Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

Abstract Background previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software. Results here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning–based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression. Conculsion Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

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Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data

10.1101/214254 ◽

2017 ◽

Author(s):

Fadhl M Alakwaa ◽

Kumardeep Chaudhary ◽

Lana X Garmire

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Estrogen Receptor ◽

Deep Learning ◽

Support Vector ◽

Integrated Analysis ◽

Learning Method ◽

Learning Methods ◽

Metabolomics Data ◽

Machine Learning Methods

ABSTRACTMetabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER-patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value<0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion & absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accurcy (AUC=0.93) and better revelation of disease biology. We encourage the adoption of autoencoder based deep learning method in the metabolomics research community for classification.

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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RCy3: Network biology using Cytoscape from within R

F1000Research ◽

10.12688/f1000research.20887.3 ◽

2019 ◽

Vol 8 ◽

pp. 1774 ◽

Cited By ~ 1

Author(s):

Julia A. Gustavsen ◽

Shraddha Pai ◽

Ruth Isserlin ◽

Barry Demchak ◽

Alexander R. Pico

Keyword(s):

Shortest Path ◽

Future Development ◽

Enrichment Analysis ◽

Network Biology ◽

R Package ◽

Programming Environment ◽

R Packages ◽

R Programming ◽

Shortest Path Algorithms ◽

Rest Api

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

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Modulation Classification of Underwater Communication with Deep Learning Network

Computational Intelligence and Neuroscience ◽

10.1155/2019/8039632 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

Yan Wang ◽

Hao Zhang ◽

Zhanliang Sang ◽

Lingwei Xu ◽

Conghui Cao ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Deep Learning ◽

Language Processing ◽

Communication Process ◽

Underwater Acoustic Communication ◽

Learning Method ◽

Modulation Recognition ◽

Learning Methods ◽

Machine Learning Methods

Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

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RCy3: Network biology using Cytoscape from within R

F1000Research ◽

10.12688/f1000research.20887.2 ◽

2019 ◽

Vol 8 ◽

pp. 1774 ◽

Cited By ~ 13

Author(s):

Julia A. Gustavsen ◽

Shraddha Pai ◽

Ruth Isserlin ◽

Barry Demchak ◽

Alexander R. Pico

Keyword(s):

Shortest Path ◽

Future Development ◽

Enrichment Analysis ◽

Network Biology ◽

R Package ◽

Programming Environment ◽

R Packages ◽

R Programming ◽

Shortest Path Algorithms ◽

Rest Api

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

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Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy

Sensors ◽

10.3390/s20247078 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7078

Author(s):

Yueting Wang ◽

Minzan Li ◽

Ronghua Ji ◽

Minjuan Wang ◽

Lihua Zheng

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Model ◽

Total Nitrogen ◽

Nir Spectroscopy ◽

Learning Method ◽

Learning Methods ◽

Data Set ◽

Machine Learning Methods ◽

Conventional Machine

Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R2) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 × 1 convolutions preforms better (R2 = 0.93; RMSEP = 0.95 g/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment.

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RCy3: Network Biology using Cytoscape from within R

10.1101/793166 ◽

2019 ◽

Author(s):

Julia A. Gustavsen ◽

Shraddha Pai ◽

Ruth Isserlin ◽

Barry Demchak ◽

Alexander R. Pico

Keyword(s):

Shortest Path ◽

Future Development ◽

Enrichment Analysis ◽

Network Biology ◽

R Package ◽

Programming Environment ◽

R Packages ◽

R Programming ◽

Shortest Path Algorithms ◽

Rest Api

AbstractRCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from igraph and graph ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

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RCy3: Network biology using Cytoscape from within R

F1000Research ◽

10.12688/f1000research.20887.1 ◽

2019 ◽

Vol 8 ◽

pp. 1774

Author(s):

Julia A. Gustavsen ◽

Shraddha Pai ◽

Ruth Isserlin ◽

Barry Demchak ◽

Alexander R. Pico

Keyword(s):

Shortest Path ◽

Future Development ◽

Enrichment Analysis ◽

Network Biology ◽

R Package ◽

Programming Environment ◽

R Packages ◽

R Programming ◽

Shortest Path Algorithms ◽

Rest Api

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

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InterMineR: an R package for InterMine databases

Bioinformatics ◽

10.1093/bioinformatics/btz039 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3206-3207 ◽

Cited By ~ 4

Author(s):

Konstantinos A Kyritsis ◽

Bing Wang ◽

Julie Sullivan ◽

Rachel Lyne ◽

Gos Micklem

Keyword(s):

Data Retrieval ◽

R Package ◽

Supplementary Information ◽

Programming Environment ◽

Biological Databases ◽

Statistical Tools ◽

Bioconductor Project ◽

Term Enrichment ◽

R Programming ◽

Downstream Analysis

Abstract Summary InterMineR is a package designed to provide a flexible interface between the R programming environment and biological databases built using the InterMine platform. The package offers access to the flexible query builder and the library of term enrichment tools of the InterMine framework, as well as interoperability with other Bioconductor packages. This facilitates automation of data retrieval tasks as well as downstream analysis with existing statistical tools in the R environment. Availability and implementation InterMineR is free and open source, released under the LGPL licence and available from the Bioconductor project and Github (https://bioconductor.org/packages/release/bioc/html/InterMineR.html, https://github.com/intermine/interMineR). Supplementary information Supplementary data are available at Bioinformatics online.

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