scholarly journals Reviewing artificial intelligence methods for predictive modeling applications

2021 ◽  
Author(s):  
Andreas Sepp
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Modeling ◽  
Significant Contribution ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Intelligence Methods

Artificial intelligence and machine learning methods had significant contribution to the advancement and progress of predictive analytics. This article presents a state of the art of methods and applications of artificial intelligence and machine learning.

Data integration and predictive modeling methods for multi-omics datasets

2018 ◽  
Vol 14 (1) ◽  
pp. 8-25 ◽  
Author(s):  
Minseung Kim ◽  
Ilias Tagkopoulos
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Predictive Modeling ◽  
Predictive Analytics ◽  
Learning Methods ◽  
Modeling Methods ◽  
Machine Learning Methods

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

scholarly journals State of the art of predictive modeling for real-life applications

2021 ◽  
Author(s):  
Andreas Sepp
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Modeling ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Real Life ◽  
Modeling Techniques

Predictive modeling techniques had recently witnessed significant improvement due the advances in artificial intelligence and machine learning. This research presents a survey on the methods and applications of artificial intelligence and machine learning used in predictive analytics.

Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

2020 ◽  
Author(s):  
Minsik Oh ◽  
Sungjoon Park ◽  
Sun Kim ◽  
Heejoon Chae
Keyword(s):  
Machine Learning ◽  
Gene Regulation ◽  
State Of The Art ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Gene Expressions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Disease Subtype

Abstract Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

scholarly journals Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0239424
Author(s):  
Peter M. Yaworsky ◽  
Kenneth B. Vernon ◽  
Jerry D. Spangler ◽  
Simon C. Brewer ◽  
Brian F. Codding
Keyword(s):  
Machine Learning ◽  
Predictive Modeling ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
National Monument

A Review on the Importance of Machine Learning and Artificial Intelligence in Real Life Problem Solving

2020 ◽  
Vol 17 (9) ◽  
pp. 4336-4339
Author(s):  
D. S. V. Suma Priya ◽  
D. Esther Rani ◽  
A. Pavan Shankar Sai ◽  
A. Konda Babu ◽  
Durgesh Nandan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Problem Solving ◽  
Concept Learning ◽  
Real Life ◽  
Learning System ◽  
Level System ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Real Life Problem

This paper clearly explains the concept, importance and main aim of machine learning and construction of the machine learning system. There are several ideas regarding this machine learning which are formed by a number of strategies. This effort leads to introduce many machine learning methods such as learning by commands, concept, learning by comparison, and learning by some algorithms. This article provides information about the main purpose of machine learning and its development. Machine learning is the primary aspect that promotes any system to have intelligence. One of its main applications is artificial intelligence. Machine learning is highly suited for complex level system representation. There are a number of machine learning concepts that leads to the integration of number of networks.

Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods

2019 ◽  
Vol 212 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Guy S. Handelman ◽  
Hong Kuan Kok ◽  
Ronil V. Chandra ◽  
Amir H. Razavi ◽  
Shiwei Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Evaluation Metrics ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees

2015 ◽  
Vol 11 (4) ◽  
pp. e1004185 ◽  
Author(s):  
Ickwon Choi ◽  
Amy W. Chung ◽  
Todd J. Suscovich ◽  
Supachai Rerks-Ngarm ◽  
Punnee Pitisuttithum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Modeling ◽  
Learning Methods ◽  
Machine Learning Methods
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