A web-based automated machine learning platform to analyze liquid biopsy data

Lab on a Chip ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 2166-2174
Author(s):  
Hanfei Shen ◽  
Tony Liu ◽  
Jesse Cui ◽  
Piyush Borole ◽  
Ari Benjamin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liquid Biopsy ◽  
Learning Models ◽  
Learning Tool ◽  
Web Based ◽  
Learning Platform ◽  
Automated Machine Learning ◽  
Machine Learning Tool ◽  
Machine Learning Models

We have developed a web-based, self-improving and overfitting-resistant automated machine learning tool tailored specifically for liquid biopsy data, where machine learning models can be built without the user's input.

scholarly journals Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

APL Materials ◽  
2016 ◽  
Vol 4 (5) ◽  
pp. 053213 ◽  
Author(s):  
Michael W. Gaultois ◽  
Anton O. Oliynyk ◽  
Arthur Mar ◽  
Taylor D. Sparks ◽  
Gregory J. Mulholland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Thermoelectric Materials ◽  
Learning Models ◽  
Web Based ◽  
Materials Properties ◽  
Machine Learning Models

scholarly journals Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models

2020 ◽  
Vol 7 (05) ◽  
Author(s):  
Shuo Wang ◽  
Sihua Niu ◽  
Enze Qu ◽  
Flemming Forsberg ◽  
Annina Wilkes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Lesions ◽  
Learning Models ◽  
Automated Machine Learning ◽  
Machine Learning Models

scholarly journals A Universal Screening Tool for Dyslexia by a Web-Game and Machine Learning

2022 ◽  
Vol 3 ◽  
Author(s):  
Maria Rauschenberger ◽  
Ricardo Baeza-Yates ◽  
Luz Rello
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Universal Screening ◽  
User Study ◽  
Future Research ◽  
Learning Models ◽  
Early Screening ◽  
Web Based ◽  
Negative Side Effects ◽  
Machine Learning Models

Children with dyslexia have difficulties learning how to read and write. They are often diagnosed after they fail school even if dyslexia is not related to general intelligence. Early screening of dyslexia can prevent the negative side effects of late detection and enables early intervention. In this context, we present an approach for universal screening of dyslexia using machine learning models with data gathered from a web-based language-independent game. We designed the game content taking into consideration the analysis of mistakes of people with dyslexia in different languages and other parameters related to dyslexia like auditory perception as well as visual perception. We did a user study with 313 children (116 with dyslexia) and train predictive machine learning models with the collected data. Our method yields an accuracy of 0.74 for German and 0.69 for Spanish as well as a F1-score of 0.75 for German and 0.75 for Spanish, using Random Forests and Extra Trees, respectively. We also present the game content design, potential new auditory input, and knowledge about the design approach for future research to explore Universal screening of dyslexia. universal screening with language-independent content can be used for the screening of pre-readers who do not have any language skills, facilitating a potential early intervention.

MLsurvival: an automated machine learning tool for survival analysis of cancer patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13555-e13555
Author(s):  
Wei Zhou ◽  
Ji He
Keyword(s):  
Machine Learning ◽  
Survival Analysis ◽  
Cancer Patients ◽  
Medical Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Model ◽  
Learning Tool ◽  
Automated Machine Learning ◽  
Machine Learning Tool

e13555 Background: Survival analysis is used to establish a connection between covariates and the time of event with censored data. Compared with traditional statistical methods, machine learning approaches based on sophisticated and effective computational algorithms are more capable for handling complex multi-dimensional medical data. Methods: We developed an automated machine learning tool MLsurvival to analyze survival data of cancer patients, algorithms of which include the statistical cox regression and machine learning based on linear model (elastic net), ensemble model (gradient boosting with least squares or regression trees and random forest) and support vector kernel (linear and non-linear). The workflow of MLsurvival is comprised with four modules: preprocessing (missing data remove or imputation and feature standardization), feature selection (unsupervised multi-statistics and supervised machine recursive feature elimination with cross-validation), modeling (hyperparameter and performance evaluation) and prediction. To evaluate the performance of this tool, we analyzed medical data for 222 hepatocellular carcinoma (HCC) patients at stage II-III who underwent surgical resection and developed five machine learning approach based estimation models for overall survival (OS). Models were trained on 155 patients with 300 features, including clinical information, somatic mutation and copy number variation, and independently validated on the rest 67 patients. Results: The ensemble model of gradient boosting fitted by MLsurvival using 48 selected features for the data of 155 HCC patients possessed the highest mean AUC and C-Index value. For 67 patients in validation set, this model predicted half year mortality of patients with an AUC of 0.9 (95% CI, 0.771-1.029) and one year mortality with an AUC of 0.897 (95% CI, 0.816-0.978). In addition to that, this model was also predictive for the time of recurrence (pvalue < 0.0001). Furthermore, we also utilized this tool in survival analysis for extensive real data from patients with breast, lung, and esophagus cancers, while most of results showed superior accuracy and stable performance. Conclusions: MLsurvial is an automate tool for survival analysis of cancer patients with well performance. The risk scoring system implemented in this tool offers a novel strategy for incorporating multi-dimensional risk factors to predict clinical outcome, contributes to the better understanding of disease background and helps to optimize the clinical follow-up and therapeutic treatment for cancer patients.

scholarly journals AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data

SoftwareX ◽  
2022 ◽  
Vol 17 ◽  
pp. 100919
Author(s):  
Moncef Garouani ◽  
Adeel Ahmad ◽  
Mourad Bouneffa ◽  
Mohamed Hamlich
Keyword(s):  
Machine Learning ◽  
Learning Tool ◽  
Industrial Data ◽  
Automated Machine Learning ◽  
Machine Learning Tool
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