scholarly journals Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures

2019 ◽  
Author(s):  
Angelina Volkova ◽  
Kelly V. Ruggles
Keyword(s):  
Random Forest ◽  
Autoimmune Disease ◽  
Autoimmune Diseases ◽  
Gut Microbiome ◽  
Multidimensional Data ◽  
Support Vector ◽  
Control Analysis ◽  
Extreme Gradient Boosting ◽  
Random Forest Tree ◽  
Disease Specific

ABSTRACTWithin the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, many results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a comprehensive re-analysis of 42 studies focusing on the gut microbiome in twelve autoimmune diseases to identify a microbial signature predictive of multiple sclerosis (MS), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and general autoimmune disease using both 16S rRNA sequencing data and shotgun metagenomics data. To do this, we used four machine learning algorithms, random forest, eXtreme Gradient Boosting (XGBoost), ridge regression and support vector machine with radial kernel and recursive feature elimination to rank disease predictive taxa comparing disease vs. healthy participants and pairwise comparisons of each disease. Comparing the performance of these models, we found XGBoost and random forest, tree-based methods capable of handling sparse multidimensional data, to consistently produce the best results. Through this modeling, we identified a number of taxa consistently identified as dysregulated in a general autoimmune disease model including Odoribacter, Lachnospiraceae Clostridium and Mogibacteriaceae implicating all as potential factors connecting the gut microbiome and to autoimmune response. Further, we computed pairwise comparison models to identify disease specific taxa signatures highlighting a role for Peptostreptococcaceae and Ruminococcaceae Gemmiger in IBD and Akkermansia, Butyricicoccus and Mogibacteriaceae in MS. We then connected a subset of these taxa with potential metabolic alterations based on metagenomic/metabolomic correlation analysis, identifying 250 metabolites associated with autoimmunity-predictive taxa.

scholarly journals Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures

2021 ◽  
Vol 12 ◽  
Author(s):  
Angelina Volkova ◽  
Kelly V. Ruggles
Keyword(s):  
Random Forest ◽  
Autoimmune Disease ◽  
Autoimmune Diseases ◽  
Gut Microbiome ◽  
Multidimensional Data ◽  
Support Vector ◽  
Control Analysis ◽  
Shotgun Metagenomics ◽  
Extreme Gradient Boosting ◽  
Disease Specific

Within the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, many results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a comprehensive re-analysis of 42 studies focusing on the gut microbiome in 12 autoimmune diseases to identify a microbial signature predictive of multiple sclerosis (MS), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and general autoimmune disease using both 16S rRNA sequencing data and shotgun metagenomics data. To do this, we used four machine learning algorithms, random forest, eXtreme Gradient Boosting (XGBoost), ridge regression, and support vector machine with radial kernel and recursive feature elimination to rank disease predictive taxa comparing disease vs. healthy participants and pairwise comparisons of each disease. Comparing the performance of these models, we found the two tree-based methods, XGBoost and random forest, most capable of handling sparse multidimensional data, to consistently produce the best results. Through this modeling, we identified a number of taxa consistently identified as dysregulated in a general autoimmune disease model including Odoribacter, Lachnospiraceae Clostridium, and Mogibacteriaceae implicating all as potential factors connecting the gut microbiome to autoimmune response. Further, we computed pairwise comparison models to identify disease specific taxa signatures highlighting a role for Peptostreptococcaceae and Ruminococcaceae Gemmiger in IBD and Akkermansia, Butyricicoccus, and Mogibacteriaceae in MS. We then connected a subset of these taxa with potential metabolic alterations based on metagenomic/metabolomic correlation analysis, identifying 215 metabolites associated with autoimmunity-predictive taxa.

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

2022 ◽  
pp. ASN.2021040538
Author(s):  
Arthur M. Lee ◽  
Jian Hu ◽  
Yunwen Xu ◽  
Alison G. Abraham ◽  
Rui Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Support Vector ◽  
Pathway Enrichment Analysis ◽  
Disease Etiology ◽  
Content Type ◽  
Extreme Gradient Boosting ◽  
Pediatric Ckd

BackgroundUntargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).MethodsUntargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.ResultsML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome–derived histidine metabolites.ConclusionML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome–derived histidine metabolites are associated with OU.

scholarly journals Machine learning as a successful approach for predicting complex spatio–temporal patterns in animal species abundance

2021 ◽  
pp. 289-301
Author(s):  
B. Martín ◽  
J. González–Arias ◽  
J. A. Vicente–Vírseda
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Animal Species ◽  
Temporal Patterns ◽  
Additive Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Spatio Temporal

Our aim was to identify an optimal analytical approach for accurately predicting complex spatio–temporal patterns in animal species distribution. We compared the performance of eight modelling techniques (generalized additive models, regression trees, bagged CART, k–nearest neighbors, stochastic gradient boosting, support vector machines, neural network, and random forest –enhanced form of bootstrap. We also performed extreme gradient boosting –an enhanced form of radiant boosting– to predict spatial patterns in abundance of migrating Balearic shearwaters based on data gathered within eBird. Derived from open–source datasets, proxies of frontal systems and ocean productivity domains that have been previously used to characterize the oceanographic habitats of seabirds were quantified, and then used as predictors in the models. The random forest model showed the best performance according to the parameters assessed (RMSE value and R2). The correlation between observed and predicted abundance with this model was also considerably high. This study shows that the combination of machine learning techniques and massive data provided by open data sources is a useful approach for identifying the long–term spatial–temporal distribution of species at regional spatial scales.

scholarly journals Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

2021 ◽  
Vol 9 (12) ◽  
pp. 1-10
Author(s):  
Harsha A K
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Steady Increase ◽  
Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

Abstract 15895: Machine Learning Algorithms to Predict Major Adverse Cardiovascular Events in Patients Undergoing Orthotopic Liver Transplantation: A Retrospective Cohort Study

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
vardhmaan jain ◽  
Vikram Sharma ◽  
Agam Bansal ◽  
Cerise Kleb ◽  
Chirag Sheth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Cardiovascular Events ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Major Adverse Cardiovascular Events ◽  
Support Vector ◽  
Post Transplant ◽  
Extreme Gradient Boosting ◽  
All Cause Mortality

Background: Post-transplant major adverse cardiovascular events (MACE) are amongst the leading cause of death amongst orthotopic liver transplant(OLT) recipients. Despite years of guideline directed therapy, there are limited data on predictors of post-OLT MACE. We assessed if machine learning algorithms (MLA) can predict MACE and all-cause mortality in patients undergoing OLT. Methods: We tested three MLA: support vector machine, extreme gradient boosting(XG-Boost) and random forest with traditional logistic regression for prediction of MACE and all-cause mortality on a cohort of consecutive patients undergoing OLT at our center between 2008-2019. The cohort was randomly split into a training (80%) and testing (20%) cohort. Model performance was assessed using c-statistic or AUC. Results: We included 1,459 consecutive patients with mean ± SD age 54.2 ± 13.8 years, 32% female who underwent OLT. There were 199 (13.6%) MACE and 289 (20%) deaths at a mean follow up of 4.56 ± 3.3 years. The random forest MLA was the best performing model for predicting MACE [AUC:0.78, 95% CI: 0.70-0.85] as well as mortality [AUC:0.69, 95% CI: 0.61-0.76], with all models performing better when predicting MACE vs mortality. See Table and Figure. Conclusion: Random forest machine learning algorithms were more predictive and discriminative than traditional regression models for predicting major adverse cardiovascular events and all-cause mortality in patients undergoing OLT. Validation and subsequent incorporation of MLA in clinical decision making for OLT candidacy could help risk stratify patients for post-transplant adverse cardiovascular events.

scholarly journals Modelos de machine learning para predição do sucesso de startups

2021 ◽  
Vol 12 (2) ◽  
pp. 28-55
Author(s):  
Fabiano Rodrigues ◽  
Francisco Aparecido Rodrigues ◽  
Thelma Valéria Rocha Rodrigues
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Initial Public Offering ◽  
Gradient Boosting ◽  
Support Vector ◽  
Trade Offs ◽  
Extreme Gradient Boosting ◽  
Public Offering

Este estudo analisa resultados obtidos com modelos de machine learning para predição do sucesso de startups. Como proxy de sucesso considera-se a perspectiva do investidor, na qual a aquisição da startup ou realização de IPO (Initial Public Offering) são formas de recuperação do investimento. A revisão da literatura aborda startups e veículos de financiamento, estudos anteriores sobre predição do sucesso de startups via modelos de machine learning, e trade-offs entre técnicas de machine learning. Na parte empírica, foi realizada uma pesquisa quantitativa baseada em dados secundários oriundos da plataforma americana Crunchbase, com startups de 171 países. O design de pesquisa estabeleceu como filtro startups fundadas entre junho/2010 e junho/2015, e uma janela de predição entre junho/2015 e junho/2020 para prever o sucesso das startups. A amostra utilizada, após etapa de pré-processamento dos dados, foi de 18.571 startups. Foram utilizados seis modelos de classificação binária para a predição: Regressão Logística, Decision Tree, Random Forest, Extreme Gradiente Boosting, Support Vector Machine e Rede Neural. Ao final, os modelos Random Forest e Extreme Gradient Boosting apresentaram os melhores desempenhos na tarefa de classificação. Este artigo, envolvendo machine learning e startups, contribui para áreas de pesquisa híbridas ao mesclar os campos da Administração e Ciência de Dados. Além disso, contribui para investidores com uma ferramenta de mapeamento inicial de startups na busca de targets com maior probabilidade de sucesso.   

scholarly journals Research on dairy products detection based on machine learning algorithm

2022 ◽  
Vol 355 ◽  
pp. 03008
Author(s):  
Yang Zhang ◽  
Lei Zhang ◽  
Yabin Ma ◽  
Jinsen Guan ◽  
Zhaoxia Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Electronic Nose ◽  
Milk Fat ◽  
Dairy Products ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.

scholarly journals A Comparative Analysis of Machine Learning Models for Prediction of Insurance Uptake in Kenya

2020 ◽  
Author(s):  
Nelson Yego ◽  
Juma Kasozi ◽  
Joseph Nkrunziza
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Gradient Boosting ◽  
Support Vector ◽  
Sampled Data ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

The role of insurance in financial inclusion as well as in economic growth is immense. However, low uptake seems to impede the growth of the sector hence the need for a model that robustly predicts uptake of insurance among potential clients. In this research, we compared the performances of eight (8) machine learning models in predicting the uptake of insurance. The classifiers considered were Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, K Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting Machines and Extreme Gradient boosting. The data used in the classification was from the 2016 Kenya FinAccess Household Survey. Comparison of performance was done for both upsampled and downsampled data due to data imbalance. For upsampled data, Random Forest classifier showed highest accuracy and precision compared to other classifiers but for down sampled data, gradient boosting was optimal. It is noteworthy that for both upsampled and downsampled data, tree-based classifiers were more robust than others in insurance uptake prediction. However, in spite of hyper-parameter optimization, the area under receiver operating characteristic curve remained highest for Random Forest as compared to other tree-based models. Also, the confusion matrix for Random Forest showed least false positives, and highest true positives hence could be construed as the most robust model for predicting the insurance uptake. Finally, the most important feature in predicting uptake was having a bank product hence bancassurance could be said to be a plausible channel of distribution of insurance products.

scholarly journals Sign language dactyl recognition based on machine learning algorithms

2021 ◽  
Vol 4 (2(112)) ◽  
pp. 58-72
Author(s):  
Chingiz Kenshimov ◽  
Zholdas Buribayev ◽  
Yedilkhan Amirgaliyev ◽  
Aisulyu Ataniyazova ◽  
Askhat Aitimov
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Sign Language ◽  
Gesture Recognition ◽  
Research Work ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language

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