Machine learning models accurately interpret liver histology and are associated with disease progression in patients with primary sclerosing cholangitis

2020 ◽  
Vol 73 ◽  
pp. S485-S486
Author(s):  
Nate Travis ◽  
Vincent Billaut ◽  
Harsha Pokkalla ◽  
Kishalve Pethia ◽  
Oscar Zevallos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Primary Sclerosing Cholangitis ◽  
Sclerosing Cholangitis ◽  
Liver Histology ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Mechanistic models versus machine learning, a fight worth fighting for the biological community?

2018 ◽  
Vol 14 (5) ◽  
pp. 20170660 ◽  
Author(s):  
Ruth E. Baker ◽  
Jose-Maria Peña ◽  
Jayaratnam Jayamohan ◽  
Antoine Jérusalem
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Royal Society ◽  
Mechanistic Models ◽  
Learning Tools ◽  
Data Generation ◽  
Learning Models ◽  
Input Output ◽  
Correlation Studies ◽  
Machine Learning Models

Ninety per cent of the world's data have been generated in the last 5 years ( Machine learning: the power and promise of computers that learn by example . Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by the development of mechanistic models focused on the causality of input–output relationships. However, the vast majority is aimed at supporting statistical or correlation studies that bypass the need for causality and focus exclusively on prediction. Along these lines, there has been a vast increase in the use of machine learning models, in particular in the biomedical and clinical sciences, to try and keep pace with the rate of data generation. Recent successes now beg the question of whether mechanistic models are still relevant in this area. Said otherwise, why should we try to understand the mechanisms of disease progression when we can use machine learning tools to directly predict disease outcome?

scholarly journals Machine learning models to predict disease progression among veterans with hepatitis C virus

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0208141 ◽  
Author(s):  
Monica A. Konerman ◽  
Lauren A. Beste ◽  
Tony Van ◽  
Boang Liu ◽  
Xuefei Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Disease Progression ◽  
Learning Models ◽  
Machine Learning Models ◽  
Predict Disease Progression

FRI-016-Validation of the PREsTo machine learning algorithm for the prediction of disease progression in patients with primary sclerosing cholangitis

2019 ◽  
Vol 70 (1) ◽  
pp. e390-e391 ◽  
Author(s):  
John Eaton ◽  
Konstantinos Lazaridis ◽  
Pietro Invernizzi ◽  
Olivier Chazouillères ◽  
Gideon Hirschfield ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Primary Sclerosing Cholangitis ◽  
Sclerosing Cholangitis ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

Improving clinical trial efficiency with machine learning models of disease progression

2021 ◽  
pp. 333-366
Author(s):  
Danielle Beaulieu ◽  
Albert A. Taylor ◽  
Dustin Pierce ◽  
Jonavelle Cuerdo ◽  
Mark Schactman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Disease Progression ◽  
Learning Models ◽  
Models Of Disease ◽  
Trial Efficiency ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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