Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.

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Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene

Frontiers in Veterinary Science ◽

10.3389/fvets.2021.683134 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jeonghoon Kim ◽

Kyuyoung Lee ◽

Ruwini Rupasinghe ◽

Shahbaz Rezaei ◽

Beatriz Martínez-López ◽

...

Keyword(s):

Machine Learning ◽

Phylogenetic Analysis ◽

Amino Acid ◽

Random Forest ◽

Real Time ◽

Classification Model ◽

Support Vector ◽

Operating Characteristics ◽

Orf5 Gene

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.

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Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽

10.3390/en14071809 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1809

Author(s):

Mohammed El Amine Senoussaoui ◽

Mostefa Brahami ◽

Issouf Fofana

Keyword(s):

Machine Learning ◽

Random Forest ◽

Oil Quality ◽

Principal Component ◽

Condition Assessment ◽

Classification Performance ◽

Transformer Oil ◽

Classification Model ◽

Insulation Degradation ◽

Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

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Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽

10.3390/atmos12010109 ◽

2021 ◽

Vol 12 (1) ◽

pp. 109

Author(s):

Ashima Malik ◽

Megha Rajam Rao ◽

Nandini Puppala ◽

Prathusha Koouri ◽

Venkata Anil Kumar Thota ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Curves ◽

Data Driven ◽

Northern California ◽

Combined Model ◽

Wildfire Risk ◽

Study Results ◽

Forest Models ◽

Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

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A Novel Machine Learning Sepsis Prediction Algorithm for Intended ICU Use (NAVOY Sepsis): A Proof-of-Concept Study (Preprint)

10.2196/preprints.28000 ◽

2021 ◽

Author(s):

Inger Persson ◽

Andreas Östling ◽

Martin Arlbrandt ◽

Joakim Söderberg ◽

David Becedas

Keyword(s):

Machine Learning ◽

High Performance ◽

Learning Algorithm ◽

Scoring Systems ◽

High Accuracy ◽

Prediction Algorithm ◽

Massachusetts Institute Of Technology ◽

Operating Characteristics ◽

Mortality And Morbidity ◽

Institute Of Technology

BACKGROUND Despite decades of research, sepsis remains a leading cause of mortality and morbidity in ICUs worldwide. The key to effective management and patient outcome is early detection, where no prospectively validated machine learning prediction algorithm is available for clinical use in Europe today. OBJECTIVE To develop a high-performance machine learning sepsis prediction algorithm based on routinely collected ICU data, designed to be implemented in Europe. METHODS The machine learning algorithm is developed using Convolutional Neural Network, based on the Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III Clinical Database, focusing on ICU patients aged 18 years or older. Twenty variables are used for prediction, on an hourly basis. Onset of sepsis is defined in accordance with the international Sepsis-3 criteria. RESULTS The developed algorithm NAVOY Sepsis uses 4 hours of input and can with high accuracy predict patients with high risk of developing sepsis in the coming hours. The prediction performance is superior to that of existing sepsis early warning scoring systems, and competes well with previously published prediction algorithms designed to predict sepsis onset in accordance with the Sepsis-3 criteria, as measured by the area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPRC). NAVOY Sepsis yields AUROC = 0.90 and AUPRC = 0.62 for predictions up to 3 hours before sepsis onset. The predictive performance is externally validated on hold-out test data, where NAVOY Sepsis is confirmed to predict sepsis with high accuracy. CONCLUSIONS An algorithm with excellent predictive properties has been developed, based on variables routinely collected at ICUs. This algorithm is to be further validated in an ongoing prospective randomized clinical trial and will be CE marked as Software as a Medical Device, designed for commercial use in European ICUs.

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For Honor, for Toxicity

Proceedings of the ACM on Human-Computer Interaction ◽

10.1145/3474680 ◽

2021 ◽

Vol 5 (CHI PLAY) ◽

pp. 1-29

Author(s):

Alessandro Canossa ◽

Dmitry Salimov ◽

Ahmad Azadvar ◽

Casper Harteveld ◽

Georgios Yannakakis

Keyword(s):

Machine Learning ◽

Random Forest ◽

Random Forests ◽

Initial Study ◽

Unfair Advantage ◽

Offensive Behavior ◽

Forest Models ◽

Random Forest Models ◽

Action Type ◽

Degree Of Severity

Is it possible to detect toxicity in games just by observing in-game behavior? If so, what are the behavioral factors that will help machine learning to discover the unknown relationship between gameplay and toxic behavior? In this initial study, we examine whether it is possible to predict toxicity in the MOBA gameFor Honor by observing in-game behavior for players that have been labeled as toxic (i.e. players that have been sanctioned by Ubisoft community managers). We test our hypothesis of detecting toxicity through gameplay with a dataset of almost 1,800 sanctioned players, and comparing these sanctioned players with unsanctioned players. Sanctioned players are defined by their toxic action type (offensive behavior vs. unfair advantage) and degree of severity (warned vs. banned). Our findings, based on supervised learning with random forests, suggest that it is not only possible to behaviorally distinguish sanctioned from unsanctioned players based on selected features of gameplay; it is also possible to predict both the sanction severity (warned vs. banned) and the sanction type (offensive behavior vs. unfair advantage). In particular, all random forest models predict toxicity, its severity, and type, with an accuracy of at least 82%, on average, on unseen players. This research shows that observing in-game behavior can support the work of community managers in moderating and possibly containing the burden of toxic behavior.

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Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

10.1115/gt2021-58916 ◽

2021 ◽

Author(s):

Enzo Losi ◽

Mauro Venturini ◽

Lucrezia Manservigi ◽

Giuseppe Fabio Ceschini ◽

Giovanni Bechini ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Gas Turbine ◽

Gas Turbines ◽

Remaining Useful Life ◽

Training Data ◽

The Novel ◽

Novel Approach ◽

Forest Models ◽

Random Forest Models

Abstract A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability. In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, Random Forests are an ensemble learning method consisting of an aggregation of decision tree classifiers. This paper presents a novel methodology aimed at exploiting information embedded in the data and develops Random Forest models, aimed at predicting gas turbine trip based on information gathered during a timeframe of historical data acquired from multiple sensors. The novel approach exploits time series segmentation to increase the amount of training data, thus reducing overfitting. First, data are transformed according to a feature engineering methodology developed in a separate work by the same authors. Then, Random Forest models are trained and tested on unseen observations to demonstrate the benefits of the novel approach. The superiority of the novel approach is proved by considering two real-word case-studies, involving filed data taken during three years of operation of two fleets of Siemens gas turbines located in different regions. The novel methodology allows values of Precision, Recall and Accuracy in the range 75–85 %, thus demonstrating the industrial feasibility of the predictive methodology.

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Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

Frontiers in Neurology ◽

10.3389/fneur.2020.548305 ◽

2020 ◽

Vol 11 ◽

Author(s):

Yi Guo ◽

Yushan Liu ◽

Wenjie Ming ◽

Zhongjin Wang ◽

Junming Zhu ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Focal Cortical Dysplasia ◽

Cortical Dysplasia ◽

Machine Learning Algorithms ◽

Classification Model ◽

Supervised Machine Learning ◽

Seizure Onset ◽

Glioneuronal Tumors ◽

Patients With Epilepsy

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

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CPT Data Interpretation Employing Different Machine Learning Techniques

Geosciences ◽

10.3390/geosciences11070265 ◽

2021 ◽

Vol 11 (7) ◽

pp. 265

Author(s):

Stefan Rauter ◽

Franz Tschuchnigg

Keyword(s):

Machine Learning ◽

Grain Size ◽

Random Forest ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Models ◽

Cone Penetration ◽

Tip Resistance ◽

Machine Learning Models

The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. Since Machine Learning could play a key role in reducing the costs and time needed for a suitable site investigation program, the basic ability of Machine Learning models to classify soils from Cone Penetration Tests (CPT) is evaluated. To find an appropriate classification model, 24 different Machine Learning models, based on three different algorithms, are built and trained on a dataset consisting of 1339 CPT. The applied algorithms are a Support Vector Machine, an Artificial Neural Network and a Random Forest. As input features, different combinations of direct cone penetration test data (tip resistance qc, sleeve friction fs, friction ratio Rf, depth d), combined with “defined”, thus, not directly measured data (total vertical stresses σv, effective vertical stresses σ’v and hydrostatic pore pressure u0), are used. Standard soil classes based on grain size distributions and soil classes based on soil behavior types according to Robertson are applied as targets. The different models are compared with respect to their prediction performance and the required learning time. The best results for all targets were obtained with models using a Random Forest classifier. For the soil classes based on grain size distribution, an accuracy of about 75%, and for soil classes according to Robertson, an accuracy of about 97–99%, was reached.

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Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

10.1101/2020.11.02.350439 ◽

2020 ◽

Author(s):

Liam Brierley ◽

Anna Fowler

Keyword(s):

Machine Learning ◽

Random Forest ◽

Machine Learning Algorithms ◽

Spike Protein ◽

Animal Origin ◽

Whole Genome ◽

Genome Sequences ◽

Genome Composition ◽

Forest Models ◽

Random Forest Models

AbstractThe COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 225 and 187 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ∼73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

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A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

Environmental Health ◽

10.1186/s12940-021-00825-9 ◽

2022 ◽

Vol 21 (1) ◽

Author(s):

Luca Boniardi ◽

Federica Nobile ◽

Massimo Stafoggia ◽

Paola Michelozzi ◽

Carla Ancona

Keyword(s):

Machine Learning ◽

Land Use ◽

Air Pollution ◽

Random Forest ◽

Citizen Science ◽

Learning Approach ◽

Machine Learning Approach ◽

Forest Models ◽

Random Forest Models ◽

The Impact

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

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