Identification of infectious disease-associated host genes using machine learning techniques.
Ranjan Kumar BarmanAnirban MukhopadhyayUjjwal MaulikSantasabuj DasPublished in: BMC bioinformatics (2019)
To 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.
Keyphrases
- infectious diseases
- bioinformatics analysis
- machine learning
- genome wide
- genome wide identification
- signaling pathway
- deep learning
- small molecule
- climate change
- genome wide analysis
- transcription factor
- copy number
- squamous cell carcinoma
- artificial intelligence
- oxidative stress
- big data
- induced apoptosis
- childhood cancer