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AlphaML : A clear, ...
AlphaML : A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data
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- Nasimian, Ahmad (författare)
- Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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- Younus, Saleena (författare)
- Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,Molekylär cancerforskning,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,Molecular Cancer Research,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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- Tatli, Özge (författare)
- Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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- Hammarlund, Emma U. (författare)
- Lund University,Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Molekylär evolution,Forskargrupper vid Lunds universitet,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Molecular Evolution,Lund University Research Groups
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- Pienta, Kenneth J. (författare)
- Johns Hopkins University School of Medicine
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- Rönnstrand, Lars (författare)
- Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,Stamcellscentrum (SCC),Avdelningen för stamcellsforskning,Molekylär cancerforskning,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,Stem Cell Center,Division of stem cell research,Molecular Cancer Research,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital
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- Kazi, Julhash U. (författare)
- Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: Patterns. - 2666-3899. ; 5:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We introduce alphaML, a user-friendly platform that provides clear, legible, explainable, transparent, and elucidative (CLETE) binary classification models with comprehensive customization options. AlphaML offers feature selection, hyperparameter search, sampling, and normalization methods, along with 15 machine learning algorithms with global and local interpretation. We have integrated a custom metric for hyperparameter search that considers both training and validation scores, safeguarding against under- or overfitting. Additionally, we employ the NegLog2RMSL scoring method, which uses both training and test scores for a thorough model evaluation. The platform has been tested using datasets from multiple domains and offers a graphical interface, removing the need for programming expertise. Consequently, alphaML exhibits versatility, demonstrating promising applicability across a broad spectrum of tabular data configurations.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- deep tabular learning
- drug sensitivity prediction
- DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
- ensemble learning
- explainable AI
- feature selection
- hyperparameter optimization
- machine learning
- precision medicine
- TabNet
- XGBoost
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- art (ämneskategori)
- ref (ämneskategori)
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