Department of Knowledge Technologies, Jožef Stefan Institute, Slovenia.
Jožef Stefan International Postgraduate School, Slovenia.
Bioinformatics. 2021 May 5;37(6):885-887. doi: 10.1093/bioinformatics/btaa762.
Causal biological interaction networks represent cellular regulatory pathways. Their fusion with other biological data enables insights into disease mechanisms and novel opportunities for drug discovery.
We developed Causal Network of Diseases (CaNDis), a web server for the exploration of a human causal interaction network, which we expanded with data on diseases and FDA-approved drugs, on the basis of which we constructed a disease-disease network in which the links represent the similarity between diseases. We show how CaNDis can be used to identify candidate genes with known and novel roles in disease co-occurrence and drug-drug interactions.
CaNDis is freely available to academic users at http://candis.ijs.si and http://candis.insilab.org.
Supplementary data are available at Bioinformatics online.
因果生物相互作用网络代表细胞调控途径。将其与其他生物数据融合，可深入了解疾病机制并为药物发现提供新的机会。
我们开发了因果疾病网络（CaNDis），这是一个用于探索人类因果相互作用网络的网络服务器，我们在该网络中扩展了疾病和 FDA 批准药物的数据，并在此基础上构建了疾病-疾病网络，其中的链接代表疾病之间的相似性。我们展示了如何使用 CaNDis 来识别已知和新的在疾病共发生和药物-药物相互作用中具有作用的候选基因。
CaNDis 可供学术用户免费使用，网址为 http://candis.ijs.si 和 http://candis.insilab.org。
补充数据可在 Bioinformatics 在线获取。
