<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kuntal, B. K.</style></author><author><style face="normal" font="default" size="100%">Chandrakar, P.</style></author><author><style face="normal" font="default" size="100%">Sadhu, S.</style></author><author><style face="normal" font="default" size="100%">Mande, S. S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">NetShift: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets</style></title><secondary-title><style face="normal" font="default" size="100%">ISME Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">442-454</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The combined effect of mutual association within the co-inhabiting microbes in human body is known to play a major role in determining health status of individuals. The differential taxonomic abundance between healthy and disease are often used to identify microbial markers. However, in order to make a microbial community based inference, it is important not only to consider microbial abundances, but also to quantify the changes observed among inter microbial associations. In the present study, we introduce a method called ‘NetShift’ to quantify rewiring and community changes in microbial association networks between healthy and disease. Additionally, we devise a score to identify important microbial taxa which serve as ‘drivers’ from the healthy to disease. We demonstrate the validity of our score on a number of scenarios and apply our methodology on two real world metagenomic datasets. The ‘NetShift’ methodology is also implemented as a web-based application available at </style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">9.520</style></custom4></record></records></xml>