Identification of infectious disease-associated host genes using machine learning techniques

Abstract

BackgroundWith the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets.ResultsWe developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases.ConclusionsTo the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

Bioinformatic analysis reveals an exosomal miRNA-mRNA network in colorectal cancer

“…Next, 5284 mRNAs were obtained by overlapping the target mRNAs and differentially expressed mRNAs, which could ensure to get the most potential mRNAs associated with both CRC progression and exosomes. Recently, machine learning approaches have gained popularity in medicine [ 34 , 35 ]. The prowess and flexibility of machine learning methods enable researchers to extract valuable information from increasing biomedical databases.…”

Incorporating Machine Learning into Established Bioinformatics Frameworks

“…Furthermore, recently developed algorithms allow ML-based visualization of disease relationships, for example, of disease-phenotype similarity and disease relationships with t-SNE [ 154 , 155 ]. In addition, ML has been integrated with PPI networks to infer a phenotype similarity score and rank protein complexes by phenotypes that are linked to human disease [ 156 ], to identify topological features of disease-associated proteins [ 157 ], and recently, to identify host genes that are associated with infectious diseases [ 158 ]. Furthermore, ML algorithms have been employed for the detection and investigation of cancer driver genes, by incorporation of ML with statistical scoring of genomic sequencing [ 159 ], pathway-level mutations [ 160 ], mutation and gene interaction data [ 161 ], and by application of deep convolutional neural networks and random forest for analysis of mutations and gene similarity networks [ 162 , 163 ].…”

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