scholarly journals Integration of Cross Species RNA-seq Meta-Analysis and Machine-Learning Models Identifies the Most Important Salt Stress–Responsive Pathways in Microalga Dunaliella

2019 ◽  
Vol 10 ◽  
Author(s):  
Bahman Panahi ◽  
Mohammad Frahadian ◽  
Jacob T. Dums ◽  
Mohammad Amin Hejazi
Keyword(s):  
Machine Learning ◽  
Salt Stress ◽  
Meta Analysis ◽  
Rna Seq ◽  
Learning Models ◽  
Machine Learning Models

Prediction of the prognosis of oesophageal cancer: Systematic review & meta-analysis of machine learning solutions- Study protocol

2021 ◽  
Author(s):  
Ali Haider Bangash
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Cancer Patients ◽  
Oesophageal Cancer ◽  
Study Protocol ◽  
Meta Analysis ◽  
Learning Models ◽  
Collaborative Project ◽  
International Collaborative ◽  
Machine Learning Models

In an international collaborative project, we shall be exploring the features of machine learning models that predict the outcome & prognosis of oesophageal cancer patients.

scholarly journals A survey of surveys on the use of visualization for interpreting machine learning models

2020 ◽  
Vol 19 (3) ◽  
pp. 207-233 ◽  
Author(s):  
Angelos Chatzimparmpas ◽  
Rafael M. Martins ◽  
Ilir Jusufi ◽  
Andreas Kerren
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Original Research ◽  
Learning Models ◽  
Visual Interpretation ◽  
Research Papers ◽  
Black Boxes ◽  
Internal Operations ◽  
Definition Of ◽  
Machine Learning Models

Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a meta-analysis (i.e. a “survey of surveys”) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.

scholarly journals Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies

2021 ◽  
Vol 7 ◽  
pp. 205520762110473
Author(s):  
Kushan De Silva ◽  
Joanne Enticott ◽  
Christopher Barton ◽  
Andrew Forbes ◽  
Sajal Saha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Type 2 Diabetes ◽  
Predictive Modeling ◽  
Prediction Models ◽  
Community Care ◽  
Meta Analysis ◽  
Learning Models ◽  
Machine Learning Models

Objective Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these prediction models of type 2 diabetes is available. We aim to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance. Methods The systematic review of English language machine learning predictive modeling studies in 12 databases will be conducted. Studies predicting type 2 diabetes in predefined clinical or community settings are eligible. Standard CHARMS and TRIPOD guidelines will guide data extraction. Methodological quality will be assessed using a predefined risk of bias assessment tool. The extent of validation will be categorized by Reilly–Evans levels. Primary outcomes include model performance metrics of discrimination ability, calibration, and classification accuracy. Secondary outcomes include candidate predictors, algorithms used, level of validation, and intended use of models. The random-effects meta-analysis of c-indices will be performed to evaluate discrimination abilities. The c-indices will be pooled per prediction model, per model type, and per algorithm. Publication bias will be assessed through funnel plots and regression tests. Sensitivity analysis will be conducted to estimate the effects of study quality and missing data on primary outcome. The sources of heterogeneity will be assessed through meta-regression. Subgroup analyses will be performed for primary outcomes. Ethics and dissemination No ethics approval is required, as no primary or personal data are collected. Findings will be disseminated through scientific sessions and peer-reviewed journals. PROSPERO registration number CRD42019130886

scholarly journals Machine Learning Models for COVID-19 Confirmed Cases Prediction: A Meta-Analysis Approach

2021 ◽  
Vol 2084 (1) ◽  
pp. 012013
Author(s):  
Wan Fairos Wan Yaacob ◽  
Norafefah Mohamad Sobri ◽  
Syerina Azlin Md Nasir ◽  
Noor Ilanie Nordin ◽  
Wan Faizah Wan Yaacob ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Confidence Interval ◽  
Meta Analysis ◽  
Random Effect Model ◽  
Good Prediction ◽  
Learning Models ◽  
Chi Square ◽  
Heterogeneity Index ◽  
Machine Learning Models

Abstract COVID-19, CoronaVirus Disease – 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg’s test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases.

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