Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against Plasmodium falciparum in vitro and in vivo and that have been suggested to act through the inhibition of PfATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na+ concentration. The structure of PfATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-PfATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.

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Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods

Sensors ◽

10.3390/s20082344 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2344 ◽

Cited By ~ 7

Author(s):

Federico Pittino ◽

Michael Puggl ◽

Thomas Moldaschl ◽

Christina Hirschl

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Statistical Learning ◽

Control Charts ◽

Industry 4.0 ◽

Production Environment ◽

Statistical Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

And Control

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.

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The Prediction-Explanation Fallacy: A Pervasive Problem in Scientific Applications of Machine Learning

10.31234/osf.io/4vq8f ◽

2021 ◽

Author(s):

Marco Del Giudice

Keyword(s):

Machine Learning ◽

Predictive Models ◽

Complex Structure ◽

Multiple Models ◽

Similar Data ◽

Scientific Applications ◽

Learning Methods ◽

Machine Learning Methods ◽

Mitigating Factors ◽

Applications Of Machine Learning

In this paper, I highlight a problem that has become ubiquitous in scientific applications of machine learning methods, and can lead to seriously distorted inferences about the phenomena under study. I call it the prediction-explanation fallacy. The fallacy occurs when researchers use prediction-optimized models for explanatory purposes, without considering the tradeoffs between explanation and prediction. This is a problem for at least two reasons. First, prediction-optimized models are often deliberately biased and unrealistic in order to prevent overfitting, and hence fail to accurately explain the phenomenon of interest. In other cases, they have an exceedingly complex structure that is hard or impossible to interpret, which greatly limits their explanatory value. Second, different predictive models trained on the same or similar data can be biased in different ways, so that multiple models may predict equally well but suggest conflicting explanations of the underlying phenomenon. In this note I introduce the tradeoffs between prediction and explanation in a non-technical fashion, present some illustrative examples from neuroscience, and end by discussing some mitigating factors and methods that can be used to limit or circumvent the problem.

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Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

Frontiers in Oncology ◽

10.3389/fonc.2021.606677 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xuejiao Han ◽

Jing Yang ◽

Jingwen Luo ◽

Pengan Chen ◽

Zilong Zhang ◽

...

Keyword(s):

Machine Learning ◽

Predictive Models ◽

Pancreatic Neuroendocrine Tumors ◽

Selection Methods ◽

Learning Methods ◽

Machine Learning Methods ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Testing Group

ObjectivesThe purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods.MethodsIn this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group.ResultsThe predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group.ConclusionsRadiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.

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Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-190262 ◽

2019 ◽

Vol 71 (3) ◽

pp. 1027-1036 ◽

Cited By ~ 4

Author(s):

Ali Ezzati ◽

Andrea R. Zammit ◽

Danielle J. Harvey ◽

Christian Habeck ◽

Charles B. Hall ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Predictive Models ◽

Learning Methods ◽

Machine Learning Methods

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An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

10.26434/chemrxiv.13194755.v1 ◽

2020 ◽

Author(s):

Edwin Tse ◽

Laksh Aithani ◽

Mark Anderson ◽

Jonathan Cardoso-Silva ◽

Giovanni Cincilla ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Predictive Models ◽

Structural Information ◽

Open Data ◽

Ion Pump ◽

Learning Methods ◽

Machine Learning Methods

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against Plasmodium falciparum in vitro and in vivo and that have been suggested to act through the inhibition of PfATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na+ concentration. The structure of PfATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-PfATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.

Download Full-text