Application of Deep Learning in Microbiome

Qiang Zhu; Ban Huo; Han Sun; Bojing Li; Xingpeng Jiang

doi:10.2991/jaims.d.201028.001

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Volume 1, Issue 1-2, June 2020, Pages 23 - 29

Application of Deep Learning in Microbiome

Authors

Qiang Zhu¹^{, 4}, Ban Huo²^{, 4}, Han Sun³^{, 4}, Bojing Li²^{, 4}, Xingpeng Jiang²^{, 4}^{, *}^,

¹School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei, China

²Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China

³School of Mathematics and Statistics, Central China Normal University, Wuhan, Hubei, China

⁴School of Computer, Central China Normal University, Wuhan, Hubei, China

^*Corresponding author. Email: xpjiang@mail.ccnu.edu.cn

Corresponding Author

Xingpeng Jiang

Received 11 December 2019, Accepted 15 October 2020, Available Online 4 November 2020.

DOI: 10.2991/jaims.d.201028.001 How to use a DOI?
Keywords: Microbiome; Deep learning; Phylogeny
Abstract: With the rapid development of high-throughput sequencing technology, massive microbial data has been accumulated. The understanding of the microbial data could help us to find the relationships between microbes and diseases. However, due to the high dimensionality, sparseness, and complexity of the data, traditional machine learning methods have insufficient learning and representational ability. Meanwhile, the rise of deep learning enables us to deal with these complex problems effectively. In this survey, we introduce the application of machine learning in microbial data analysis and focus on microbial classification and feature selection tasks. In particular, we discuss the current application and challenges of deep learning in microbial studies. Based on these discussions, we recommend that before using deep learning to conduct microbiome-wide association studies, it is essential to consider prior knowledge such as phylogeny, which would improve the accuracy and interpretability of the model.
Copyright: © 2020 The Authors. Published by Atlantis Press B.V.
Open Access: This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION

A microbiota may consist of bacteria, archaea, fungi, viruses, and protists. All of the genetic materials in a specific ecological community are called the microbiome [1]. Recent studies in the microbiome have developed rapidly and researchers have found the microbiota could have a profound impact on human health. For example, studies have shown microbes in the human body have a great impact on immunology, digestion, absorption, and other physiological activities [2]. It is believed that there might be two-way communication between the brain and the intestine [3]. In addition, the intestinal flora may have a close relationship with irritable bowel syndrome [4] and it may lead to chronic kidney disease [5]. Therefore, the microbiome is often called the second human genome [6]. Compared with our human genome, it is much easier to intervene with the microbiota, which will make it an ideal target for medical treatment. However, it is estimated the number of microbial genes in the human body is much larger than the human genome. For example, the human gut contains more than 1 billion bacterium which have encoded more than 3 million genes [7]. Most of the species in the human body are concentrated in the intestinal tract, oral cavity, genital tract, and skin surface. However, the microbial communities in different environments are varying [8]. It is of great significance to understand the composition and function of the microbial community for different diseases or physiological states, which will greatly benefit the disease diagnosis and treatment [9].

However, the microbiome is complex, high diversity, and dependent. It is estimated there are more than 1000 kinds of bacteria in the human gut [10]. With the help of next-generation sequencing (NGS) technology, we could retrieve all genetic information in the sample [11]. NGS technology provides us with opportunities to understand the composition, function, and dynamic evolution of microbial communities, but it is often limited by metagenomic data analysis methods because the metagenomic data is too large and complex to be studied through visual means such as correlation analysis [12]. As a result, these methods need to guide new biological hypothesis or discovery from the massive data. To study the microbiome, researchers start to utilize machine learning methods to mine the relationships between microbes and their hosts. It is believed machine learning could learn and discover patterns from the data. However, the performance for each algorithm is usually dependent on feature engineering which is a process of using domain knowledge to extract features from the raw data. When performed manually, the process of feature engineering for machine learning can be prone to error. Besides, manual feature engineering is problem-specific—the algorithm cannot be applied to solve other issues. Another shortcoming of manual feature engineering is that the understanding of the problem limits it before thinking up so many features [13]. With automated feature engineering, none of these obstacles exist. Deep learning is a sub-discipline of machine learning and it automatically learns an end-to-end model from the data, eliminating the need for manual feature design. Therefore, deep learning could discover highly complex relevant features to improve prediction [14].

Deep neural networks (DNNs) are applied to conduct the microbial data analysis, but there are few surveys of deep learning in the microbiome rather than bioinformatics. In this survey, we will review related work in data analysis in microbiome studies, especially deep learning. Our survey will include four parts: first, we will give a brief introduction to the microbiome research background and its relationship with human health. Also, we will explain why it is time to apply machine learning and deep learning for microbial data analysis; second, we will review the research of machine learning in microbial data analysis; after that, we will discuss deep learning in microbial studies; lastly, we will explain some challenges of deep learning. The survey gives a snapshot of the field at present and it is naturally somewhat biased towards the authors' view, even though we hope that it provides useful information to the reader.

2. MACHINE LEARNING IN MICROBIOME

It is possible to understand better the hierarchical structure and composition of the microbial community via classifying microbial samples. The classification of microbial samples refers to identify microbial samples from different phenotypes in the environment [15]. One of the main goals of the microbial study is to explore the relative abundance of microbes, find the association between microbes and diseases, and analyze the different states of the disease to lay the foundation for further application in the follow-up treatment [9] or forensic identification [16].

The machine learning methods are used to conduct microbiome data analysis, including two steps: data preprocessing (such as feature selection) and model prediction (such as supervised learning). The general workflow of machine learning methods on microbiome data analysis includes (Figure 1): first of all, sequencing data needs to be collected from bacterial communities associated with various environments or hosts. What's more, these sequences can be directly used as input to a machine learning model, or they can be preprocessed. The preprocessing step usually improves the prediction results of the model. For example, it will be easier to construct a better prediction model if the model can learn the hierarchical relationship of the microbiome in advance. Finally, it may also improve the prediction of machine learning algorithms for the microbial associated disease after embedding structural information, such as the phylogenetic relationship among pedigrees and the average nucleotide similarity network between sequences.

Operational taxonomic units (OTUs) are the most common data representation for marker genes (16S rRNA genes) sequencing [17]. Knights et al. [15] benchmarked a classification task algorithm and found that the random forest (RF) achieved the best performance. Although the classification error of the elastic net (ENet) classifier was higher than that of RF, it was still helpful for feature selection as the preprocessing step of other classifiers. They suggested using a machine learning method to analyze the human body related to microbial composition and summarized the overall analysis process. Host-related microbial community composition was specific and closely related to diseases. Statnikov et al. [18] systematically compared 18 major machine learning methods in the classification task. They found the most efficient machine learning methods for accurately classifying sample data were SVM, Kernel Ridge Regression, and Laplace prior Bayesian Logistic Regression.

Machine learning could also be applied to detect microbial community composition and functional correlation analysis. Yazdani et al. [19] focused on the functional characteristics of the microbiome of inflammatory bowel disease to determine how microorganisms played a role in health and diseases. They developed and trained a two-step classifier to identify major changes in intestinal microbiome abundance between healthy and inflammatory bowel disease populations via the Kolmogorov–Smirnov (KS) test and RS. To determine the relationship between gut microbiota composition and clinical features of irritable bowel syndrome, Tap et al. [20] analyzed samples using L1 regularized logistic regression and found 90 OTUs that could be used to measure the severity of irritable bowel syndrome. Pasolli et al. [21] developed a machine learning analysis framework to assess the association between microbiome and phenotypes. They recommended using a RF approach based on species' abundance to predict diseases.

3. DEEP LEARNING IN MICROBIOME

Deep learning has achieved amazing success in areas such as image recognition, text processing, and automatic translation. Therefore, more and more researchers are trying to apply deep learning techniques to biomedical data analysis [22,23,24,25]. Compared with traditional machine learning, the advantage of deep learning lies in its end-to-end learning ability, which can automatically discover multiple representations to achieve better prediction. Among these deep learning architectures, low-level features (e.g., patterns in DNA sequence motifs or pathological images), high-level features (e.g., damaged mRNA splicing or asymmetric skin lesions), and output (e.g., cancer detection), all of these features can be learned from the data to reduce or eliminate the need for manual feature engineering [26].

Currently, with the development and application of high-throughput technology, a large number of microbiome data are emerging [27,28]. As a result, the deep learning method for metagenomics has attracted more and more attention. This section will focus on the following three applications of deep learning in microbial data analysis: metagenomic classification, metagenomic gene prediction, and microbiome-wide association studies (MWAS).

3.1. Metagenomic Classification

To characterize the diversity of the microbial community is one of the main objectives for metagenomic research. The classification and analysis of microbial sequencing reads are known as taxonomy, in which sequence reads are classified or clustered to specific bins [29,30,31]. Abe et al. [32] proposed an unsupervised neural network algorithm names self-organizing maps (SOMs) for de novo genome binning. SOM was used to analyze the dinucleotide, trinucleotide, and tetranucleotide frequencies in various prokaryotic and eukaryotic genomes. Essinger et al. [33] applied the adaptive resonance theory (ART) to cluster similar genome fragments and showed ART achieved better performance than k-means. Interestingly, methods based on interpolating the Markov model were better than these early genome binning techniques [34]. Liang et al. [35] reported a bidirectional long short-term memory (LSTM) with the self-attention mechanism named DeepMicrobes to conduct taxonomic classification for metagenomics, which could precisely identify species from microbial community sequencing data. In addition, Rojas-Carulla et al. [36] introduced a convolutional neural network (CNN) model for the shotgun metagenomic classification named GeNet. GeNet was trained from raw DNA sequences and exploited a hierarchical taxonomy between organisms via a novel architecture. However, neural networks were less frequently used for reference-based taxonomic classification because the training was time-consuming. TAC-ELM [37] was the first neural network-based method to classify massive amounts of metagenomic data, which introduced a new sequence composition-based taxonomic classifier via extreme learning machines. Fiannaca et al. [38] proposed a 16S short-read sequences based on k-means representation and deep learning architecture, in which a classification model was generated for each taxon (from phyla to genera).

It is believed that viruses play an important role in the microbial community [39]. However, the viruses are difficult to classify because they have not marker genes. It is essential to detect viral signals from the mixed metagenomic sequences. Fang et al. [40] presented a 3-class classifier named PPR-Meta, a CNN architecture, to identify both phage and plasmid fragments from metagenomic assemblies. Also, Ren et al. [41] developed a CNN model named DeepVirFinder to identify viral sequences in metagenomics, while VirNet used ‘deep attention’ [42], a technique commonly used for natural language processing. In addition, Tampuu et al. [43] proposed a CNN-based method named ViraMiner to detect viral contigs in diverse human biospecimens. ViraMiner was composed of the global max-pooling (pattern branch) and global average-pooling (frequency branch) after the convolutional layer. Kristopher et al. [44] introduced a hybrid approach to mining viral genomes named VIBRANT which uses neural networks of protein signatures from nonreference-based similarity searches with hidden Markov models (HMMs) as well as a v-score metric to maximize identification of diverse and novel viruses.

3.2. Metagenomic Gene Prediction

The NGS techniques used in metagenomics have generated many thousands of short reads. Gene recognition is a necessary step to fully understand the functions, activities, and effects of genes in cellular processes. Accurate identification of genes in metagenomic fragments is one of the fundamental problems for metagenomics [45]. In many situations, metagenomic reads are from thousands of highly heterogeneous species. Besides, high sequence coverage for a single species is often unavailable. It is difficult to assemble short reads into long overlapped contigs. One way is to bypass the assembly and find the genes directly from these short reads. Zhang et al. [46] proposed a deep stack network learning model named Meta-MFDL to predict metagenomic genes by fusing multiple characteristics of short reads, such as monoamine acid usage, ORF length coverage, and Z-curve features.

Meanwhile, bacterial antimicrobial resistance is usually genetically encoded and it is urgent to identify resistance genes in metagenomic samples [47]. Arango-argoty et al. [48] proposed DeepARG-SS and DeepARG-LS to determine the potential resistance gene, gene exchange hotspot, and diffusion pathway of a new antibiotic. Both DeepARG-SS and DeepARG-LS were constructed for short-read sequences and full gene length sequences, respectively. Due to the emergence of antibiotic-resistant bacteria, there is an alarming requirement to discover new antibiotics. Recently, Jonathan et al. [49] trained a DNN to predict antibiotics through the discovery of structurally distinct antibacterial molecules.

3.3. Microbiome-Wide Association Studies

MWAS are to predict the relationship between the microbiome and the disease state. MWAS are similar to genome-wide association studies. They aim to identify microbial species, genes, or metabolites associated with the disease phenotype [9,50,51], which often involve feature selection and classification.

In the metagenomic sample classification task, Ditzler et al. [52] tried two deep learning methods: deep belief network and recursive neural network, to determine whether the methods of DNNs were suitable for metagenomic analysis. They found traditional machine learning methods were powerful classifiers when the data was limited. Also, Nguyen et al. [53] proposed a method named MET2IMG to predict the disease state. To make it easy for CNNs, MET2IMG mainly utilized “filling” and t-SNE embedding methods to construct “synthetic images” from the metagenomic data. Since related organisms tend to have similar characteristics, the phylogeny is helpful to classify and infer ecological function, as well as a tool for organizing and understanding the microbial world [54]. Therefore, phylogenetic knowledge could improve the model's performance. Reiman et al. [55] introduced PopPhy-CNN to predict host phenotypes via metagenomic samples by embedding phylogenetic knowledge. Fioravanti et al. [56] introduced a new deep learning architecture, Ph-CNN, based on the CNN to conduct classification metagenomic samples. The ancestor distance defined on the phylogenetic tree and the sparse version of multi-dimensional scaling of Ph-CNN were used to embed the phylogenetic tree into the Euclidean space. Zhu et al. [57] proposed a deep forest that kept the spatial structure between nodes by embedding the phylogenetic tree to conduct the classification. Nathan et al. [58] conducted a comparison of methods, including tree-based (such as gcForest) and CNN-based (such as PopPhy-CNN) on microbial abundance features in different datasets.

Feature selection for MWAS is to discover the meaningful microbial biomarkers to guide the noninvasive diagnosis [59]. Many feature selection methods have been proposed for the microarray gene expression and mass spectrometry-based proteomics data to identify disease-associated genes or proteins [60]. When algorithms are applied to these high-dimensional data, a critical problem is known as the curse of dimensionality. Dimensionality reduction is one of the powerful ways to address the issue [61]. Autoencoder is a deep learning approach to learn latent representation to achieve this purpose [62]. However, autoencoder is considered as a feature extraction process. To deal with feature selection via deep learning, Zhu et al. introduced an ensemble feature selection method based on Deep Forest to conduct MWAS [57]. Compared to DNNs, Deep Forest is composed of decision trees and each decision tree could guide feature selection [63]. Meanwhile, Zhu et al. proposed a graph embedding approach to identify meaningful microbial biomarkers through deep feedforward neural network [64].

4. PROBLEMS OF DEEP LEARNING FOR MICROBIOME

The biological data is complex and hierarchical, it is not easy to get valuable information with simple analysis tools. Compared with traditional machine learning methods, deep learning has advantages on pattern discovering automatically. However, there are still many challenges for deep learning to conduct microbial data analysis [65].

4.1. Black Box

In the last decade, the application of DNNs to long-standing problems has brought a breakthrough in performance and prediction power [66]. However, high accuracy, deriving from the increased model complexity, often comes at the price of loss of interpretability, i.e., many deep models behave as black-boxes and fail to provide explanations on their predictions. While in specific application fields, this issue may play a secondary role in high-risk domains, e.g., health care or self-driving cars, it is crucial to building trust in a model and being able to understand its behavior. We are unlikely to trust a prediction if we do not understand how it was made. It is believed there are two major reasons why the interpretable model is vital for bioinformatics, not only microbial data analysis. First, understanding how predictions are made is essential to identify mistakes or biases in the input data when the model is trained. Second, deep learning could learn novel patterns from the massive biological data, which will guide meaningful insights if the model can be interpreted [67].

However, What makes it even worse is that there is no unified and standard definition for the interpretability of deep learning [68]. Techniques of interpretability have been adopted to a wide range of problems, and various meanings, such as understanding, interpreting, or explaining. For example, Grégoire et al. [69] gave an excellent review of methods to interpret and understand DNNs. In their survey, the authors focused on the post-hoc interpretability to understand what the model predicts (e.g., categories) in terms of what is readily interpretable (e.g., the input features) based on a trained model.

4.2. A Large Amount of Training Data

A large number of data is required to train DNNs. However, due to privacy concerns and high-cost issues (e.g., shotgun sequencing), it is impractical to obtain a large amount of biological data. Machine learning methods are superior to DNNs, such as XGBoost [70] when the data is limited. One of the main challenges to train DNNs without enough data is the risk of overfitting: when the training error is low while the test error is large, the models' generalization ability will become poorly. Fortunately, there are some approaches to alleviate the overfitting problem, such as Dropout [71]. Dropout will randomly remove neurons and their connections, which will reduce the capacity or thinning of the network during training. However, the overfitting problem is still one of the threats for the small biological or microbial data sets.

Furthermore, DNN requires a lot of computing resources during training, which is often computationally intensive and time-consuming and usually requires graphics processing units (GPUs) to process.

4.3. Model Selection and Hyper-Parameters Tuning

At present, there are many types of DNNs and the practitioners and researchers propose a growing number of new models. However, each model is varying in different scenarios, and it isn't easy and direct to choose a deep learning architecture for the specific task. Also, many hyper-parameters such as regularization degree, learning rate, number of neurons, etc., are required to tune and debug for the model to achieve optimal results. Therefore, efficiently choosing a suitable network architecture and fine-tune its hyper-parameters for a specific dataset is a time-consuming task given the staggering number of possible alternatives.

Automated machine learning (AutoML) is proposed, which is devoted to developing algorithms and solutions to enable people with limited machine learning background knowledge to use machine learning models easily [72]. In addition, many software libraries make the implementation of deep learning models easier. TensorFlow [73], Keras [74], CNTK [75], and Pytorch [76] are some examples of such libraries. Despite the availability of such libraries and tools, the tasks of picking the right neural network model and its hyper-parameters are usually complex and iterative. As a result, Steven et al. [77] proposed a method named multi-node evolutionary neural networks for deep learning (MENNDL), which was used to automate network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms. Besides, automatic model selection (AMS) is a flexible and scalable method to automate the process of selecting artificial neural network models [78].

5. CONCLUSIONS

There are two basic questions to answer for the microbial studies, who's there and what they are doing. Many studies use the 16S rRNA gene as a taxonomic marker, then develop predictive models that can classify samples of disease states or habitats correctly. Compared with 16S rRNA gene sequencing, the shotgun metagenomics offers increased resolution, enabling a more specific taxonomic and functional classification of sequences as well as the discovery of new bacterial genes and genomes. Therefore, deep learning is preferred for the metagenomic data analysis, such as the classification or contig binning. But most of the current researches are focusing on different deep learning architectures(e.g., CNN or LSTM) and there is less work on deep transfer or reinforcement learning for the microbial studies.

This paper discussed deep learning for microbial data analysis, including metagenomic classification, metagenomic gene prediction. In particular, this survey introduced the application of deep learning in MWAS and further suggested a deep learning method based on the phylogenetic tree, which could improve the classification performance. We also analyzed the problems of deep learning in biomedical data analysis, including the model's interpretability, the need for a large amount of data and hyper-parameters tuning.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

AUTHORS' CONTRIBUTIONS

QZ, BH and XJ conceived the concept of the work. QZ and BL performed literature search. QZ, BH and HS wrote the paper. All authors have approved the final manuscript.

ACKNOWLEDGMENTS

This research is supported by the National Key Research and Development Program of China (2017YFC0909502) and the National Natural Science Foundation of China (No. 61532008 and 61872157).

REFERENCES

1.P.J. Turnbaugh, R.E. Ley, M. Hamady, C.M. Fraser-Liggett, R. Knight, and J.I. Gordon, The human microbiome project, Nature, Vol. 449, 2007, pp. 804-810.

2.J.C. Clemente, L.K. Ursell, L.W. Parfrey, and R. Knight, The impact of the gut microbiota on human health: an integrative view, Cell, Vol. 148, 2012, pp. 1258-1270.

3.S.M. Collins, M. Surette, and P. Bercik, The interplay between the intestinal microbiota and the brain, Nat. Rev. Microbiol., Vol. 10, 2012, pp. 735-742.

4.I.B. Jeffery, P.W. O’toole, L. Öhman, M.J. Claesson, J. Deane, E.M.M. Quigley, and M. Simrén, An irritable bowel syndrome subtype defined by species-specific alterations in faecal microbiota, Gut, Vol. 61, 2012, pp. 997-1006.

5.T. Yang, E.M. Richards, C.J. Pepine, and M.K. Raizada, The gut microbiota and the brain–gut–kidney axis in hypertension and chronic kidney disease, Nat. Rev. Nephrol., Vol. 14, 2018, pp. 442-456.

6.E.A. Grice and J.A. Segre, The human microbiome: our second genome, Ann. Rev. Genomics Hum. Genet., Vol. 13, 2012, pp. 151-170.

7.J. Qin, R. Li, J. Raes, T. Arumugam, et al., A human gut microbial gene catalogue established by metagenomic sequencing, Nature, Vol. 464, 2010, pp. 59-65.

8.C. Huttenhower, D. Gevers, R. Knight, et al., Structure, function and diversity of the healthy human microbiome, Nature, Vol. 486, 2012, pp. 207.

9.J.A. Gilbert, R.A. Quinn, J. Debelius, Z.Z. Xu, J. Morton, N. Garg, J.K. Jansson, P.C. Dorrestein, and R. Knight, Microbiome-wide association studies link dynamic microbial consortia to disease, Nature, Vol. 535, 2016, pp. 94-103.

10.E. Thursby and N. Juge, Introduction to the human gut microbiota, Biochem. J., Vol. 474, 2017, pp. 1823-1836.

11.S. Behjati and P.S. Tarpey, What is next generation sequencing?, Arch. Dis. Child. Educ. Pract., Vol. 98, 2013, pp. 236-238.

12.H. Teeling and F.O. Glöckner, Current opportunities and challenges in microbial metagenome analysis—a bioinformatic perspective, Brief. Bioinformatics, Vol. 13, 2012, pp. 728-742.

13.P. Domingos, A few useful things to know about machine learning, Commun. ACM, Vol. 55, 2012, pp. 78-87.

14.Y. LeCun, Y. Bengio, and G. Hintonm, Deep learning, Nature, Vol. 521, 2015, pp. 436-444.

15.D. Knights, E.K. Costello, and R. Knight, Supervised classification of human microbiota, FEMS Microbiol. Rev., Vol. 35, 2011, pp. 343-359.

16.T.H. Clarke, A. Gomez, H. Singh, K.E. Nelson, and L.M. Brinkac, Integrating the microbiome as a resource in the forensics toolkit, Forensic Sci. Int. Genetics, Vol. 30, 2017, pp. 141-147.

17.X. Hao, R. Jiang, and T. Chen, Clustering 16s rRNA for OTU prediction: a method of unsupervised Bayesian clustering, Bioinformatics, Vol. 27, 2011, pp. 611-618.

18.A. Statnikov, M. Henaff, V. Narendra, K. Konganti, Z. Li, L. Yang, Z. Pei, M.J. Blaser, C.F. Aliferis, and A.V. Alekseyenko, A comprehensive evaluation of multicategory classification methods for microbiomic data, Microbiome, Vol. 1, 2013, pp. 11.

19.M. Yazdani, B.C. Taylor, J.W. Debelius, W. Li, R. Knight, and L. Smarr, Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease, IEEE, in 2016 IEEE International Conference on Big Data (Big Data) (Washington, DC, USA), 2016, pp. 1272-1280.

20.J. Tap, M. Derrien, H. Törnblom, R. Brazeilles, S. Cools-Portier, J. Doré, S. Störsrud, B.L. Nevé, L. Öhman, and M. Simrén, Identification of an intestinal microbiota signature associated with severity of irritable bowel syndrome, Gastroenterology, Vol. 152, 2017, pp. 111-123.

21.E. Pasolli, D.T. Truong, F. Malik, L. Waldron, and N. Segata, Machine learning meta-analysis of large metagenomic datasets: tools and biological insights, PLoS Comput. Biol., Vol. 12, 2016, pp. e1004977.

22.P. Mamoshina, A. Vieira, E. Putin, and A. Zhavoronkov, Applications of deep learning in biomedicine, Mol. Pharm., Vol. 13, 2016, pp. 1445-1454.

23.C. Angermueller, T. Pärnamaa, L. Parts, and O. Stegle, Deep learning for computational biology, Mol. Syst. Biol., Vol. 12, 2016, pp. 878.

24.M. Wainberg, D. Merico, A. Delong, and B.J. Frey, Deep learning in biomedicine, Nat. Biotechnol., Vol. 36, 2018, pp. 829-838.

25.D.M. Camacho, K.M. Collins, R.K. Powers, J.C. Costello, and J.J. Collins, Next-generation machine learning for biological networks, Cell, Vol. 173, 2018, pp. 1581-1592.

26.G. Eraslan, Ž. Avsec, J. Gagneur, and F.J. Theis, Deep learning: new computational modelling techniques for genomics, Nat. Rev. Genetics, Vol. 20, 2019, pp. 389-403.

27.J.A. Gilbert, J.K. Jansson, and R. Knight, The earth microbiome project: successes and aspirations, BMC Biol., Vol. 12, 2014, pp. 69.

28.J.A. Navas-Molina, E.R. Hyde, J.G. Sanders, and R. Knight, The microbiome and big data, Curr. Opin. Syst. Biol., Vol. 4, 2017, pp. 92-96.

29.S.S. Mande, M.H. Mohammed, and T.S. Ghosh, Classification of metagenomic sequences: methods and challenges, Brief. Bioinfor., Vol. 13, 2012, pp. 669-681.

30.J. Jovel, J. Patterson, W. Wang, et al., Characterization of the gut microbiome using 16s or shotgun metagenomics, Front. Microbiol., Vol. 7, 2016, pp. 459.

31.C. Quince, A.W. Walker, J.T. Simpson, N.J. Loman, and N. Segata, Shotgun metagenomics, from sampling to analysis, Nat. Biotechnol., Vol. 35, 2017, pp. 833-844.

32.T. Abe, S. Kanaya, M. Kinouchi, Y. Ichiba, T. Kozuki, and T. Ikemura, Informatics for unveiling hidden genome signatures, Genome Res., Vol. 13, 2003, pp. 693-702.

33.S.D. Essinger, R. Polikar, and G.L. Rosen, Neural network-based taxonomic clustering for metagenomics, IEEE, in The 2010 International Joint Conference on Neural Networks (IJCNN) (Barcelona, Spain), 2010, pp. 1-7.

34.D.R. Kelley and S.L. Salzberg, Clustering metagenomic sequences with interpolated markov models, BMC Bioinform., Vol. 11, 2010, pp. 544-544.

35.Q. Liang, P.W. Bible, Y. Liu, B. Zou, and L. Wei, Deepmicrobes: taxonomic classification for metagenomics with deep learning, NAR Genomics Bioinform., Vol. 2, 2020, pp. lqaa009.

36.M. Rojas-Carulla, I.O. Tolstikhin, G. Luque, N. Youngblut, R. Ley, and B. Schölkopf, Genet: deep representations for metagenomics, 2019, pp. 537795. arXiv preprint arXiv:1901.11015

37.Z. Rasheed and H. Rangwala, Metagenomic taxonomic classification using extreme learning machines, J. Bioinform. Comput. Biol., Vol. 10, 2012, pp. 1250015.

38.A. Fiannaca, L.L. Paglia, M.L. Rosa, G.L. Bosco, G. Renda, R. Rizzo, S. Gaglio, and A. Urso, Deep learning models for bacteria taxonomic classification of metagenomic data, BMC Bioinform., Vol. 19, 2018, pp. 61-76.

39.K. Cadwell, The virome in host health and disease, Immunity, Vol. 42, 2015, pp. 805-813.

40.Z. Fang, J. Tan, S. Wu, M. Li, C. Xu, Z. Xie, and H. Zhu, Ppr-meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning, GigaScience, Vol. 8, 2019, pp. giz066.

41.J. Ren, K. Song, C. Deng, N.A. Ahlgren, J.A. Fuhrman, Y. Li, X. Xie, R. Poplin, and F. Sun, Identifying viruses from metagenomic data using deep learning, Quant. Biol., Vol. 8, 2020, pp. 64-77.

42.A.O. Abdelkareem, M.I. Khalil, M. Elaraby, H. Abbas, and A.H.A. Elbehery, Virnet: deep attention model for viral reads identification, in 2018 13th International Conference on Computer Engineering and Systems (ICCES) (Cairo, Egypt), 2018.

43.A. Tampuu, Z. Bzhalava, J. Dillner, and R. Vicente, Viraminer: deep learning on raw DNA sequences for identifying viral genomes in human samples, PLoS One, Vol. 14, 2019, pp. e0222271.

44.K. Kieft, Z. Zhou, and K. Anantharaman, Vibrant: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences, Microbiome, Vol. 8, 2020, pp. 1-23.

45.H. Noguchi, J. Park, and T. Takagi, Metagene: prokaryotic gene finding from environmental genome shotgun sequences, Nucleic Acids Res., Vol. 34, 2006, pp. 5623-5630.

46.S.W. Zhang, X.-Y. Jin, and T. Zhang, Gene prediction in metagenomic fragments with deep learning, BioMed Res. Int., Vol. 2017, 2017, pp. 4740354.

47.M. Boolchandani, A.W. D’Souza, and G. Dantas, Sequencing-based methods and resources to study antimicrobial resistance, Nat. Rev. Genetics, Vol. 20, 2019, pp. 356-370.

48.G. Arango-Argoty, E. Garner, A. Pruden, L.S. Heath, P.J. Vikesland, and L. Zhang, Deeparg: a deep learning approach for predicting antibiotic resistance genes from metagenomic data, Microbiome, Vol. 6, 2018, pp. 23-23.

49.J.M. Stokes, K. Yang, K. Swanson, et al., A deep learning approach to antibiotic discovery, Cell, Vol. 180, 2020, pp. 475-483.

50.J. Wang and H. Jia, Metagenome-wide association studies: fine-mining the microbiome, Nat. Rev. Microbiol., Vol. 14, 2016, pp. 508-522.

51.R.A. Power, P. Parkhill, and T. de Oliveira, Microbial genome-wide association studies: lessons from human gwas, Nat. Rev. Genetics, Vol. 18, 2017, pp. 41-50.

52.G. Ditzler, R. Polikar, and G. Rosen, Multi-layer and recursive neural networks for metagenomic classification, IEEE Trans. Nanobiosci., Vol. 14, 2015, pp. 608-616.

53.T.H. Nguyen, E. Prifti, Y. Chevaleyre, N. Sokolovska, and J.-D. Zucker, Disease classification in metagenomics with 2d embeddings and deep learning, in La Conférence sur l’Apprentissage automatique (CAp), CoRR, 2018. http://arxiv.org/abs/1806.09046

54.A.D. Washburne, J.T. Morton, J. Sanders, D. McDonald, Q. Zhu, A.M. Oliverio, and R. Knight, Methods for phylogenetic analysis of microbiome data, Nat. Microbiol., Vol. 3, 2018, pp. 652-661.

55.D. Reiman, A. Metwally, J. Sun, and Y. Dai, Popphy-cnn: A phylogenetic tree embedded architecture for convolutional neural networks to predict host phenotype from metagenomic data, IEEE J. Biomed. Health Inform., Vol. 24, 2020, pp. 2993-3001.

56.D. Fioravanti, Y. Giarratano, V. Maggio, C. Agostinelli, M. Chierici, G. Jurman, and C. Furlanello, Phylogenetic convolutional neural networks in metagenomics, BMC Bioinform., Vol. 19, 2018, pp. 49-49.

57.Q. Zhu, Q. Zhu, M. Pan, X. Jiang, X. Hu, and T. He, The phylogenetic tree based deep forest for metagenomic data classification, in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Madrid, Spain), 2018, pp. 279-282.

58.N. LaPierre, J.-T. Chelsea, G. Zhou, and W. Wang, Metapheno: a critical evaluation of deep learning and machine learning in metagenome-based disease prediction, Methods, Vol. 166, 2019, pp. 74-82.

59.J. Yu, Q. Feng, S.H. Wong, et al., Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer, Gut, Vol. 66, 2017, pp. 70-78.

60.Y. Saeys, I. Inza, and P. Larrañaga, A review of feature selection techniques in bioinformatics, Bioinformatics, Vol. 23, 2007, pp. 2507-2517.

61.L. van der Maaten, E. Postma, and J. van den Herik, Dimensionality reduction: a comparative review, J. Mach. Learn Res., Vol. 10, 2009, pp. 13.

62.G.E. Hinton and R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, Vol. 313, 2006, pp. 504-507.

63.Z. Qiang, B. Li, T. He, G. Li, and J. Xingpeng, Robust biomarker discovery for microbiome-wide association studies, Methods, Vol. 173, 2020, pp. 144-151.

64.Q. Zhu, X. Jiang, Q. Zhu, M. Pan, and T. He, Graph embedding deep learning guides microbial biomarkers’ identification, Front. Genetics, Vol. 10, 2019, pp. 1182.

65.T. Ching, D.S. Himmelstein, B.K. Beaulieu-Jones, et al., Opportunities and obstacles for deep learning in biology and medicine, J. Royal Soc. Interface, Vol. 15, 2018, pp. 20170387.

66.I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, MIT press, 2016.

67.C.B. Azodi, J. Tang, and S.-H. Shiu, Opening the black box: interpretable machine learning for geneticists, Trends Genetics, Vol. 36, 2020, pp. 442-455.

68.W. James Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, Definitions, methods, and applications in interpretable machine learning, Proc. Natl. Acad. Sci., Vol. 116, 2019, pp. 22071-22080.

69.G. Montavon, W. Samek, and K.-R. Müller, Methods for interpreting and understanding deep neural networks, Digital Signal Process, Vol. 73, 2018, pp. 1-15.

70.T. Chen and C. Guestrin, Xgboost: a scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, CA, USA), 2016, pp. 785-794.

71.N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, J. Machine Learn. Res., Vol. 15, 2014, pp. 1929-1958.

72.M. Feurer, A. Klein, K. Eggensperger, et al., Efficient and robust automated machine learning, in NIPS’15 Proceedings of the 28th International Conference on Neural Information Processing Systems, Vol. 2, 2015, pp. 2755-2763.

73.M. Abadi, P. Barham, J. Chen, et al., Tensorflow: a system for large-scale machine learning, USENIX Association, in OSDI’16 Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, 2016, pp. 265-283.

74.F. Chollet, Keras: the python deep learning library, ASCL, 2018.

75.F. Seide and A. Agarwal, CNTK: Microsoft’s open-source deep-learning toolkit, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, CA, USA), 2016, pp. 2135-2135.

76.B. Steiner, Z. DeVito, S. Chintala, et al., Pytorch: an imperative style, high-performance deep learning library, H. Wallach, H. Larochelle, A. Beygelzimer, F. Alché-Buc, E. Fox, and R. Garnett (editors), Advances in Neural Information Processing Systems, Curran Associates, Inc, Vol. 32, 2019, pp. 8026-8037.

77.S.R. Young, D.C. Rose, T.P. Karnowski, S.-H. Lim, and R.M. Patton, Optimizing deep learning hyper-parameters through an evolutionary algorithm, in Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments (MLHPC ’15) (Austin, TX, USA), 2015.

78.D. Laredo, Y. Qin, O. Schütze, and J.-Q. Sun, Automatic model selection for neural networks, 2019. arXiv preprint arXiv:190506010

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Journal: Journal of Artificial Intelligence for Medical Sciences
Volume-Issue: 1 - 1-2
Pages: 23 - 29
Publication Date: 2020/11/04
ISSN (Online): 2666-1470
DOI: 10.2991/jaims.d.201028.001 How to use a DOI?
Open Access: This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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ris enw bib

TY  - JOUR
AU  - Qiang Zhu
AU  - Ban Huo
AU  - Han Sun
AU  - Bojing Li
AU  - Xingpeng Jiang
PY  - 2020
DA  - 2020/11/04
TI  - Application of Deep Learning in Microbiome
JO  - Journal of Artificial Intelligence for Medical Sciences
SP  - 23
EP  - 29
VL  - 1
IS  - 1-2
SN  - 2666-1470
UR  - https://doi.org/10.2991/jaims.d.201028.001
DO  - 10.2991/jaims.d.201028.001
ID  - Zhu2020
ER  -

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