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High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomic functions. Machine learning (ML) models can be trained using microbial features which are then used to classify microbial data into different functional classes. For example, ML analyses over the human microbiome data has been linked to the prediction of important biological states. For analysing omics data, integrating abundance count of taxonomical features with their biological relationships is important. These relationships can potentially be uncovered from the phylogenetic tree of microbial taxa. In this paper, we propose a novel integrative framework Phy-PMRFI. This framework is driven by the phylogeny-based modeling of omics data to predict metagenomic functions using important features selected by a random forest importance (RFI) strategy. The proposed framework integrates the underlying phylogenetic tree information with abundance measures of microbial species (features) by creating a novel phylogeny and abundance aware matrix structure (PAAM). Phy-PMRFI progresses by ranking the microbial features using an RFI measure. This is then used as input for microbiome classification. The resultant feature set enhances the performance of the state-of-art methods such as support vector machines. Our proposed integrative framework also outperforms the state-of-the-art pipeline of phylogenetic isometric log-ratio transform (PhILR) and MetaPhyl. Prediction accuracy of 90 % is obtained with Phy-PMRFI over human throat microbiome in comparison to other approaches of PhILR with 53% and MetaPhyl with 71% accuracy. [ABSTRACT FROM AUTHOR]

"Metagenomics" is the study of genomic sequences obtained directly from environmental microbial communities with the aim to linking their structures with functional roles. The field has been aided in the unprecedented advancement through high-throughput omics data sequencing. The outcome of sequencing are biologically rich data sets. Metagenomic data consisting of microbial species which outnumber microbial samples, lead to the "curse of dimensionality" in datasets. Hence, the focus in metagenomics studies has moved towards developing efficient computational models using Machine Learning (ML), reducing the computational cost. In this paper, we comprehensively assessed various ML approaches to classifying high-dimensional human microbiota effectively into their functional phenotypes. We propose the application of embedded feature selection methods, namely, Extreme Gradient Boosting and Penalized Logistic Regression to determine important microbial species. The resultant feature set enhanced the performance of one of the most popular state-of-the-art methods, Random Forest (RF) over metagenomic studies. Experimental results indicate that the proposed method achieved best results in terms of accuracy, area under the Receiver Operating Characteristic curve (ROC-AUC), and major improvement in processing time. It outperformed other feature selection methods of filters or wrappers over RF and classifiers such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), and k- Nearest Neighbors (k-NN). [ABSTRACT FROM AUTHOR]

Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. The questionnaires can consist of more than 300 questions, which is tedious for people to complete at home. This paper presents a machine learning approach to analyze self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self-management. In addition, a classification model is proposed to differentiate the treatment stages. Four different feature selection methods were applied to rank the questions. In addition, four supervised learning classifiers were used to investigate the relationships between the numbers of questions and classification performance. There were no significant differences between the feature ranking methods for each classifier in overall classification accuracy or AUC ( p > 0.05); however, there were significant differences between the classifiers for each ranking method (p < 0.001). The results showed the multilayer perceptron classifier had the best classification performance on an optimized subset of questions, which consisted of ten questions. Its overall classification accuracy and AUC were 100% and 1, respectively. [ABSTRACT FROM AUTHOR]

Abstract: In recent years there has been a growing trend towards the inclusion of diverse genomic information to support comprehensive large-scale prediction of protein–protein interaction networks. The Gene Ontology (GO) is one such functional knowledge resource, which consists of three hierarchies to describe functional attributes of gene products: Molecular function, biological process, and cellular component. Using Bayesian networks, this paper presents a framework for the probabilistic combination of semantic similarity knowledge extracted from the three GO hierarchies for analysis of protein–protein interaction networks and demonstrates its application in yeast. The results indicate that by integrating information encoded in the GO hierarchies a better result can be achieved in terms of both statistical prediction capability and potential biological relevance. [Copyright &y& Elsevier]

The ABR is commonly used in the Audiology clinic to determine and quantify hearing loss. Its interpretation is subjective, dependent upon the expertise and experience of the clinical scientist. In this study we investigated the role of machine learning for pattern classification in this domain. We extracted features from the ABRs of 85 test subjects (550 waveforms) and compared four complimentary supervised classification methods: Naïve Bayes, Support Vector Machine Multi-Layer Perceptron and KStar. The Abr dataset comprised both high level and near threshold recordings, labeled as 'response' or 'no response' by the human expert. Features were extracted from single averaged recordings to make the classification process straightforward. A best classification accuracy of 83.4% was obtained using Naïve Bayes and five relevant features extracted from time and wavelet domains. Naïve Bayes also achieved the highest specificity (86.3%). The highest sensitivity (93.1%) was obtained with Support Vector Machine-based classification models. In terms of the overall classification accuracy, four classifiers have shown the consistent, relatively high performance, indicating the relevance of selected features and the feasibility of using machine learning and statistical classification models in the analysis of ABR. [ABSTRACT FROM AUTHOR]

Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual's physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient's medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer's and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging). [ABSTRACT FROM AUTHOR]