scholarly journals Meta-Signer: Metagenomic Signature Identifier based onrank aggregation of features

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 194
Author(s):  
Derek Reiman ◽  
Ahmed Metwally ◽  
Jun Sun ◽  
Yang Dai
Keyword(s):  
Machine Learning ◽  
Optimization Procedure ◽  
Predictive Performance ◽  
Rank Aggregation ◽  
Support Vector ◽  
Learning Models ◽  
Vector Machines ◽  
Single List ◽  
User Friendly ◽  
Machine Learning Models

The advance of metagenomic studies provides the opportunity to identify microbial taxa that are associated with human diseases. Multiple methods exist for the association analysis. However, the results could be inconsistent, presenting challenges in interpreting the host-microbiome interactions. To address this issue, we develop Meta-Signer, a novel Metagenomic Signature Identifier tool based on rank aggregation of features identified from multiple machine learning models including Random Forest, Support Vector Machines, Logistic Regression, and Multi-Layer Perceptron Neural Networks. Meta-Signer generates ranked taxa lists by training individual machine learning models over multiple training partitions and aggregates the ranked lists into a single list by an optimization procedure to represent the most informative and robust microbial features. A User will receive speedy assessment on the predictive performance of each ma-chine learning model using different numbers of the ranked features and determine the final models to be used for evaluation on external datasets. Meta-Signer is user-friendly and customizable, allowing users to explore their datasets quickly and efficiently.

scholarly journals Meta-Signer: Metagenomic Signature Identifier based on Rank Aggregation of Features

2020 ◽  
Author(s):  
Derek Reiman ◽  
Ahmed A. Metwally ◽  
Jun Sun ◽  
Yang Dai
Keyword(s):  
Machine Learning ◽  
Optimization Procedure ◽  
Rank Aggregation ◽  
Support Vector ◽  
Learning Models ◽  
Ranking Methods ◽  
Vector Machines ◽  
User Friendly ◽  
Novel Model ◽  
Machine Learning Models

AbstractBackgroundThe advance of metagenomic studies provides the opportunity to identify microbial taxa that are associated to human diseases. Multiple methods exist for the association analysis. However, the results could be inconsistent, presenting challenges in interpreting the host-microbiome interactions. To address this issue, we introduce Meta-Signer, a novel Metagenomic Signature Identifier tool based on rank aggregation of features identified from multiple machine learning models including Random Forest, Support Vector Machines, LASSO, Multi-Layer Perceptron Neural Networks, and our recently developed Convolutional Neural Network framework (PopPhy-CNN). Meta-Signer generates ranked taxa lists by training individual machine learning models over multiple training partitions and aggregates them into a single ranked list by an optimization procedure to represent the most informative and robust microbial features. Meta-Signer can rank taxa using two input forms of the data: the relative abundances of the original taxa and taxa from the populated taxonomic trees generated from the original taxa. The latter form allows the evaluation of the association of microbial features at different taxonomic levels to the disease, which is attributed to our novel model of PopPhy-CNN.ResultsWe evaluate Mega-Signer on five different human gut-microbiome datasets. We demonstrate that the features derived from Meta-Signer were more informative compared to those obtained from other available feature ranking methods. The highly ranked features are strongly supported by published literature.ConclusionMeta-Signer is capable of deriving a robust set of microbial features at multiple taxonomic levels for the prediction of host phenotype. Meta-Signer is user-friendly and customizable, allowing users to explore their datasets quickly and efficiently.

scholarly journals Learning to Identify At-Risk Students in Distance Education Using Interaction Counts

2016 ◽  
Vol 23 (2) ◽  
pp. 124 ◽  
Author(s):  
Douglas Detoni ◽  
Cristian Cechinel ◽  
Ricardo Araujo Matsumura ◽  
Daniela Francisco Brauner
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
At Risk Students ◽  
Drop Out ◽  
Support Vector ◽  
Learning Models ◽  
Data Set ◽  
Student Dropout ◽  
Vector Machines ◽  
Machine Learning Models

Student dropout is one of the main problems faced by distance learning courses. One of the major challenges for researchers is to develop methods to predict the behavior of students so that teachers and tutors are able to identify at-risk students as early as possible and provide assistance before they drop out or fail in their courses. Machine Learning models have been used to predict or classify students in these settings. However, while these models have shown promising results in several settings, they usually attain these results using attributes that are not immediately transferable to other courses or platforms. In this paper, we provide a methodology to classify students using only interaction counts from each student. We evaluate this methodology on a data set from two majors based on the Moodle platform. We run experiments consisting of training and evaluating three machine learning models (Support Vector Machines, Naive Bayes and Adaboost decision trees) under different scenarios. We provide evidences that patterns from interaction counts can provide useful information for classifying at-risk students. This classification allows the customization of the activities presented to at-risk students (automatically or through tutors) as an attempt to avoid students drop out.

scholarly journals Daily Cryptocurrency Returns Forecasting and Trading via Machine Learning

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Andrew Falcon ◽  
Tianshu Lyu
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Learning Models ◽  
Investor Attention ◽  
Vector Machines ◽  
Price Trends ◽  
Sharpe Ratios ◽  
Returns Forecasting ◽  
Machine Learning Models ◽  
Significant Factors

We execute a comparative analysis of machine learning models for the time-series forecasting of the sign of next-day cryptocurrency returns. We begin by compiling a proprietary dataset that encompasses a wide array of potential cryptocurrency valuation factors (price trends, liquidity, volatility, network, production, investor attention), subsequently identifying and evaluating the most significant factors. We apply eight machine learning models to the dataset, utilizing them as classifiers to predict the sign of next day price returns for the three largest cryptocurrencies by market capitalization: bitcoin, ethereum, and ripple. We show that the most significant valuation factors for cryptocurrency returns are price trend variables, seven and thirty-day reversal, to be specific. We conclude that support vector machines result in the most accurate classifications for all three cryptocurrencies. Additionally, we find that boosted models like AdaBoost and XGBoost have the poorest classification accuracy. At length, we construct a probability-based trading strategy that secures either a daily long or short position on one of the three examined cryptocurrencies. Ultimately, the strategy yields a Sharpe of 2.8 and a cumulative log return of 3.72. On average, the strategy’s log returns outperformed standalone investments in all three cryptocurrencies by a factor of 5.64, and Sharpe ratios more than threefold.

scholarly journals Empirical asset pricing via machine learning: evidence from the European stock market

2021 ◽  
Author(s):  
Wolfgang Drobetz ◽  
Tizian Otto
Keyword(s):  
Machine Learning ◽  
Stock Returns ◽  
Network Architecture ◽  
Risk Measures ◽  
Predictive Performance ◽  
Support Vector ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Models

AbstractThis paper evaluates the predictive performance of machine learning methods in forecasting European stock returns. Compared to a linear benchmark model, interactions and nonlinear effects help improve the predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality problem and to avoid overfitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit substantial variation in statistical predictive performance that translate into pronounced differences in economic profitability. The return and risk measures of long-only trading strategies indicate that machine learning models produce sizeable gains relative to our benchmark. Neural networks perform best, also after accounting for transaction costs. A classification-based portfolio formation, utilizing a support vector machine that avoids estimating stock-level expected returns, performs even better than the neural network architecture.

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C

2021 ◽  
pp. 000456322110468
Author(s):  
Anudeep P P ◽  
Suchitra Kumari ◽  
Aishvarya S Rajasimman ◽  
Saurav Nayak ◽  
Pooja Priyadarsini
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Random Forests ◽  
Predictive Performance ◽  
Support Vector ◽  
Learning Models ◽  
Complex Interactions ◽  
Clinical Biochemistry Laboratory ◽  
Study Laboratory ◽  
Machine Learning Models

Background LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models—random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population. Methods The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019–2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas. Results LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used. Conclusion Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.

scholarly journals A Method to Extract Feature Variables Contributed in Nonlinear Machine Learning Prediction

2020 ◽  
Vol 59 (01) ◽  
pp. 001-008
Author(s):  
Mayumi Suzuki ◽  
Takuma Shibahara ◽  
Yoshihiro Muragaki
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Deep Neural Networks ◽  
Extraction Technique ◽  
Support Vector ◽  
Learning Models ◽  
Analysis Technique ◽  
Vector Machines ◽  
Backward Analysis ◽  
Machine Learning Models

Abstract Background Although advances in prediction accuracy have been made with new machine learning methods, such as support vector machines and deep neural networks, these methods make nonlinear machine learning models and thus lack the ability to explain the basis of their predictions. Improving their explanatory capabilities would increase the reliability of their predictions. Objective Our objective was to develop a factor analysis technique that enables the presentation of the feature variables used in making predictions, even in nonlinear machine learning models. Methods A factor analysis technique was consisted of two techniques: backward analysis technique and factor extraction technique. We developed a factor extraction technique extracted feature variables that was obtained from the posterior probability distribution of a machine learning model which was calculated by backward analysis technique. Results In evaluation, using gene expression data from prostate tumor patients and healthy subjects, the prediction accuracy of a model of deep neural networks was approximately 5% better than that of a model of support vector machines. Then the rate of concordance between the feature variables extracted in an earlier report using Jensen–Shannon divergence and the ones extracted in this report using backward elimination using Hilbert–Schmidt independence criteria was 40% for the top five variables, 40% for the top 10, and 49% for the top 100. Conclusion The results showed that models can be evaluated from different viewpoints by using different factor extraction techniques. In the future, we hope to use this technique to verify the characteristics of features extracted by factor extraction technique, and to perform clinical studies using the genes, we extracted in this experiment.

scholarly journals Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly

2020 ◽  
Vol 45 (2) ◽  
pp. 66-77
Author(s):  
German Cuaya-Simbro ◽  
Alberto-Isaac Perez-Sanpablo ◽  
Angélica Muñoz-Meléndez ◽  
Ivett Quiñones Uriostegui ◽  
Eduardo-F. Morales-Manzanares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fall Risk ◽  
Dynamic Bayesian Networks ◽  
Community Dwelling ◽  
Support Vector ◽  
Learning Models ◽  
Risk Of Falling ◽  
Gait Parameters ◽  
Vector Machines ◽  
Machine Learning Models

AbstractFalls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are more vulnerable to falls. The focus of this study is to investigate the performance of the different machine learning models built on spatiotemporal gait parameters to predict falls particularly in subjects with osteoporosis. Spatiotemporal gait parameters and prospective registration of falls were obtained from a sample of 110 community dwelling older women with osteoporosis (age 74.3 ± 6.3) and 143 without osteoporosis (age 68.7 ± 6.8). We built four different models, Support Vector Machines, Neuronal Networks, Decision Trees, and Dynamic Bayesian Networks (DBN), for each specific set of parameters used, and compared them considering their accuracy, precision, recall and F-score to predict fall risk. The F-score value shows that DBN based models are more efficient to predict fall risk, and the best result obtained is when we use a DBN model using the experts’ variables with FSMC’s variables, mixed variables set, obtaining an accuracy of 80%, and recall of 73%. The results confirm the feasibility of computational methods to complement experts’ knowledge to predict risk of falling within a period of time as high as 12 months.

scholarly journals Comparing machine learning and ensemble learning in the field of football

2019 ◽  
Vol 9 (5) ◽  
pp. 4321
Author(s):  
Shuaib Khan ◽  
Kirubanand V. B
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Learning Model ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Model ◽  
Vector Machines ◽  
The Right ◽  
Enormous Number ◽  
Machine Learning Models

Football has been one of the most popular and loved sports since its birth on November 6th, 1869. The main reason for this is because it is highly unpredictable in nature. Predicting football matches results seems like the perfect problem for machine learning models. But there are various caveats such as picking the right features from an enormous number of available features.  There have been many models which have been applied to various football-related datasets. This paper aims to compare Support Vector Machines a machine learning model and XGBoost an Ensemble learning model and how Ensemble Learning can greatly improve the accuracy of the predictions.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

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