Human limits in machine learning: prediction of potato yield and disease using soil microbiome data
Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understa...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05977-2 |
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| author | Rosa Aghdam Xudong Tang Shan Shan Richard Lankau Claudia Solís-Lemus |
| author_facet | Rosa Aghdam Xudong Tang Shan Shan Richard Lankau Claudia Solís-Lemus |
| author_sort | Rosa Aghdam |
| collection | DOAJ |
| description | Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management. |
| format | Article |
| id | doaj-art-6525ebd742914ad1aa1c5a17ca490b4f |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-6525ebd742914ad1aa1c5a17ca490b4f2024-12-01T12:47:36ZengBMCBMC Bioinformatics1471-21052024-11-0125113310.1186/s12859-024-05977-2Human limits in machine learning: prediction of potato yield and disease using soil microbiome dataRosa Aghdam0Xudong Tang1Shan Shan2Richard Lankau3Claudia Solís-Lemus4Wisconsin Institute for Discovery, University of Wisconsin-MadisonWisconsin Institute for Discovery, University of Wisconsin-MadisonDepartment of Plant Pathology, University of Wisconsin-MadisonDepartment of Plant Pathology, University of Wisconsin-MadisonWisconsin Institute for Discovery, University of Wisconsin-MadisonAbstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.https://doi.org/10.1186/s12859-024-05977-2Soil microbiomePhenotype predictionMicrobiome networks analysisMachine learningBayesian neural networks |
| spellingShingle | Rosa Aghdam Xudong Tang Shan Shan Richard Lankau Claudia Solís-Lemus Human limits in machine learning: prediction of potato yield and disease using soil microbiome data BMC Bioinformatics Soil microbiome Phenotype prediction Microbiome networks analysis Machine learning Bayesian neural networks |
| title | Human limits in machine learning: prediction of potato yield and disease using soil microbiome data |
| title_full | Human limits in machine learning: prediction of potato yield and disease using soil microbiome data |
| title_fullStr | Human limits in machine learning: prediction of potato yield and disease using soil microbiome data |
| title_full_unstemmed | Human limits in machine learning: prediction of potato yield and disease using soil microbiome data |
| title_short | Human limits in machine learning: prediction of potato yield and disease using soil microbiome data |
| title_sort | human limits in machine learning prediction of potato yield and disease using soil microbiome data |
| topic | Soil microbiome Phenotype prediction Microbiome networks analysis Machine learning Bayesian neural networks |
| url | https://doi.org/10.1186/s12859-024-05977-2 |
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