scholarly journals Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Ivan Laponogov ◽  
Guadalupe Gonzalez ◽  
Madelen Shepherd ◽  
Ahad Qureshi ◽  
Dennis Veselkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Disorders ◽  
Learning Algorithm ◽  
Biologically Active ◽  
Machine Learning Method ◽  
Nutrition Interventions ◽  
Drug Candidates ◽  
Protein Interactome ◽  
Varied Chemical ◽  
Approved Drugs

AbstractIn this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80–85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

scholarly journals Network machine learning maps phytochemically-rich “Hyperfoods” to fight COVID-19

2020 ◽  
Author(s):  
Ivan Laponogov ◽  
Guadalupe Gonzalez ◽  
Madelen Shepherd ◽  
Ahad Qureshi ◽  
Dennis Veselkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Disorders ◽  
Learning Algorithm ◽  
Biologically Active ◽  
Machine Learning Method ◽  
Nutrition Interventions ◽  
Drug Candidates ◽  
Protein Interactome ◽  
Varied Chemical ◽  
Approved Drugs

Abstract In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our experiments were run using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine-learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in-silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

scholarly journals Target identification of drug candidates with machine-learning algorithms: how choosing negative examples for training

2021 ◽  
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Target Identification ◽  
Learning Algorithms ◽  
Target Prediction ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Candidates ◽  
Approved Drugs

Abstract(1) Background:Identification of hit molecules protein targets is essential in the drug discovery process. Target prediction with machine-learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positive predicted targets, thus increasing time and cost of experimental validation campaigns. (2) Methods: To minimize the number of false positive predicted proteins, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for 3 particular drugs, and more globally for 200 approved drugs. (3) Results: For the detailed 3 drugs examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positive among the top ranked predicted targets decreased and overall the rank of the true targets was improved. (4) Conclusion: Our method enables to correct databases statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

scholarly journals Modeling Commuter’s Sociodemographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method

2019 ◽  
Vol 15 (2) ◽  
pp. 1-7
Author(s):  
Nabeel Shakeel ◽  
Farrukh Baig ◽  
Muhammad Abubakar Saddiq
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Survey Data ◽  
Public Transport ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Transport Services ◽  
Usage Frequency

Abstract Predictive modeling is the key fundamental method to study passengers’ behavior in transportation research. One of the limited studied topic is modeling of public transport usage frequency, which can be used to estimate present and future demand and users’ trend toward public transport services. The artificial intelligence and machine learning methods are promising to be better substitute to statistical techniques. No doubt, traditionally been used econometrics models are better for causal relationship studies among variables, but they made rigid assumptions and unable to recognize the pattern in data. This paper aims to build a predictive model to solve passengers’ classification, and public transport usage frequency using socio-demographic survey data. The supervised machine learning algorithm, K-Nearest Neighbor (KNN) applied to build a predictive model, which is the better machine learning method for dealing with small datasets, because of its ability of having less parameter tuning. Survey data has been used to train and validate the model performance, which is able to predict public transport usage frequency of future users of public transport. This model can practically be used by public transport agencies and relevant government organizations to predict the public transport demand for new commuters before introducing any new transportation projects.

scholarly journals Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Functional Outcome ◽  
Functional Outcomes ◽  
Learning Algorithm ◽  
Machine Learning Method ◽  
Ensemble Machine Learning ◽  
Predictive Algorithms ◽  
Ensemble Algorithm ◽  
Bagging Ensemble

AbstractGenetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia’ functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.

scholarly journals Early Prediction of Coronary Artery Disease (CAD) by Machine Learning Method - A Comparative Study

2021 ◽  
Vol 3 (1) ◽  
pp. 17-33
Author(s):  
Joy Iong Zong Chen ◽  
Hengjinda P
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Early Prediction ◽  
Machine Learning Method ◽  
Learning Method ◽  
Medical Field ◽  
Knowledge Based ◽  
Artery Disease

Coronary Artery Disease (CAD) prediction is a very hard and challenging task in the medical field. The early prediction in the medical field especially the cardiovascular sector is one of the virtuosi. The prior studies about the construction of the early prediction model developed an understanding of the recent techniques to find the variation in medical imaging. The prevention of cardiovascular can be fulfilled through a diet chart prepared by the concerned physician after early prediction. Our research paper consists of the prediction of CAD by the proposed algorithm by constructing of pooled area curve (PUC) in the machine learning method. This knowledge-based identification is an important factor for accurate prediction. This significant approach provides a good impact to determine variation in medical images although weak pixels surrounding it. This pooled area construction in our machine learning algorithm is bagging shrinking veins and tissues with the help of clogging and plaque of blood vessels. Besides, the noisy type database is used in this article for better clarity about identifying the classifier. This research article provides the recent adaptive image-based classification techniques and it comparing existing classification methods to predict CAD earlier for a higher accurate value. This proposed method is taking as evidence to diagnosis any heart disease earlier. The decision-making of classified output provides better accurate results in our proposed algorithm.

scholarly journals Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262131
Author(s):  
Adil Aslam Mir ◽  
Kimberlee Jane Kearfott ◽  
Fatih Vehbi Çelebi ◽  
Muhammad Rafique
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Series Data ◽  
Machine Learning Method ◽  
Learning Method ◽  
Imputation Methods ◽  
Squared Error ◽  
Feature Importance

A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing not at random (MNAR), and missing at random (MAR). IBFI utilizes the feature importance and iteratively imputes missing values using any base learning algorithm. For this work, IBFI is tested on soil radon gas concentration (SRGC) data. XGBoost is used as the learning algorithm and missing data are simulated using R for different missingness scenarios. IBFI is based on the physically meaningful assumption that SRGC depends upon environmental parameters such as temperature and relative humidity. This assumption leads to a model obtained from the complete multivariate series where the controls are available by taking the attribute of interest as a response variable. IBFI is tested against other frequently used imputation methods, namely mean, median, mode, predictive mean matching (PMM), and hot-deck procedures. The performance of the different imputation methods was assessed using root mean squared error (RMSE), mean squared log error (MSLE), mean absolute percentage error (MAPE), percent bias (PB), and mean squared error (MSE) statistics. The imputation process requires more attention when multiple variables are missing in different samples, resulting in challenges to machine learning methods because some controls are missing. IBFI appears to have an advantage in such circumstances. For testing IBFI, Radon Time Series Data (RTS) has been used and data was collected from 1st March 2017 to the 11th of May 2018, including 4 seismic activities that have taken place during the data collection time.

scholarly journals NP Animacy Identification for Anaphora Resolution

2007 ◽  
Vol 29 ◽  
pp. 79-103 ◽  
Author(s):  
C. Orasan ◽  
R. J. Evans
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Word Sense Disambiguation ◽  
Anaphora Resolution ◽  
Machine Learning Algorithm ◽  
Machine Learning Method ◽  
Word Sense ◽  
Rule Based ◽  
Sense Disambiguation ◽  
Integral Role

In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.

scholarly journals Research on target feature extraction and location positioning with machine learning algorithm

2020 ◽  
Vol 30 (1) ◽  
pp. 429-437
Author(s):  
Licheng Li
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Experimental Results ◽  
Machine Learning Algorithm ◽  
Machine Learning Method ◽  
Target Feature ◽  
Speeded Up Robust Features ◽  
Theoretical Support

Abstract The accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positioned the location using Back Propagation Neural Networks (BPNN) method, and tested the method through experiments. The experimental results showed that the feature extraction of SURF method was fast, about 0.2 s, and was less affected by noise. It was found from the positioning results that the output position of the BPNN method was basically consistent with the actual position, and errors in X, Y and Z directions were very small, which could meet the positioning needs of the robot. The experimental results verify the effectiveness of machine learning method and provide some theoretical support for its further promotion and application in practice.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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