An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

Abstract Background . Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes. Results . The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: http://synapticgenes.bnd.edu.uy Conclusions . We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function. Availability : http://synapticgenes.bnd.edu.uy

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An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

10.21203/rs.2.13746/v3 ◽

2019 ◽

Author(s):

Flavio Pazos Obregón ◽

Martín Palazzo ◽

Pablo Soto ◽

Gustavo Guerberoff ◽

Patricio Yankilevich ◽

...

Keyword(s):

Machine Learning ◽

Empirical Data ◽

Scientific Community ◽

Synaptic Function ◽

Whole Body ◽

Training Set ◽

Protein Coding ◽

Training Scheme ◽

Transcription Profiles ◽

Ensemble Machine Learning

Abstract Background . Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes. Results . The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: http://synapticgenes.bnd.edu.uy Conclusions . We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function. Availability : http://synapticgenes.bnd.edu.uy

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An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

BMC Genomics ◽

10.1186/s12864-019-6380-z ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Flavio Pazos Obregón ◽

Martín Palazzo ◽

Pablo Soto ◽

Gustavo Guerberoff ◽

Patricio Yankilevich ◽

...

Keyword(s):

Machine Learning ◽

Empirical Data ◽

Scientific Community ◽

Synaptic Function ◽

Whole Body ◽

Training Set ◽

Link Type ◽

Training Scheme ◽

Transcription Profiles ◽

Ensemble Machine Learning

Abstract Background Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes. Results The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: http://synapticgenes.bnd.edu.uy Conclusions We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function. Availability http://synapticgenes.bnd.edu.uy

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An improved catalogue of putative synaptic genes defined by their temporal transcription profiles through an ensemble machine learning approach

10.21203/rs.2.9628/v1 ◽

2019 ◽

Author(s):

Flavio Pazos ◽

Pablo Soto ◽

Martín Palazzo ◽

Gustavo Guerberoff ◽

Patricio Yankilevich ◽

...

Keyword(s):

Machine Learning ◽

Empirical Data ◽

Predictive Power ◽

Learning Model ◽

Synaptic Function ◽

Training Set ◽

Training Scheme ◽

Transcription Profiles ◽

Ensemble Machine Learning ◽

Machine Learning Model

Abstract Background. Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes that was postulated to be very enriched in genes with still undocumented synaptic functions. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we used these new empirical data to evaluate the predictive power of the catalogue. Then we implemented a series of improvements to the training scheme and the ensemble rules of our model and added the new synaptic genes to the training set, to obtain a new, enhanced catalogue of putative synaptic genes. Results. The retrospective analysis demonstrated that our original catalogue was indeed highly enriched in genes with unknown synaptic function. The changes to the training scheme and the ensemble rules resulted in a catalogue with better predictive power. Finally, training this improved model with an updated training set, that includes all the new synaptic genes, we obtained a new, enhanced catalogue of putative synaptic genes, which we present here announcing a regularly updated version that will be available online at: http://synapticgenes.bnd.edu.uy Conclusions. We show that training a machine learning model solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data, we validated our original approach, improved our model an obtained a better catalogue. The utility of this approach is that it reduces the number of genes to be tested through hypothesis-driven experimentation.

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Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Information ◽

10.3390/info11060332 ◽

2020 ◽

Vol 11 (6) ◽

pp. 332

Author(s):

Ernest Kwame Ampomah ◽

Zhiguang Qin ◽

Gabriel Nyame

Keyword(s):

Machine Learning ◽

Stock Market ◽

Stock Price ◽

Superior Performance ◽

Operating Characteristics ◽

Training Set ◽

Data Set ◽

Test Set ◽

Ensemble Machine Learning ◽

Better Than

Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

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Prediction of Pipe Performance with Ensemble Machine Learning Based Approaches

2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) ◽

10.1109/sdpc.2017.84 ◽

2017 ◽

Author(s):

Fang Shi ◽

Zheng Liu ◽

Eric Li

Keyword(s):

Machine Learning ◽

Ensemble Machine Learning

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A Novel Ensemble Machine Learning Method to Detect Phishing Attack

2020 IEEE 23rd International Multitopic Conference (INMIC) ◽

10.1109/inmic50486.2020.9318210 ◽

2020 ◽

Author(s):

Abdul Basit ◽

Maham Zafar ◽

Abdul Rehman Javed ◽

Zunera Jalil

Keyword(s):

Machine Learning ◽

Machine Learning Method ◽

Learning Method ◽

Ensemble Machine Learning

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Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽

10.3390/en14041052 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1052

Author(s):

Baozhong Wang ◽

Jyotsna Sharma ◽

Jianhua Chen ◽

Patricia Persaud

Keyword(s):

Machine Learning ◽

Random Forest ◽

Reservoir Characterization ◽

Time Lapse ◽

Production Data ◽

Oil Saturation ◽

Ensemble Machine Learning ◽

Input Parameters ◽

Saturation Profiles ◽

Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

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