Random Forests machine learning applied to gas chromatography – Mass spectrometry derived average mass spectrum data sets for classification and characterisation of essential oils

Lebanov, Leo; Tedone, Laura; Ghiasvand, Alireza; Paull, Brett

doi:10.1016/j.talanta.2019.120471

Cited by 39 publications

(15 citation statements)

References 31 publications

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“…Two classification algorithms were used to classify healthy and IBS individuals. We built an AdaBoost classifiers (Montazeri et al, 2016) function with 1000 estimators utilizing Python's scikit-learn module (Pedregosa et al, 2011), and random forest classification models using the RandomForest package (Lebanov et al, 2020). All models were built with 10-fold cross-validation using data at the genus level.…”

Section: Discussionmentioning

confidence: 99%

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Liu

Yang

et al. 2021

Front. Cell. Infect. Microbiol.

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Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics.

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Section: Discussionmentioning

confidence: 99%

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Liu

Yang

et al. 2021

Front. Cell. Infect. Microbiol.

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“…With this improved method, we were able to identify the key features for identification of pre-miRNAs in two Drosophila species. Cross-validation tests [51]- [58] and classification results showed that the classification accuracy, precision rate and recall rate obtained with this method were increased compared with those of previous reports.…”

Section: Introductionmentioning

confidence: 58%

An Improved Method for Identification of Pre-miRNA in Drosophila

Chen

Wang

2020

IEEE Access

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Identification of microRNAs is important in studies of regulation of gene expression in many biologyical processes. In this study, we developed an improved method for identification of microRNAs in Drosophila. We used the iLearn, PyFeat, and Pse-in-One methods to extract the features and then used Max-Relevance-Max-Distance (MRMD2.0) and t-Distributed Stochastic Neighbour Embedding (t-SNE) to reduce dimension of the features and the random forest classifier in Weka to identify miRNAs. With this method, we found that the discriminative features for identification of pre-miRNAs were, in Drosophila melanogaster, the occurrences of G_GUG and C_AGU when the value of the feature vector was greater than 2, and in Drosophila pseudoobscura, the 4-tuple nucleotide composition and the occurrence of 4-length neighbouring nucleic acids when the value of the feature vector was less than 0.02. These vectors covered all compositional information or the frequency of bases. Classification results showed the classification accuracy was 95.7% and 93.6%, the precision rate was 95.8% and 93.6%, and the recall rate was 95.7% and 93.6% in Drosophila melanogaster and Drosophila pseudoobscura, respectively, which are higher than those reported in previous studies.

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“…In another investigation random forests machine learning algorithm was applied to the classification of 20 different EOs in order to use their differences in chemical profiles as chemical markers for EO classification and determination of the quality [ 174 ]. The total chromatogram average mass spectra (TCAMS) and segment average mass spectra (SAMS) were created from three-way raw gas chromatography–mass spectra data, and the resulted SAMS data set showed superior potential for quality assurance, compared with TCAMS, while TCAMS is much faster and more readily created.…”

Section: Machine Learning Analysis In Support Of the Eos Usementioning

confidence: 99%

Polymeric Carriers Designed for Encapsulation of Essential Oils with Biological Activity

Chiriac¹,

Rusu²,

Niţă³

et al. 2021

Pharmaceutics

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The article reviews the possibilities of encapsulating essential oils EOs, due to their multiple benefits, controlled release, and in order to protect them from environmental conditions. Thus, we present the natural polymers and the synthetic macromolecular chains that are commonly used as networks for embedding EOs, owing to their biodegradability and biocompatibility, interdependent encapsulation methods, and potential applicability of bioactive blend structures. The possibilities of using artificial intelligence to evaluate the bioactivity of EOs—in direct correlation with their chemical constitutions and structures, in order to avoid complex laboratory analyses, to save money and time, and to enhance the final consistency of the products—are also presented.

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Random Forests machine learning applied to gas chromatography – Mass spectrometry derived average mass spectrum data sets for classification and characterisation of essential oils

Cited by 39 publications

References 31 publications

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

An Improved Method for Identification of Pre-miRNA in Drosophila

Polymeric Carriers Designed for Encapsulation of Essential Oils with Biological Activity

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