Detection of Colorectal Carcinoma Based on Microbiota Analysis Using Generalized Regression Neural Networks and Nonlinear Feature Selection

Arabameri, Abazar; Asemani, Davud; Teymourpour, Pegah

doi:10.1109/tcbb.2018.2870124

Cited by 9 publications

(18 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, a general regression neural networks (GRNNs) method for detecting CRC was proposed in [12]. The proposed method used a nonlinear feature selection method to filter the most predictive microbial species.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

et al. 2021

View full text Add to dashboard Cite

Colorectal cancer (CRC) is the third most deadly cancer worldwide. The use of gut microbiome in early detection of the disease has attracted much attention from the research community, mainly because of its noninvasive nature. Recent achievements in next generation sequencing technology have led to increased availability of sequence data and enabled an environment for the growth of gut microbiome research. The use of conventional machine learning algorithms for automatic detection of CRC based on the microbiome is limited by factors such as low accuracy and the need for manual selection of features. Despite their success in other fields, Deep Neural Network (DNN) algorithms have limitations in microbiome-based CRC classification. These limitations include high dimensionality of microbiome data and other characteristics associated with sequence data such as feature dominance. In this paper, we propose a feature augmentation approach that aggregates data normalization methods to extend existing features of a dataset. The proposed method combines feature extension with data augmentation to improve CRC classification performance of a DNN model. The proposed model obtained area under the curve (AUC) scores of 0.96 and 0.89 on two publicly available microbiome datasets.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For example, using Bayesian modeling, it is possible to determine the structure/topology of the gene regulatory network of several bacteria living together in the same environment [135] . Other approaches use machine learning algorithms, for example, to determine a network of cross-feeding interactions [136] ; classification of host-disease phenotypes from metagenomic data [137] , [138] ; or which bacteria are part of the normal community in certain gut regions [139] . Machine learning has been recently combined with GSMMs, expanding the potential of both approaches [140] .…”

Section: Other Approaches For Community Modelingmentioning

confidence: 99%

Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Saa

Urrutia

Silva-Andrade

et al. 2022

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.

show abstract

“…However, the study did not investigate how the model would perform in relation to a DNN algorithm in a situation where there are relatively large samples. In addition, authors in [25] proposed a nonlinear method for selecting features in order to determine the microbial species that are more predictive. The authors also combined their novel feature selection method with a general regression neural networks (GRNNs) algorithm to detect CRC in samples.…”

Section: Literature Reviewmentioning

confidence: 99%

Stacking and Chaining of Normalization Methods in Deep Learning-Based Classification of Colorectal Cancer Using Gut Microbiome Data

et al. 2021

View full text Add to dashboard Cite

Machine learning (ML)-based detection of diseases using sequence based gut microbiome data has been of great interest within the artificial intelligence in medicine (AIM) community. The approach offers a non-invasive alternative in colorectal cancer detection, where data can be obtained from stool samples. Considering limitations of existing methods for CRC detection, medical research has shown interest in the use of high throughput data to identify the disease. Owing to several limitations of conventional ML algorithms, deep learning (DL) methods are becoming more popular due to their outstanding performance in related fields. However, the performance of DL methods is affected by limitations such as dimensionality, sparsity, and feature dominance inherent in microbiome data. This research proposes stacking and chaining of normalisation methods to address the limitations. While the stacking technique offers a robust, easy to use, and interpretable alternative for augmenting microbiome and other tabular data, the chaining technique is an alternative to data normalisation that dynamically adjusts the underlying properties of data towards the normal distribution. The proposed techniques are combined with rank transformation and feature selection to further improve the performance of the model, with area under the curve (AUC) values between 0.857 to 0.987using publicly available datasets.

show abstract

Detection of Colorectal Carcinoma Based on Microbiota Analysis Using Generalized Regression Neural Networks and Nonlinear Feature Selection

Cited by 9 publications

References 48 publications

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Stacking and Chaining of Normalization Methods in Deep Learning-Based Classification of Colorectal Cancer Using Gut Microbiome Data

Contact Info

Product

Resources

About