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A review on host–pathogen interactions: classification and prediction

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Abstract

The research on host–pathogen interactions is an ever-emerging and evolving field. Every other day a new pathogen gets discovered, along with comes the challenge of its prevention and cure. As the intelligent human always vies for prevention, which is better than cure, understanding the mechanisms of host–pathogen interactions gets prior importance. There are many mechanisms involved from the pathogen as well as the host sides while an interaction happens. It is a vis-a-vis fight of the counter genes and proteins from both sides. Who wins depends on whether a host gets an infection or not. Moreover, a higher level of complexity arises when the pathogens evolve and become resistant to a host’s defense mechanisms. Such pathogens pose serious challenges for treatment. The entire human population is in danger of such long-lasting persistent infections. Some of these infections even increase the rate of mortality. Hence there is an immediate emergency to understand how the pathogens interact with their host for successful invasion. It may lead to discovery of appropriate preventive measures, and the development of rational therapeutic measures and medication against such infections and diseases. This review, a state-of-the-art updated scenario of host–pathogen interaction research, has been done by keeping in mind this urgency. It covers the biological and computational aspects of host–pathogen interactions, classification of the methods by which the pathogens interact with their hosts, different machine learning techniques for prediction of host–pathogen interactions, and future scopes of this research field.

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Notes

  1. A procedure through which genotypes give rise to phenotypes during development due to changes in underlying DNA sequences, i.e., histone modifications, DNA methylation, DNA silencing via noncoding RNAs and chromatin remodeling proteins.

  2. Temporal alterations in host and viral proteins throughout the course of a productive infection

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Acknowledgments

LN acknowledges University Grants Commission, India, for a UGC Post-Doctoral Fellowship (No. F.15-1/2013-14/PDFWM-2013-14-GE-ORI-19068(SA-II)).

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  1. Machine Intelligence Unit, Indian Statistical Institute, 203, Barrackpore Trunk Road, Kolkata, 700108, India

    R. Sen, L. Nayak & R. K. De

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RS conceptualized the whole review. She prepared the initial manuscript. LN and RKD gave theoretical input and modified it. RS, LN, and RKD read and corrected the final manuscript.

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Sen, R., Nayak, L. & De, R.K. A review on host–pathogen interactions: classification and prediction. Eur J Clin Microbiol Infect Dis 35, 1581–1599 (2016). https://doi.org/10.1007/s10096-016-2716-7

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