Our study results suggest that HCWs in an acute hospital care setting are at no higher risk of influenza than the general public, or that if they are, the increased risk is modest. Household contacts, particularly children, play an important role with individuals with three or more children being at highest risk. Use of public transport does not increase the risk of influenza, whereas (in the absence of household children) car ownership does seem to significantly increase the risk for influenza infection, although the mechanism is unclear. Further research would help to clarify the role of household contacts of different age groups, the relevance of car ownership, and whether subgroups of hospital HCWsor HCWs in other settings, such as in primary care,are at increased risk for influenza infection. Finally, the ILI-syndrome is a poor marker for influenza infection, suggesting that HCWs cannot rely on this syndrome if they wish to confidently protect their patients but, should instead be vaccinated. Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features BACKGROUND: Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. METHODS/PRINCIPAL FINDINGS: To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. CONCLUSION/SIGNIFICANCE: Our results indicate that the network prediction system thus established is quite promising and encouraging. Identification of drug-target interaction networks is an essential step in the drug discovery pipeline [1] . The emergence of molecular medicine and the completion of the human genome project provide more opportunity to discover unknown target proteins of drugs. Many efforts have been made to discover new drugs in the past few years. However, the number of new drug approvals remains quite low (around only 30 per year). This is partially because many compounds or drug candidates have to be withdrawn owing to unacceptable toxicity. Such failures have wasted a lot of money. It would be beneficial to develop computational methods for predicting the sensitivity and toxicity before a drug candidate was synthesized [2, 3, 4] . However, a number of problems need to be overcome in order to find out the exact effects of a drug. Firstly, drugs could have numerous effects including positive and negative effects, and it is hard to find out and elucidate the possible effects; secondly, different people would have completely different responses to a drug even though the same gene products are only slightly different [5, 6, 7, 8] ; thirdly, it is very hard to trace the drug effects since the biological interaction pathways are extremely complicated in human beings. Therefore, it would be very helpful for drug development if the interactions between drugs and target proteins could be predicted more accurately and the underlying mechanisms could be better understood.