mlr3: A modern object-oriented machine learning framework in R

Lang, Michel; Binder, Martin; Richter, Jakob; Schratz, Patrick; Pfisterer, Florian; Coors, Stefan; Au, Quay; Casalicchio, Giuseppe; Kotthoff, Lars; Bischl, Bernd

doi:10.21105/joss.01903

Cited by 261 publications

(219 citation statements)

References 4 publications

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“…The most important toolboxes for R are mlr, caret [149], and tidy models [160]. Most recently, the mlr3 [161] package has become available for complex multi-stage experiments with advanced functionality that use a broad range of machine learning functionality. The authors of this study also suggest that researchers explore the diverse functionality of this package in their studies.…”

Section: Recommendations and Future Prospectmentioning

confidence: 99%

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Sheykhmousa¹,

Mahdianpari

Ghanbari

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

641

195

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Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This paper reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on: (1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and (2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.

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Section: Recommendations and Future Prospectmentioning

confidence: 99%

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Sheykhmousa¹,

Mahdianpari

Ghanbari

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

641

195

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“…The other top-ranking AMP classifiers are not far behind AmpGram in the prediction of typical AMPs, but they have problems with longer peptides and proteins ( Figure 1 , Table 2 and Table 3 ), e.g., all CAMPR3 tools [ 27 ], which are based on: random forests (CAMPR3-RF), support vector machine (CAMPR3-SVM), artificial neural network (CAMPR3-ANN) and discriminant analysis (CAMPR3-DA), are characterized by decent sensitivity but very low specificity and precision. Sensitivity and specificity reflect the proportion of AMP and non-AMP sequences that are identified correctly as AMPs and non-AMPs, respectively, and precision the proportion of AMPs that actually are AMPs [ 46 , 47 ]. It means that all CAMPR3 algorithms, tend to ‘overpredict’ longer sequences as AMPs, i.e., generate a high number of false positive results.…”

Section: Resultsmentioning

confidence: 99%

Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram

Burdukiewicz

Sidorczuk

Rafacz

et al. 2020

IJMS

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Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

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“…Due to the lack of a unified method for proper application of ML process, even experienced bioinformaticians struggle with these time-consuming ML tasks. To provide a uniform interface and standardize the process of building predictive models, ML libraries were developed, for example mlr3 32 (https://mlr3.mlr-org.com), the classification and regression training (caret) 30,33 (https://rdrr.io/cran/caret), scikit-learn 34 (https://scikit-learn.org), mlPy 35 (https://mlpy.fbk.eu), SciPy (https://www.scipy.org/) including also ones for deep learning, such as TensorFlow (https://www.tensorflow.org/), PyTorch (https://pytorch.org/) and Keras (https://keras.io/). Since those libraries do not have a graphical user interface, usage requires extensive programming experience and general knowledge of R or Python making it inaccessible for many life science researchers.…”

Section: Main Textmentioning

confidence: 99%

SIMON: open-source knowledge discovery platform

Tomić

Kühn³

et al. 2020

Preprint

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Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

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mlr3: A modern object-oriented machine learning framework in R

Cited by 261 publications

References 4 publications

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram

SIMON: open-source knowledge discovery platform

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