scholarly journals It takes guts to learn: machine learning techniques for disease detection from the gut microbiome

2021 ◽  
Author(s):  
Kristen D. Curry ◽  
Michael G. Nute ◽  
Todd J. Treangen
Keyword(s):  
Machine Learning ◽  
Gut Microbiome ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Neurological Deficits ◽  
Irritable Bowel Disease ◽  
Learning Techniques ◽  
Irritable Bowel ◽  
Microbiome Data ◽  
Future Work

Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.

Applications of Machine Learning Techniques in Disease Classification From Medical Images

2018 ◽  
pp. 318-337 ◽  
Author(s):  
Bikesh Kumar Singh ◽  
Satya Eswari Jujjavarapu
Keyword(s):  
Machine Learning ◽  
Medical Images ◽  
Radial Basis Function Network ◽  
Objective Evaluation ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Future Challenges ◽  
Artificial Neural Network Ann ◽  
Applications Of Machine Learning

Machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), radial basis function network (RBFN), random forest (RF), naive Bayes classifier, etc. have gained much attention in recent years due to their widespread applications in diverse fields. This chapter is focused on providing a comprehensive insight of various techniques employed for key areas of medical image processing and analysis. Different applications covered in this chapter include feature extraction, feature selection, and cancer classification in medical images. The authors present current practices and evaluation measures used for objective evaluation of different machine learning methods in context to above-mentioned applications. Various factors associated with acceptance/rejection of such automated systems by medical research community are discussed. The authors also discuss how the interaction between automated analysis systems and medical professionals can be improved for its acceptance in clinical practice. They conclude the chapter by presenting research gaps and future challenges.

Skin Disease Classification Using Machine Learning Techniques

2021 ◽  
pp. 597-608
Author(s):  
Mohammad Ashraful Haque Abir ◽  
Golam Kibria Anik ◽  
Shazid Hasan Riam ◽  
Mohammed Ariful Karim ◽  
Azizul Hakim Tareq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Skin Disease ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques

Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques

2021 ◽  
Vol 58 (2) ◽  
pp. 1-10
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Jamal Abdul Nasir ◽  
Arooj Fatima ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Space ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Corn Seed ◽  
Area Of Interest ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Correlation Based Feature Selection ◽  
Conventional Machine

The purpose of this learning is to detect the Corn Seed Fusarium Disease using Hybrid Feature Space and Conventional machine learning (ML) approaches. A novel machine learning approach is employed for the classification of a total of six types of corn seed are collected which contain Infected Fusarium (moniliforme, graminearum, gibberella, verticillioides, kernel) as well as healthy corn seed, based on a multi-feature dataset, which is the grouping of geometric, texture and histogram features extracted from digital images. For each corn seed image, a total of twenty-five multi-features have been developed on every area of interest (AOI), sizes (50 × 50), (100 × 100), (150 × 150), and (200 × 200). A total of seven optimized features were selected by using a machine learning-based algorithm named “Correlation-based Feature Selection”. For experimentation, “Random forest”, “BayesNet” and “LogitBoost” have been employed using an optimized multi-feature user-supplied dataset divided with 70% training and 30 % testing. A comparative analysis of three ML classifiers RF, BN, and LB have been used and a considerably very high classification ratio of 96.67 %, 97.22 %, and 97.78 % have been achieved respectively when the AOI size (200×200) have been deployed to the classifiers.

scholarly journals TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction

2020 ◽  
Vol 36 (17) ◽  
pp. 4544-4550 ◽  
Author(s):  
Divya Sharma ◽  
Andrew D Paterson ◽  
Wei Xu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Human Microbiome ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Operational Taxonomic Units ◽  
Learning Techniques ◽  
Conventional Machine ◽  
Risk Of Disease ◽  
Microbiome Data

Abstract Motivation Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently, as compared to using a single CNN ignoring OTU correlations. Results We used simulated datasets containing 168 OTUs in 200 cases and 200 controls for model testing. Thirty-two OTUs, potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity. We also implemented this novel method in two human microbiome studies: (i) Cirrhosis with 118 cases, 114 controls; (ii) type 2 diabetes (T2D) with 170 cases, 174 controls; to demonstrate the model’s effectiveness. Extensive experimentation and comparison against conventional machine learning techniques yielded encouraging results. We obtained mean AUC values of 0.88, 0.92, 0.75, showing a consistent increment (5%, 3%, 7%) in simulations, Cirrhosis and T2D data, respectively, against the next best performing method, Random Forest. Availability and implementation https://github.com/divya031090/TaxoNN_OTU. Supplementary information Supplementary data are available at Bioinformatics online.

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