Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

COST Action CA18131 Cierva Grant IJC2019-042188-I (LM-Z) Estonian Research Council grant PUT 1371The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.publishersversionpublishe

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Machine Learning representation of loss of eye regularity in a drosophila neurodegenerative model

The fruit fly compound eye is a premier experimental system for modeling human neurodegenerative diseases. The disruption of the retinal geometry has been historically assessed using time-consuming and poorly reliable techniques such as histology or pseudopupil manual counting. Recent semiautomated quantification approaches rely either on manual region-of-interest delimitation or engineered features to estimate the extent of degeneration. This work presents a fully automated classification pipeline of bright-field images based on orientated gradient descriptors and machine learning techniques. An initial region-of-interest extraction is performed, applying morphological kernels and Euclidean distance-to-centroid thresholding. Image classification algorithms are trained on these regions (support vector machine, decision trees, random forest, and convolutional neural network), and their performance is evaluated on independent, unseen datasets. The combinations of oriented gradient C gaussian kernel Support Vector Machine [0.97 accuracy and 0.98 area under the curve (AUC)] and fine-tuned pre-trained convolutional neural network (0.98 accuracy and 0.99 AUC) yielded the best results overall. The proposed method provides a robust quantification framework that can be generalized to address the loss of regularity in biological patterns similar to the Drosophila eye surface and speeds up the processing of large sample batche

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection

Motivation: In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results: Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability and Implementation: IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/ approximately SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

Feature Selection Stability and Accuracy of Prediction Models for Genomic Prediction of Residual Feed Intake in Pigs Using Machine Learning

Feature selection (FS, i.e., selection of a subset of predictor variables) is essential in high-dimensional datasets to prevent overfitting of prediction/classification models and reduce computation time and resources. In genomics, FS allows identifying relevant markers and designing low-density SNP chips to evaluate selection candidates. In this research, several univariate and multivariate FS algorithms combined with various parametric and non-parametric learners were applied to the prediction of feed efficiency in growing pigs from high-dimensional genomic data. The objective was to find the best combination of feature selector, SNP subset size, and learner leading to accurate and stable (i.e., less sensitive to changes in the training data) prediction models. Genomic best linear unbiased prediction (GBLUP) without SNP pre-selection was the benchmark. Three types of FS methods were implemented: (i) filter methods: univariate (univ.dtree, spearcor) or multivariate (cforest, mrmr), with random selection as benchmark; (ii) embedded methods: elastic net and least absolute shrinkage and selection operator (LASSO) regression; (iii) combination of filter and embedded methods. Ridge regression, support vector machine (SVM), and gradient boosting (GB) were applied after pre-selection performed with the filter methods. Data represented 5,708 individual records of residual feed intake to be predicted from the animal’s own genotype. Accuracy (stability of results) was measured as the median (interquartile range) of the Spearman correlation between observed and predicted data in a 10-fold cross-validation. The best prediction in terms of accuracy and stability was obtained with SVM and GB using 500 or more SNPs [0.28 (0.02) and 0.27 (0.04) for SVM and GB with 1,000 SNPs, respectively]. With larger subset sizes (1,000–1,500 SNPs), the filter method had no influence on prediction quality, which was similar to that attained with a random selection. With 50–250 SNPs, the FS method had a huge impact on prediction quality: it was very poor for tree-based methods combined with any learner, but good and similar to what was obtained with larger SNP subsets when spearcor or mrmr were implemented with or without embedded methods. Those filters also led to very stable results, suggesting their potential use for designing low-density SNP chips for genome-based evaluation of feed efficiency.info:eu-repo/semantics/publishedVersio

Functional characterization of single amino acid variants

Single amino acid variants (SAVs) are one of the main causes of Mendelian disorders, and play an important role in the development of many complex diseases. At the same time, they are the most common kind of variation affecting coding DNA, without generally presenting any damaging effect. With the advent of next generation sequencing technologies, the detection of these variants in patients and the general population is easier than ever, but the characterization of the functional effects of each variant remains an open challenge. It is our objective in this work to tackle this problem by developing machine learning based in silico SAVs pathology predictors. Having the PMut classic predictor as a starting point, we have rethought the entire supervised learning pipeline, elaborating new training sets, features and classifiers. PMut2017 is the first result of these efforts, a new general-purpose predictor based on SwissVar and trained on 12 different conservation scores. Its performance, evaluated bothby cross-validation and different blind tests, was in line with the best predictors published to date. Continuing our efforts in search for more accurate predictors, especially for those cases were general predictors tend to fail, we developed PMut-S, a suite of 215 protein-specific predictors. Similar to PMut in nature, Pmut-S introduced the use of co-evolution conservation features and balanced training sets, and showed improved performance, specially for those proteins that were more commonly misclassified by PMut. Comparing PMut-S to other specific predictors we proved that it is possible to train specific predictors using a unique automated pipeline and match the results of most gene specific predictors released to date. The implementation of the machine learning pipeline of both PMut and PMut-S was released as an open source Python module: PyMut, which bundles functions implementing the features computation and selection, classifier training and evaluation, plots drawing, among others. Their predictions were also made available in a rich web portal, which includes a precomputed repository with analyses of more than 700 million variants on over 100,000 human proteins, together with relevant contextual information such as 3D visualizationsof protein structures, links to databases, functional annotations, and more.Les mutacions puntuals d’aminoàcids són la principal causa de moltes malalties mendelianes, i juguen un paper important en el desenvolupament de moltes malalties complexes. Alhora, són el tipus de variant més comuna que afecta l’ADN codificant de proteïnes, sense provocar, en general, cap efecte advers. Amb l’adveniment de la seqüenciació de nova generació, la detecció d’aquestes variants en pacients i en la població general és més fàcil que mai, però la caracterització dels efectes funcionals de cada variant segueix sent un repte. El nostre objectiu en aquest treball és abordar aquest problema desenvolupant predictors de patologia in silico basats en l’aprenentatge automàtic. Prenent el predictor clàssic PMut com a punt de partida, hem repensat tot el procés d’aprenentatge supervisat, elaborant nous conjunts d’entrenament, descriptors i classificadors. PMut2017 és el primer resultat d’aquests esforços, un nou predictor basat en SwissVar i entrenat amb 12 mètriques de conservació de seqüència. La seva precisió, mesurada mitjançant validació creuada i amb tests cecs, s’ha mostrar en línia amb els millors predictors publicats a dia d’avui. Continuant els nostres esforços en la cerca de predictors més acurats, hem desenvolupat PMut-S, un conjunt de 215 predictors específics per cada proteïna. Similar a PMut en la seva concepció, PMut-S introdueix l’ús de descriptors basats en la coevolució i conjunts d’entrenament balancejats, millorant el rendiment de PMut2017 en 0.1 punts del coeficient de correlació de Matthews. Comparant PMut-S a d’altres predictors específics hem provat que és possible entrenar predictors específics seguint un únic procediment automatitzat i assolir uns resultats tan bon com els de la majoria de predictors específics publicats. La implementació del procediment d’aprenentatge automàtic tant de PMut com de PMut-S ha sigut publicat com a un mòdul de Python de codi obert: PyMut, el qual inclou les funcions que implementen el càlcul dels descriptors i la seva selecció, l’entrenament i avaluació dels classificadors, el dibuix de diverses gràfiques... Les prediccions també estan disponibles en un portal web que inclou un repositori precalculat amb els anàlisis de més de 700 milions de variants en més de 100 mil proteïnes humanes, junt a rellevant informació de context com visualitzacions 3D de les proteïnes, enllaços a bases de dades, anotacions funcionals i molt més

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.DDF, YG, AP, CWD, BBM, DH, JR, and VC have been funded by Enveda Biosciences. This work has been funded by Enveda Biosciences (https://www.envedabio.com/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SM and DRB received no specific funding for this work.Peer ReviewedPostprint (author's final draft