal of Artificial Intelligence (IJ-AI)</a> </i> &nbsp;
This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets.  ...  <span lang="EN-US">Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement  ...  Automated Machine Learning (AML) is the current approach proved beneficial to enexperienced data scientist and in some cases offered better performances than the conventional or manual approach [4] [5  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijai.v9.i3.pp402-408">doi:10.11591/ijai.v9.i3.pp402-408</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lnurby2t5ngmxmo3xvo3dhmiuu">fatcat:lnurby2t5ngmxmo3xvo3dhmiuu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106041425/http://ijai.iaescore.com/index.php/IJAI/article/download/20499/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/65/d5/65d5662eb8b8fe8ebc78ae9706dcc7598d33bb88.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijai.v9.i3.pp402-408"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability

Ramin Dehghanpoor, Evan Ricks, Katie Hursh, Sarah Gunderson, Roshanak Farhoodi, Nurit Haspel, Brian Hutchinson, Filip Jagodzinski
<span title="2018-01-27">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dstyyzbt45gknhqqjsh45p55h4" style="color: black;">Molecules</a> </i> &nbsp;
In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations.  ...  Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates.  ...  Acknowledgments: The work was funded in part by NSF grant CCF-1421871 (N.H.). We would also like to thank NVIDIA Corporation for donating a Titan Xp GPU used in this research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/molecules23020251">doi:10.3390/molecules23020251</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29382060">pmid:29382060</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jr7sy4ouzjhy3ofsrghq7t6auy">fatcat:jr7sy4ouzjhy3ofsrghq7t6auy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180727104749/https://res.mdpi.com/def502005cfee2c5e9fa57e65ba3e4ac973cb6b16af9f828cfcb25df7cbd9e7a8009b6d11f54ea3a1fddf047e943942407c5dd162256a5e22f496f1695d50635d457aeb23c1e5a006fc3609d399fb7c4ad3636a5a89a3fef00750bf265181cb2362e36a676edb5e5e571affbf5fa91a417a766c56bfdd53ffcf4df32928b9a8e2efd0b0e45c9d8619d8cf6bbe161b2c5db0661fcf497?filename=&amp;attachment=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5d/39/5d393ef108f5cbf9fc43026c7020f750698f45c3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/molecules23020251"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Machine Learning Predicts Accurately Mycobacterium tuberculosis Drug Resistance From Whole Genome Sequencing Data

Wouter Deelder, Sofia Christakoudi, Jody Phelan, Ernest Diez Benavente, Susana Campino, Ruth McNerney, Luigi Palla, Taane G. Clark
<span title="2019-09-26">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5%  ...  Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their  ...  In summary, our approach has shown that machine learning can robustly predict drug resistance and inform on its underlying mutations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2019.00922">doi:10.3389/fgene.2019.00922</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31616478">pmid:31616478</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6775242/">pmcid:PMC6775242</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zovu7mzlg5cuninbrf5drfjng4">fatcat:zovu7mzlg5cuninbrf5drfjng4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200305132624/https://researchonline.lshtm.ac.uk/id/eprint/4654651/1/fgene-10-00922.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/44/75/447565c02abb8d960c4bc93c1402440a1a56ee89.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2019.00922"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775242" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Machine Learning-Based Enzyme Engineering of PETase for Improved Efficiency in Degrading Non-Biodegradable Plastic [article]

Arjun Gupta, Sangeeta Agrawal
<span title="2022-01-12">2022</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
First, we trained three machine learning models to predict Topt with high performance, including Logistic Regression, Linear Regression and Random Forest.  ...  We used machine learning (ML) guided directed evolution to modify the PETase enzyme to have a higher optimal temperature (Topt), which would allow the enzyme to degrade PET more efficiently.  ...  In this type of directed evolution, machine learning is used to score and evaluate different possible mutations of enzymes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2022.01.11.475766">doi:10.1101/2022.01.11.475766</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qemshcimxnbtdnrr5nc4iiaoki">fatcat:qemshcimxnbtdnrr5nc4iiaoki</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220424110426/https://www.biorxiv.org/content/biorxiv/early/2022/01/12/2022.01.11.475766.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/55/51/5551b8835219f963df0c356df6ce230514608268.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2022.01.11.475766"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

An Ensemble Learning Approach for Cancer Drug Prediction [article]

Darsh Mandera, Anna Ritz
<span title="2020-08-11">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We developed a machine learning classifier to automatically predict a drug given a carcinogenic gene mutation profile.  ...  Ensemble Learning yielded prediction accuracy of 66% on the test set in predicting the correct drug.  ...  Methods This project involved multiple steps including data wrangling, training the machine learning model, and testing the machine learning model ( Figure 1 ).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.08.10.245142">doi:10.1101/2020.08.10.245142</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wf5yhu55vjh4hitdqdkkldzose">fatcat:wf5yhu55vjh4hitdqdkkldzose</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107174208/https://www.biorxiv.org/content/biorxiv/early/2020/08/11/2020.08.10.245142.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/32/6232035b81015f09618d28c3f8e2f503bc9727a8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.08.10.245142"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Classifying clinically actionable genetic mutations using KNN and SVM

Rohit Chivukula, T. Jaya Lakshmi, Sanku Satya Uday, Satti Thanuja Pavani
<span title="2021-12-01">2021</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trvfti3jm5hnxhei7rl7owpcqq" style="color: black;">Indonesian Journal of Electrical Engineering and Computer Science</a> </i> &nbsp;
In this work, an attempt is made to classify these data points using K-nearest neighbors (KNN) and linear support vector machines (SVM) in a multi class environment.  ...  Cancer is one of the major causes of death in humans.  ...  PROPOSED APPROACH Any machine learning task, the approach in this work also follows steps such as performing exploratory data analysis, preprocessing, then training the classification model with train  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijeecs.v24.i3.pp1672-1679">doi:10.11591/ijeecs.v24.i3.pp1672-1679</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uap5wbi3ybdujkbtwjjrdmoqti">fatcat:uap5wbi3ybdujkbtwjjrdmoqti</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220116161224/http://ijeecs.iaescore.com/index.php/IJEECS/article/download/24490/15816" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/43/ad/43adb84937b6935a2f9a83293c4d35605a3f422e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijeecs.v24.i3.pp1672-1679"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Predicting mutation score using source code and test suite metrics

Kevin Jalbert, Jeremy S. Bradbury
<span title="">2012</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wvv27s77dvd5flktsj246kcxwu" style="color: black;">2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE)</a> </i> &nbsp;
To improve the performance and reduce the cost of mutation testing, we propose a machine learning approach to predict mutation score based on a combination of source code and test suite metrics.  ...  Mutation testing has traditionally been used to evaluate the effectiveness of test suites and provide confidence in the testing process.  ...  Our proposed approach uses machine learning to predict the mutation score based on a combination of source code and test suite metrics of the code unit under test.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/raise.2012.6227969">doi:10.1109/raise.2012.6227969</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icse/JalbertB12.html">dblp:conf/icse/JalbertB12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/avldggke6vbonmlb4rfsbl6pkm">fatcat:avldggke6vbonmlb4rfsbl6pkm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130108012510/http://faculty.uoit.ca/bradbury/sqrlab/papers/RAISE2012/RAISE2012.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9a/78/9a780232dd66b6062a4e3c4002552713b88ce97b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/raise.2012.6227969"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Software Testing for Machine Learning

Dusica Marijan, Arnaud Gotlieb
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
More specifically, it discusses six key challenge areas for software testing of machine learning systems, examines current approaches to these challenges and highlights their limitations.  ...  This summary talk discusses the current state-of-the-art of software testing for machine learning.  ...  Metamorphic testing has been applied to machine learning classifiers (Xie, Ho, and et al. 2011 ) (Dwarakanath et al. 2018) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i09.7084">doi:10.1609/aaai.v34i09.7084</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/77qy7777nrc4zbofd3pasfjhsi">fatcat:77qy7777nrc4zbofd3pasfjhsi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201103155306/https://aaai.org/ojs/index.php/AAAI/article/download/7084/6938" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e2/c3/e2c312fae590b96e6cf61775b79cfab6d13e8cb4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i09.7084"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

BioExcel Webinar #64: BioExcel HPC Workflows: predictive power and its applications in pharmacology

Adam Hospital, Federica Battistini, Miłosz Wieczór
<span title="2022-04-26">2022</span> <i title="Zenodo"> Zenodo </i> &nbsp;
on the evolutionary path and host-selection mechanism of SARS-CoV-2. * DNAffinity: A Machine-Learning approach to predict DNA Binding affinities of Transcription Factors.  ...  Developed workflows have been tested in different use cases with an important link to the pharmacological field, demonstrating the predictive power of the BioExcel key applications assisted by HPC workflows  ...  • Large-scale SARS-CoV2 mutation analysis, including a study on the evolutionary path and host-selection mechanism of SARS-CoV-2. • DNAffinity: A Machine-Learning approach to predict DNA Binding affinities  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6512153">doi:10.5281/zenodo.6512153</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gjon3rq6svdyzizlaes7fmvcza">fatcat:gjon3rq6svdyzizlaes7fmvcza</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220504231323/https://zenodo.org/record/6512154/files/EGFR_Webinar_April2022.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a0/f5/a0f5c77fe0d955a3b621fac62203ebdfacbd5260.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6512153"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>
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