scholarly journals Editorial: Application of Novel Statistical and Machine-Learning Methods to High-Dimensional Clinical Cancer and (Multi-)Omics Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Chao Xu ◽  
Shaolong Cao ◽  
Md. Ashad Alam
Keyword(s):  
Machine Learning ◽  
High Dimensional ◽  
Omics Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Cancer

scholarly journals A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 82-96 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Sanja Pfeifer ◽  
Nataša Šarlija
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Classification Problem ◽  
High Dimensional ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dimensional Classification ◽  
Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

scholarly journals TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification

2019 ◽  
Vol 35 (14) ◽  
pp. i31-i40 ◽  
Author(s):  
Erfan Sayyari ◽  
Ban Kawas ◽  
Siavash Mirarab
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Data Augmentation ◽  
Training Data ◽  
Supplementary Information ◽  
High Dimensional ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Phenotype Classification ◽  
Microbiome Data

Abstract Motivation Learning associations of traits with the microbial composition of a set of samples is a fundamental goal in microbiome studies. Recently, machine learning methods have been explored for this goal, with some promise. However, in comparison to other fields, microbiome data are high-dimensional and not abundant; leading to a high-dimensional low-sample-size under-determined system. Moreover, microbiome data are often unbalanced and biased. Given such training data, machine learning methods often fail to perform a classification task with sufficient accuracy. Lack of signal is especially problematic when classes are represented in an unbalanced way in the training data; with some classes under-represented. The presence of inter-correlations among subsets of observations further compounds these issues. As a result, machine learning methods have had only limited success in predicting many traits from microbiome. Data augmentation consists of building synthetic samples and adding them to the training data and is a technique that has proved helpful for many machine learning tasks. Results In this paper, we propose a new data augmentation technique for classifying phenotypes based on the microbiome. Our algorithm, called TADA, uses available data and a statistical generative model to create new samples augmenting existing ones, addressing issues of low-sample-size. In generating new samples, TADA takes into account phylogenetic relationships between microbial species. On two real datasets, we show that adding these synthetic samples to the training set improves the accuracy of downstream classification, especially when the training data have an unbalanced representation of classes. Availability and implementation TADA is available at https://github.com/tada-alg/TADA. Supplementary information Supplementary data are available at Bioinformatics online.

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 Sparse Boosting Based Machine Learning Methods for High-Dimensional Data

2021 ◽  
Author(s):  
Mu Yue
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Variable Selection ◽  
Survival Data ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Learning Methods ◽  
Require Time ◽  
Machine Learning Methods ◽  
Boosting Method

In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.

Morphological classification of brains via high-dimensional shape transformations and machine learning methods

NeuroImage ◽  
2004 ◽  
Vol 21 (1) ◽  
pp. 46-57 ◽  
Author(s):  
Zhiqiang Lao ◽  
Dinggang Shen ◽  
Zhong Xue ◽  
Bilge Karacali ◽  
Susan M. Resnick ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Dimensional ◽  
Morphological Classification ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Shape Transformations ◽  
Dimensional Shape

scholarly journals Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases

NeuroImage ◽  
2018 ◽  
Vol 183 ◽  
pp. 401-411 ◽  
Author(s):  
Ramon Casanova ◽  
Ryan T. Barnard ◽  
Sarah A. Gaussoin ◽  
Santiago Saldana ◽  
Kathleen M. Hayden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Factor ◽  
High Dimensional ◽  
Learning Methods ◽  
Machine Learning Methods

[P1-439]: ESTIMATING ALZHEIMER's DISEASE ANATOMICAL RISK ACROSS IMAGING DATABASES USING HIGH-DIMENSIONAL MACHINE LEARNING METHODS

2017 ◽  
Vol 13 (7S_Part_9) ◽  
pp. P450-P451
Author(s):  
Ramon Casanova ◽  
Ryan Barnard ◽  
Santiago Saldana ◽  
Sarah A. Gaussoin ◽  
Kathleen M. Hayden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Dimensional ◽  
Learning Methods ◽  
Machine Learning Methods

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

2021 ◽  
Vol 23 ◽  
Author(s):  
Xiong Li ◽  
Yangping Qiu ◽  
Juan Zhou ◽  
Ziruo Xie
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Analysis ◽  
Joint Analysis ◽  
Future Research ◽  
Omics Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Source Data

Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data. Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis. Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on. Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.

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