Machine learning-based predictions of dietary restriction associations across ageing-related genes.

BACKGROUND: Dietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related genes and DR-related genetic features. RESULTS: This work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking DR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict DR-relatedness among ageing-related genes currently lacking DR-related annotations in the data, resulting in a set of promising candidate DR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted DR-relatedness remain to be validated in future wet-lab experiments. CONCLUSIONS: This work demonstrated the strong potential of ML-based techniques to identify DR-associated features as our findings are consistent with literature and recent discoveries. Although the inference of new DR-related mechanistic findings based solely on GO terms and biological pathways was limited due to their knowledge-driven nature, the predictive power of these two features types remained useful as it allowed inferring new promising candidate DR-related genes.

Investigating the role of Simpson's paradox in the analysis of top-ranked features in high-dimensional bioinformatics datasets.

An important problem in bioinformatics consists of identifying the most important features (or predictors), among a large number of features in a given classification dataset. This problem is often addressed by using a machine learning-based feature ranking method to identify a small set of top-ranked predictors (i.e. the most relevant features for classification). The large number of studies in this area has, however, an important limitation: they ignore the possibility that the top-ranked predictors occur in an instance of Simpson's paradox, where the positive or negative association between a predictor and a class variable reverses sign upon conditional on each of the values of a third (confounder) variable. In this work, we review and investigate the role of Simpson's paradox in the analysis of top-ranked predictors in high-dimensional bioinformatics datasets, in order to avoid the potential danger of misinterpreting an association between a predictor and the class variable. We perform computational experiments using four well-known feature ranking methods from the machine learning field and five high-dimensional datasets of ageing-related genes, where the predictors are Gene Ontology terms. The results show that occurrences of Simpson's paradox involving top-ranked predictors are much more common for one of the feature ranking methods.

Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes.

Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a 'background' set of genes. These enrichment methods, although based on rigorous statistical foundations, are not always the best single option to identify patterns in biological data. In many cases, one can also use classification algorithms from the machine-learning field. Unlike enrichment methods, classification algorithms are designed to maximize measures of predictive performance and are capable of analysing combinations of gene properties, instead of one property at a time. In practice, however, the majority of studies use either enrichment or classification methods (rather than both), and there is a lack of literature discussing the pros and cons of both types of method. The goal of this paper is to compare and contrast enrichment and classification methods, offering two contributions. First, we discuss the (to some extent complementary) advantages and disadvantages of both types of methods for identifying gene properties that discriminate between gene classes. Second, we provide a set of high-level recommendations for using enrichment and classification methods. Overall, by highlighting the strengths and the weaknesses of both types of methods we argue that both should be used in bioinformatics analyses.

Using deep learning to associate human genes with age-related diseases.

MOTIVATION: One way to identify genes possibly associated with ageing is to build a classification model (from the machine learning field) capable of classifying genes as associated with multiple age-related diseases. To build this model, we use a pre-compiled list of human genes associated with age-related diseases and apply a novel Deep Neural Network (DNN) method to find associations between gene descriptors (e.g. Gene Ontology terms, protein-protein interaction data and biological pathway information) and age-related diseases. RESULTS: The novelty of our new DNN method is its modular architecture, which has the capability of combining several sources of biological data to predict which ageing-related diseases a gene is associated with (if any). Our DNN method achieves better predictive performance than standard DNN approaches, a Gradient Boosted Tree classifier (a strong baseline method) and a Logistic Regression classifier. Given the DNN model produced by our method, we use two approaches to identify human genes that are not known to be associated with age-related diseases according to our dataset. First, we investigate genes that are close to other disease-associated genes in a complex multi-dimensional feature space learned by the DNN algorithm. Second, using the class label probabilities output by our DNN approach, we identify genes with a high probability of being associated with age-related diseases according to the model. We provide evidence of these putative associations retrieved from the DNN model with literature support. AVAILABILITY AND IMPLEMENTATION: The source code and datasets can be found at: https://github.com/fabiofabris/Bioinfo2019. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new approach for interpreting Random Forest models and its application to the biology of ageing.

Motivation: This work uses the Random Forest (RF) classification algorithm to predict if a gene is over-expressed, under-expressed or has no change in expression with age in the brain. RFs have high predictive power, and RF models can be interpreted using a feature (variable) importance measure. However, current feature importance measures evaluate a feature as a whole (all feature values). We show that, for a popular type of biological data (Gene Ontology-based), usually only one value of a feature is particularly important for classification and the interpretation of the RF model. Hence, we propose a new algorithm for identifying the most important and most informative feature values in an RF model. Results: The new feature importance measure identified highly relevant Gene Ontology terms for the aforementioned gene classification task, producing a feature ranking that is much more informative to biologists than an alternative, state-of-the-art feature importance measure. Availability and implementation: The dataset and source codes used in this paper are available as 'Supplementary Material' and the description of the data can be found at: https://fabiofabris.github.io/bioinfo2018/web/. Supplementary information: Supplementary data are available at Bioinformatics online.

Systematic analysis of the gerontome reveals links between aging and age-related diseases.

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the 'gerontome'. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.

New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins.

MOTIVATION: The incidence of ageing-related diseases has been constantly increasing in the last decades, raising the need for creating effective methods to analyze ageing-related protein data. These methods should have high predictive accuracy and be easily interpretable by ageing experts. To enable this, one needs interpretable classification models (supervised machine learning) and features with rich biological meaning. In this paper we propose two interpretable feature types based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and compare them with traditional feature types in hierarchical classification (a more challenging classification task regarding predictive performance) and binary classification (a classification task producing easier to interpret classification models). As far as we know, this work is the first to: (i) explore the potential of the KEGG pathway data in the hierarchical classification setting, (i) use the graph structure of KEGG pathways to create a feature type that quantifies the influence of a current protein on another specific protein within a KEGG pathway graph and (iii) propose a method for interpreting the classification models induced using KEGG features. RESULTS: We performed tests measuring predictive accuracy considering hierarchical and binary class labels extracted from the Mouse Phenotype Ontology. One of the KEGG feature types leads to the highest predictive accuracy among five individual feature types across three hierarchical classification algorithms. Additionally, the combination of the two KEGG feature types proposed in this work results in one of the best predictive accuracies when using the binary class version of our datasets, at the same time enabling the extraction of knowledge from ageing-related data using quantitative influence information. AVAILABILITY AND IMPLEMENTATION: The datasets created in this paper will be freely available after publication. CONTACT: ff79@kent.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A review of supervised machine learning applied to ageing research.

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results.

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

Comparing multilabel classification methods for provisional biopharmaceutics class prediction.

The biopharmaceutical classification system (BCS) is now well established and utilized for the development and biowaivers of immediate oral dosage forms. The prediction of BCS class can be carried out using multilabel classification. Unlike single label classification, multilabel classification methods predict more than one class label at the same time. This paper compares two multilabel methods, binary relevance and classifier chain, for provisional BCS class prediction. Large data sets of permeability and solubility of drug and drug-like compounds were obtained from the literature and were used to build models using decision trees. The separate permeability and solubility models were validated, and a BCS validation set of 127 compounds where both permeability and solubility were known was used to compare the two aforementioned multilabel classification methods for provisional BCS class prediction. Overall, the results indicate that the classifier chain method, which takes into account label interactions, performed better compared to the binary relevance method. This work offers a comparison of multilabel methods and shows the potential of the classifier chain multilabel method for improved biological property predictions for use in drug discovery and development.

Coping with unbalanced class data sets in oral absorption models.

Class imbalance occurs frequently in drug discovery data sets. In oral absorption data sets, in the literature, there are considerably more highly absorbed compounds compared to poorly absorbed compounds. This produces models that are biased toward highly absorbed compounds which lack generalization to industry settings where more early stage drug candidates are poorly absorbed. This paper presents two strategies to cope with unbalanced class data sets: undersampling the majority high absorption class and misclassification costs using classification decision trees. The published data set by Hou et al. [J. Chem. Inf. Model.2007, 47, 208-218], which contained percentage human intestinal absorption of 645 drug and drug-like compounds, was used for the development and validation of classification trees using classification and regression tree (C&RT) analysis. The results indicate that undersampling the majority class, highly absorbed compounds, leads to a balanced distribution (50:50) training set which can achieve better accuracies for poorly absorbed compounds, whereas the biased training set achieved higher accuracies for highly absorbed compounds. The use of misclassification costs resulted in improved class predictions, when applied to reduce false positives or false negatives. Moreover, it was shown that the classical overall accuracy measure used in many publications is particularly misleading in the case of unbalanced data sets and more appropriate measures presented here may be used for a more realistic assessment of the classification models' performance. Thus, these strategies offer improvements to cope with unbalanced class data sets to obtain classification models applicable in industry.

Pre-processing feature selection for improved C&RT models for oral absorption.

There are currently thousands of molecular descriptors that can be calculated to represent a chemical compound. Utilizing all molecular descriptors in Quantitative Structure-Activity Relationships (QSAR) modeling can result in overfitting, decreased interpretability, and thus reduced model performance. Feature selection methods can overcome some of these problems by drastically reducing the number of molecular descriptors and selecting the molecular descriptors relevant to the property being predicted. In particular, decision trees such as C&RT, although they have an embedded feature selection algorithm, can be inadequate since further down the tree there are fewer compounds available for descriptor selection, and therefore descriptors may be selected which are not optimal. In this work we compare two broad approaches for feature selection: (1) a "two-stage" feature selection procedure, where a pre-processing feature selection method selects a subset of descriptors, and then classification and regression trees (C&RT) selects descriptors from this subset to build a decision tree; (2) a "one-stage" approach where C&RT is used as the only feature selection technique. These methods were applied in order to improve prediction accuracy of QSAR models for oral absorption. Additionally, this work utilizes misclassification costs in model building to overcome the problem of the biased oral absorption data sets with more highly absorbed than poorly absorbed compounds. In most cases the two-stage feature selection with pre-processing approach had higher model accuracy compared with the one-stage approach. Using the top 20 molecular descriptors from the random forest predictor importance method gave the most accurate C&RT classification model. The molecular descriptors selected by the five filter feature selection methods have been compared in relation to oral absorption. In conclusion, the use of filter pre-processing feature selection methods and misclassification costs produce models with better interpretability and predictability for the prediction of oral absorption.

Automatic design of decision-tree algorithms with evolutionary algorithms.

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features.

Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four types of datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on: compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase's Phenotype Ontology. To analyse these datasets, we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. In addition, we interpreted the most important features in the two best models in light of the biology of ageing. One noteworthy feature was the GO term "Glutathione metabolic process", which plays an important role in cellular redox homeostasis and detoxification. We also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds. These include nitroprusside, which is used as an antihypertensive medication. Overall, our work opens avenues for future work in employing machine learning to predict novel life-extending compounds.

Interpretable Ensembles of Classifiers for Uncertain Data With Bioinformatics Applications.

Data uncertainty remains a challenging issue in many applications, but few classification algorithms can effectively cope with it. An ensemble approach for uncertain categorical features has recently been proposed, achieving promising results. It consists in biasing the sampling of features for each model in an ensemble so that less uncertain features are more likely to be sampled. Here we extend this idea of biased sampling and propose two new approaches: one for selecting training instances for each model in an ensemble and another for sampling features to be considered when splitting a node in a Random Forest training. We applied these approaches to classify ageing-related genes and predict drugs' side effects based on uncertain features representing protein-protein and protein-chemical interactions. We show that ensembles based on our proposed approaches achieve better predictive performance. In particular, our proposed approaches improved the performance of a Random Forest based on the most sophisticated approach for handling uncertain data in ensembles of this kind. Furthermore, we propose two new approaches for interpreting an ensemble of Naive Bayes classifiers and analyse their results on our datasets of ageing-related genes and drug's side effects.

Machine learning methods applied to audit of surgical margins after curative surgery for facial (non-melanoma) skin cancer.

We aimed to build a model to predict positive margin status after curative excision of facial non-melanoma skin cancer based on known risk factors that contribute to the complexity of the case mix. A pathology output of consecutive histology reports was requested from three oral and maxillofacial units in the south east of England. The dependent variable was a deep margin with peripheral margin clearance at a 0.5 mm threshold. A total of 3354 cases were analysed. Positivity of either the peripheral or deep margin for both squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) was 15.4% at Unit 1, 21.1% at Unit 2, and 15.4% at Unit 3. Predictive models accounting for patient and tumour factors were developed using automated machine learning methods. The champion models demonstrated good discrimination for predicting margin status after excision of BCCs (AUROC = 0.67) and SCCs (AUROC = 0.71). We demonstrate that rates of positive excision margins of facial non-melanoma skin cancer (fNMSC), when adjusted by the risk prediction model, can be used to compare unit performance fairly once variations in tumour factors and patient factors are accounted for.

Different criteria for body mass index classification for excess weight screening in children aged six to ten years.

OBJECTIVE: To evaluate the agreement between body mass index (BMI) parameters applied to children aged six to ten years in the city of Montes Claros (MG), Brazil with national and international criteria, also calculating their sensitivity and specificity regarding excess weight screening. METHODS: A sample comprising 4151 children aged six to ten years was assessed, with height and body mass determined for BMI calculation. The obtained values were classified according to cutoff points established by the World Health Organization (WHO), International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), Conde & Monteiro, and a recent local proposal. The agreement index between the mentioned criteria was calculated and thereafter the sensitivity and specificity. RESULTS: The local proposal was proven to be highly consistent in most combinations, especially concerning the excess weight criteria of the World Health Organization (WHO) (k=0.895). Regarding excess weight, the local proposal presented sensitivity and specificity values of 0.8680 and 0.9956, respectively, indicating high BMI discrimination power. CONCLUSIONS: The locally applied BMI parameters for children aged six to ten years represent a valid, highly viable and practical proposal for excess weight screening in this population group, improving professional decision-making in their follow-up.

The evolution of endocrine disruptor chemical assessments worldwide in the last three decades.

Endocrine Disrupting Chemicals (EDCs) encompass a wide variety of substances capable of interfering with the endocrine system, including but not limited to bisphenol A, organochlorines, polybrominated flame retardants, alkylphenols and phthalates. These compounds are widely produced and used in everyday modern life and have increasingly been detected in aquatic matrices worldwide. In this context, this study aimed to carry out a literature review to assess the evolution of EDCs detected in different matrices in the last thirty years. A bibliometric analysis was conducted at the Scopus, Web of Science, and Google Scholar databases. Data were evaluated using the Vosviewer 1.6.17 software. A total of 3951 articles in English were retrieved following filtering. The results demonstrate a gradual and significant growth in the number of published documents, strongly associated with the increasing knowledge on the real environmental impacts of these compounds. Studied were mostly conducted by developed countries in the first two decades, 1993 to 2012, but in the last decade (2013 to 2022), an exponential leap in the number of publications by countries such as China and an advance in research by developing countries, such as Brazil, was verified.

Machine learning methods applied to risk adjustment of cumulative sum chart methodology to audit free flap outcomes after head and neck surgery.

We describe a risk adjustment algorithm to benchmark and report free flap failure rates after immediate reconstruction of head and neck defects. A dataset of surgical care episodes for curative surgery for head and neck cancer and immediate reconstruction (n = 1593) was compiled from multiple NHS hospitals (n = 8). The outcome variable was complete flap failure. Classification models using preoperative patient demographic data, operation data, functional status data and tumour stage data, were built. Machine learning processes are described to model free flap failure. Overall complete flap failure was uncommon (4.7%) with a non-statistical difference seen between hospitals. The champion predictive model had acceptable discrimination (AUROC 0.66). This model was used to risk-adjust cumulative sum (CuSUM) charts. The use of CuSUM charts is a viable way to monitor in a 'Live Dashboard' this quality metric as part of the quality outcomes in oral and maxillofacial surgery audit.

Multiproxy analysis in contaminated sediments from Niterói Harbour (Guanabara Bay), Brazil.

Harbours are located in major urban centres around the world and are of great economic importance to the cities in their surroundings. However, the intense traffic of boats and ships can generate environmental impacts that can directly affect the local biota as well as the population that lives in surrounding areas. Therefore, this work aimed to analyse the surface sediment of the Niterói Harbour using chemical, biological and micropalaeontological tools to investigate the environmental condition of this important harbour in Rio de Janeiro State. The pseudototal trace metal data analysed in the surface samples showed values far above those of the greater Guanabara Bay background. These data were corroborated by a high mortality rate of Artemia sp. and elevated presence of the bacterium Vibrio fischeri, indicating a high rate of local pollution. Dinoflagellate cysts also showed a direct response to high values of pseudototal trace metals. The data obtained in this study emphasize a need for greater monitoring of ports since the experience gained through this study in a Brazilian harbour can serve as an example for the management of other harbours located in large urban centres around the world.