BicNET is a powerful and flexible tool for the exploratory and unsupervised analysis of biological and social networks since it enables the discovery of non-trivial yet coherent modules.
BicNET integrates state-of-the-art contributions on pattern-based biclustering (including BicPAM, BicSPAM, BiC2PAM, BiP, DeBi and BiModule), and extends them with principles to effectively explore the inherent structural sparsity of network data. As such, BicNET is able to efficiently discover exhaustive and flexible structures of biclusters, with parameterizable coherency (constant, plaid, symmetric and order-preserving assumptions) and heightened robustness to noise.

Rui Henriques and Sara C. Madeira, 2015, BicNET: Efficient Biclustering of Biological Networks to Unravel Non-Trivial Modules, In Algorithms in Bioinformatics (WABI), 1-15, LNCS Series, vol.9289, Springer Berlin Heidelberg [10.1007/978-3-662-48221-6_1]

Online GUI: access the official webpage of BicNET to run your requests on our server.
Note: we detected on September 19 2016 a problem on the server and connectivity to the online interface may be temporarily down until the end of the month.

Programmatic GUI: download BicNET Source v2.0.0 (requirement: JDK v1.7 or superior)

For previous versions of the BicNET software please contact the authors.

BicNET software is made solely available for research and educational purposes. In this context, you can use the software under: 1) the terms of the GNU General Public License as published by the Free Software Foundation (either version 3 or any later version), and 2) its proper citation. BicNET is distributed in the hope that it will be useful, but WITHOUT any warranty (without even the implied warranty of merchantability or fitness for a particular purpose).

If you wish to use it for other purposes, you must contact the authors for permission.

The BicNET team appreciates your suggestions and the reporting of any problem that you may be able to detect.
For this purpose, clarifications or any other questions related with BicNET please email Rui Henriques: rmch [at] tecnico [dot] ulisboa [dot] pt

BicNET Team:
Rui Henriques (responsible) is finishing his PhD in the field of learning from high-dimensional and structured data at IST, University of Lisbon and INESC-ID. He had received distinctions for his academic achievements by IST between 2006 and 2008, and a national award for his merits by Caixa Geral de Depositos in 2009. He has also been a business analyst at McKinsey with wide exposure to real-life projects. [collapse] ... [expand]
Sara C. Madeira (supervision) received a (5-year) BSc degree in computer science from the University of Beira Interior and the MSc and PhD degree in computer science and engineering at IST, University of Lisbon in 2002 and 2008. She is currently an Assistant Professor, at the CSE department at IST, and a senior researcher at Inesc-ID. Her research interests include algorithms and data structures, data mining, machine learning, bioinformatics and medical informatics. [collapse]
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