dataesr/scientific-paragraphs-categorization huggingface dataset:
For the studies identified by the NLP Toolkit, the relevance of the study was assessed based on the NLP augmented evaluation of title and abstract according to the prespecified criteria. The papers identified through an automated search of GitHub resources were scored for relevance using the trained model based on learning to rank approach. The detailed description of the methodology used in automated search and eligibility assessment for both NLP Toolkit and learning to rank approach are provided in Supplementary Material. The scoping review workflow illustrating the number of identified, scanned, and articles included in this scoping review using all three data collection procedures is presented in Figure  1. The listing of all articles included in this study labeled with respect to different descriptors/keywords is available as Multimedia Appendix.
Data Acquisition From Different Resources The human microbiome has been described as a fingerprint, unique and specific to each individual, set in early life and modeled by diet, lifestyle and environmental factors  (Gilbert et al. , 2018). Besides the high inter-variability of the microbiome, there are some shared functions between the different microbial strains, the so-called core human metagenome established by the analysis of large population studies. Moreover, the characterization of the microbial genes implied in human metabolic functions, the creation of a "gene catalog" of the human microbiome, and the description of differences between specific human conditions have been pointed out by assessing populational studies that have generated great amounts of metagenomics data. The list of main population studies, gene catalogs generated and database resources for analyzing microbiome data, respectively, are shown in Table  1.
Literature Review of ML Applications for Microbiome Studies We finally selected 89 papers for review (35 manually selected, 37 using the automated NLP Toolkit search through PubMed, IEEE Xplore and Springer digital libraries, and 17 by searching in GitHub repositories). ML implies training and evaluation of models to identify, classify, and predict patterns from data. Unsupervised methods aim to identify plausible patterns in the data, without the use of ground truth/labels, while supervised approaches rely on the given labels to train the model and learn the mapping of input features to the labels at the output. Here, we present the most frequently applied ML methods in microbiome studies, taking into account that ML applied on the large volumes of microbiome data can offer valuable insight into human-microbiome interactions We focused on those studies in which ML is used for: (i) the classification and prediction of microbial taxa, i.
Qin et al. , 2010; Wen et al. , 2017  The Unified Human Gastrointestinal Genome (UHGG) and Protein (UHGP) catalogs. 286,997 microbial genomes from the available human gut microbiome datasets. These catalogs were created by analyzing 286,997 microbial genomes and over 625 million protein sequences, including more than four thousand species. Up to 71% of the taxons analyzed are viable but non-culturable (VBNC), lacking viable culture indicating that most of the microbial diversity in the catalog remains to be characterized. Almeida et al. , 2021  MGnify (formerly EBI Metagenomics) Diverse microbiome types, including ∼63. 000 samples from human microbiome. Free-access resource for browsing, analyzing, and archiving metagenomic and metatranscriptomic data. The platform contains an automated pipeline for the analysis of microbiome data to determine the taxonomic diversity along with functional and metabolic characteristics.
https://www. ebi. ac. uk/ metagenomics/\penalty-\@M  Mitchell et al. , 2018 Mitchell et al. , , 2020  CuratedMetagenomeData Includes taxonomic and metabolic functional profiles and curated metadata for the publicly available human microbiome samples generated by shotgun metagenomic sequencing. Bioconductor  (Gentleman et al. , 2004)  package that provides uniformly processed and manually annotated human microbiome data. All data is processed by using the same pipeline, i. e. , MetaPhlAn2  (Truong et al. , 2015)  for taxonomic abundance, gene marker presence and absence, and HUMAnN2  (Franzosa et al. , 2018)  for coverage and abundance of metabolic pathways and gene families abundance. Finally, many of the reviewed ML methods have implemented within the Bioconductor packages, initially developed for the microchip/microarray-based data analyses  (Gentleman et al.
Linear discriminant analysis (LDA) Linear Discriminant Analysis (LDA) is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that provides good separation between the classes of objects or events. When applied to microbiome data, this approach finds a linear combination of microbial features in the training data that models the multivariate mean differences between classes  (Zhou and Gallins, 2019). The linear discriminant analysis (LDA) effect size (LEfSe) method proposed by The Huttenhower Lab as part of bioBakery workflows for executing microbial community analyses 3  was specifically designed for biomarker discovery in metagenomic data (16S rRNA gene and whole-genome shotgun datasets).
5 • C  (Johnson et al. , 2016). Hacılar et al. (2018)  compared several ML-based techniques to classify fecal samples as healthy or with disease [i. e. , Inflammatory Bowel Disease (IBD)]. They used a dataset containing shotgun metagenomic data from 382 individuals (234 healthy and 148 IBD patients). The training set was a profile of gut microbial communities for each sample generated by MetaPhlAn2  (Segata et al. , 2012). Several models were trained (RF, Adaboost, k-NN + LogitBoost, Decision tree, Neural network, LogitBoost and Furia) and 10-fold cross-validation was performed to evaluate the performance for each model. Finally, they added a feature selection (i. e. , mRMR: minimum redundancy and maximal relevance) step before the training process. With and without feature selection k-NN + LogitBoost performed best with 0.
, 2010)  for assembly; MetaGene  (Noguchi et al. , 2006)  for gene prediction; KEGG  (Kanehisa et al. , 2004)  and eggNOG  (Jensen et al. , 2007)  for functional annotation. They selected 50 marker genes for T2D (using mRMR: minimum redundancy and maximal relevance) out of a gene catalog containing roughly 300,000 genes. They also show that taxonomic abundance data segregates IBD and healthy individuals when performing PCoA. Cui and Zhang (  2013 ) described an alignment-free supervised classification procedure for the classification of metagenome samples into predefined classes with sequence signatures from shotgun metagenomics sequencing data by using recursive SVM, this approach integrates feature selection and classification steps in one method. They also applied the methodology on a real metagenome dataset to classify IBD and non-IBD samples.
The accuracy obtained using the stringent leave-one-out cross-validation (LOOCV) was 88%, additionally permutation experiment were performed to evaluate statistical significance  (Cui and Zhang, 2013). Liu Y. et al. (2011)  presented "MetaGUN" 4  a gene prediction method for identifying genes in metagenomic fragments based on SVM. Initially, input sequences were classified into phylogenetic groups, using a k-mer based sequence binning method. Afterward, for each group, the identification of protein-coding sequences was performed using SVM classifiers. MetaGUN applies universal prediction modules and a novel prediction module to identify protein-coding sequences. Entropy density profiles (EDP) of codon usage, Translation Initiation Side (TIS) scores and Open Reading Frame (ORF) length are employed as discriminative features and used as inputs into the classifiers to distinguish protein-coding sequences from non-coding sequences.
For microbiome data, OTUs/ASVs are commonly used at the input layer, with separate neurons for each OTU/ASV. Lo and Marculescu (2019) describe a neural network platform for the classification of host phenotypes from metagenomic data, using a new data augmentation technique to mitigate the effects of data over-fitting. They tested the proposed framework on eight real datasets including data from HMP  (Turnbaugh et al. , 2007) , and two diseases, i. e. , IBD  (Gevers et al. , 2014) , and esophagus diseases (esophagitis, Barrett's esophagus, esophagal adenocarcinoma;  Yang et al. , 2015) , finding that the new proposed methodology outperforms other models previously used in the literature  (Lo and Marculescu, 2019).
A total of 1636 validated reference samples from these sites were used to identify most informative sequence positions via correspondence analysis. High-inertia positions were used as input matrix to train 50 DL networks based on a 4-layer ANN. Two sets of samples (110 test and 41 mock casework samples) were deployed to validate the output from the deep learning approach with most of the samples being classified correctly. This approach offers a more accurate and efficient tissue-classification approach compared to human biomarkers, as donor DNA-based methods often lead to cross-identification and low specificity due to overlaps in human cell composition. However, a successful application of DL methods in such a context ideally requires standardized biological and methodological conditions during the generation of training and test data  (Díez López et al.
, 2019). Another example of using DL approach for analyses of metagenomic data are DeepARG networks which are trained to predict antibiotic resistance genes (ARGs) from metagenomic data  (Arango-Argoty et al. , 2018). DeepARG consists of two models: DeepARG-LS, which was developed to classify ARGs based on full gene length sequences, and DeepARG-SS, which was developed to identify and classify ARGs from short sequence reads. The initial collection of ARGs was obtained from three major databases: CARD, ARDB, and UNIPROT and 30 ARG categories were used to train the models. To further evaluate and validate performance, the DeepARG-LS model was applied to all the ARG sequences in the MEGARes database  (Lakin et al. , 2017). Also, the ability of the DeepARG-LS model to predict novel ARGs was tested on a set of 76 metallo-beta-lactamase genes obtained from the study of  Berglund et al.
(2017). Based on the results the authors conclude that the DeepARG models can be used to get an overview or inference of the kinds of antibiotic resistance in a collection of sequences; however, still the downstream experimental validation is required to confirm whether the sequences truly confer resistance. Asgari et al. (2019)  used deep learning, Random Forest(RF) and SVM, for distinguishing among human body-sites, diagnosis of Crohn's disease, and predicting the environments from representative 16S gene sequences. Moreover, they also proposed a reference-and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing data based on k-mer representations. They described that for large datasets (10K samples per class) using DL provides more accurate predictions.
Those phenotypes included the different subtypes of the disease: Crohn's disease (CD) and Ulcerative Colitis (UC), as well as the state of the pathology (flare or remission) and the part of the intestine that is affected for CD (ileum or colon). The dataset used for training  (Sokol et al. , 2017)  contained bacterial and fungal community (16S rDNA and ITS) from 38 controls and 222 IBD patients. Pre-processing of the raw data was carried out using QIIME2  (Kuczynski et al. , 2012) , UCLUST  (Edgar, 2010)  and RAxML  (Stamatakis, 2014) , in order to get relative abundance, cluster the taxa and build a phylogenetic tree that will then be input to the CNN. A synthetic dataset was also constructed as deep learning performs better when trained on large datasets. To do so, they generated vectors in the Aitchison simplex that is spanned by the "real" dataset.
Random forests (RF) RFs are an example of ensemble learning, in which a complex model is made by combining many simple models. In this case, simple models are decision trees. RFs use a bootstrap resampling on the given dataset to learn each decision tree using a single boostrap set. The final output of a RF is obtained using a majority voting of the individual decision trees. As these are well-studied methods, they are used as baselines for comparison in many studies  (Breiman, 2001). The most widely used ML algorithm, RF classifiers have been frequently used along with Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection, for stratification of patients  (Flemer et al. , 2017; Yachida et al. , 2019)  and biomarker finding  (Koohi-Moghadam et al. , 2019; Thomas et al.
, 2019; Wirbel et al. , 2019)  and finding of host-microbial signatures to detect fecal contamination in environmental samples  (Roguet et al. , 2018). RF has been used for classification of pediatric patients of Crohn's disease (CD) according to disease state and treatment response by using the alpha diversity of the samples and the genetic risk score (GRS) of each patient  (Douglas et al. , 2018). They found higher classification accuracy with 16S rRNA datasets than shotgun metagenomics due to the higher contamination of human DNA in the shotgun metagenomes. Ross et al. (2017)  analyzed the impact of cohabitation on the individual composition of the skin microbiome. For the analysis, the authors used 16S rDNA amplicons of bacteria and archaea from 330 skin samples from 17 skin regions of 10 heterosexual cohabiting couples.
Analysis was performed using both statistical and ML methods. Their results showed that the two most important factors that affect the skin microbiome are individuality and body region, which is in line with previous studies. The authors also showed that cohabitation strongly influences skin microbial community diversity. When RF method was applied for skin microbiome classification, accuracy greater than 86% was achieved  (Ross et al. , 2017). Ai et al. (2019)  took advantage of the continuously decreasing price of whole genome sequencing technology to diagnose colorectal cancer (CRC) based on fecal shotgun sequencing data. They used a dataset consisting of French and Austrian cohorts both containing 156 individuals (312 in total; 124 healthy and 188 CRC and adenoma patients). To preprocess the raw reads and produce the relative abundance of each taxon in the gut, they used the GRAMMy tool  (Xia et al.
Top three taxa were used to construct the flare index that was significantly different for flare and no-flare samples. Flare index also significantly correlated with microbial richness and microbial dysbiosis index  (Braun et al. , 2019). Fabijanić and Vlahoviček (2016)  utilized the translational optimization effect, a property of gene regulation, to distinguish subjects with liver cirrhosis from healthy controls using the RF classifier  (Fabijanić and Vlahoviček, 2016). Another study that utilized the RF algorithm on gut microbiome data is described by Hasic and Music; the condition studied was Multiple Sclerosis (MS). The results demonstrate the best accuracy in distinguishing control samples from MS samples when genuslevel taxa abundances were used as features. The model learned on one dataset was evaluated on another set of the MS samples coming from people living in another country.
The classification accuracy on this test set was comparable to the error on the validation set  (Telalovic and Azra, 2020). Travisany et al. (2015)  proposed an ensemble method for microbial taxa prediction present in a specific environment as well as their abundances using multiple CARTs (classification and regression tree). The authors first constructed a dataset of genomic fragments by collecting genomes from publicly available databases. They built two predictors, one using a dataset with 98 genera of the gastrointestinal tract available from the Human Microbiome Project, and the other with 17 early studied genera of the gastrointestinal tract. They computed the statistics of k-mer frequencies, GC radio and GC skew for each read for a specific environment-associated dataset. The prediction was then performed by majority vote selection of multiple (n = 558) CART trees.
, 2015). Faust et al. (2012)  employed GB to investigate co-occurrence relationships in 16S rRNA data obtained from the Human Microbiome Project. Generalized boosted linear models were fitted using taxa abundance data from source sites to predict abundances of target taxa within targets sites. The analysis was augmented with the integration of a set of similarity and dissimilarity measures (Pearson and Spearman coefficients for correlation, Bray-Curtis and Kullback-Leibler as dissimilarity measures) to finally create a network of co-occurrence and coexclusion relationships within the analyzed microbiomes. By putting these tools together, the authors were able to reveal that closer related taxa tend to co-occur in special vicinity or environmentally similar habitats whereas phylogenetically more distant microbes with similar functional aptitudes are more likely to compete.
, 2016). A publicly available dataset of 16S rRNA containing fecal sequencing data from 37 healthy individuals and 122 IBD patients) was used in order to train the three aforementioned models. For each sample, the sequenced reads were pre-processed into taxonomic and functional profiles using QIIME2  (Kuczynski et al. , 2012)  and PICRUSt  (Langille et al. , 2013)  respectively. Then, a pipeline of three consecutive classifiers (SVM for stages one and two, multilayer perceptron (MLP) for stage three) was developed and the classifiers were cross-validated. The outcomes of the different classification steps were disease-free, IBD remission and IBD active for stage one. Ulcerative colitis (UC), Crohn's disease and control for stage two and finally mild, moderate and severe for stage three. The average precision scores for the k-fold cross-validations were rather low, 0.
For Phy-PMRFI, the authors used RF to recognize microbial features that are useful for classifying phenotypic groups and improve metagenomic predictions. Afterward, the informative microbial taxa obtained acted as an input to three commonly used MLclassifiers: (1) SVM, (2) Logistic Regression, and (3) Naive Bayes, intending to identify if phylogenetic relatedness is a good predictor of functional similarity. For this, the authors used three microbiome datasets as cases to demonstrate the utility of the Phy-PMRFI framework in predicting functions of metagenomic data. They concluded that inclusion of the phylogenetic measure potentially maximizes the opportunity of classifying microbiome functions according to naturally inherent properties of taxa  (Wassan et al. , 2019). Beck and Foster (2014)  applied genetic programming, RF and logistic regression to classify microbial communities into bacterial vaginosis (BV) positive and negative categories.
Using the mentioned classification models, most important features of the microbial community used to predict BV were also identified. The classification was applied to two different datasets. The authors obtained an accuracy above 90% for Nugent score and above 80% for the Amsel criteria. Even though different sets of most important features were identified by the tested classifiers, the shared features, in general, agree with the previous research  (Beck and Foster, 2014). In the context of the human gut microbiome, Zhu et al. (  2020 ) proposed a DL ensemble feature selection model, Deep Forest, which is based on the RF method to perform microbiome-wide association studies (MWAS). When tested on three data sets using several classifiers, the proposed method achieved better classification performance than SVMs, k-NNs and convolutional neural networks (CNNs).
Performance evaluation of Deep Forest was also evaluated in terms of feature selection. The method achieved better results with the selected reduced feature subset. When the selected features were compared to the existing literature, identified microbial biomarkers have found to have a relationship with the diseases  (Zhu et al. , 2020). Statnikov et al. (2013)  performed a comprehensive evaluation of 18 ML methods and five feature selection methods to perform body site and subject multicategory classification and diagnosis using microbiome data. The evaluation was performed on eight datasets using constructed OTU tables as input features for the ML methods. Performance of evaluated methods was measured using the proportion of correct classifications and relative classifier information metrics.
From the evaluated methods, RF, SVM, kernel ridge regression, and Bayesian logistic regression with Laplace priors were among the best-performing methods with statistically similar levels of classification accuracy  (Statnikov et al. , 2013). In work published by  Eck et al. (2017)  two datasets were analyzed. One distinguished skin from gut microbiome samples and the other IBD patients from healthy individuals. Several ML algorithms were applied: Linear SVM, RF, nearest shrunken centroids, logistic regression with l 2 regularization. The authors measured the most important taxa on species level (applying intergenic spacer profiling of 16S-23S rRNA) for the classification when applying different algorithms. The identification of such taxa facilitates biologically meaningful interpretation of the microbiota-based predictions  (Eck et al.
The authors first performed a comparative analysis based on the literature review of published work, focusing on 17 reported datasets generated from OTU tables. Additionally, the authors performed their own comparative analysis of the mentioned ML methods using three datasets available from MicrobiomeHD database 5   (Duvallet et al. , 2017). For feature extraction, the authors applied a hierarchical feature engineering (HFE)  (Oudah and Henschel, 2018). Among the compared methods, decision tree-based methods, in general, performed well, achieving similar results with the neural network models in the analyzed published literature. Furthermore, by applying HFE for OTU table feature reduction, better performance results were achieved for almost all of the evaluated methods  (Zhou and Gallins, 2019).
The evaluation of aMiSPU test on simulated and real data indicates that the aMiSPU test is better performing than several competing with well-controlled type I error rates. A by-product of the method is a ranking of the importance of the taxa and be used as a selection tool for the taxa which are likely to be associated with the outcome of interest. The MiSPU R package is public and accessible at https://github. com/ChongWu-Biostat/MiSPU. Its application for understanding the association between microbial communities (i. e. , microbiotas) throughout the human body and disease can help in developing personalized medicine. Biclustering is a powerful data mining technique that allows simultaneously clustering rows and columns of a data matrix to find submatrices that can overlap  (Xie et al. , 2019).
The authors demonstrate the utility of the proposed approach by comparing four alternative methods in simulations and then by applying it into Qin's IBD microbiome dataset revealing clusters which contain bacteria families that are known to be related to the inflammatory bowel disease and its subtypes according to biological literature  (Zhou et al. , 2020). To cluster groups of communities with similar compositions into envirotypes or enterotypes and thus into "metacommunities" the Dirichlet multinomial mixture (DMM) generative modeling framework has been developed  (Holmes et al. , 2012). It assesses the community structure, including the sample density and size. Multinomial sampling coupled with Dirichlet prior was used before, but the extension of the prior to a mixture of Dirichlet components is a novelty in this work.