MSc Thesis

https://mscthesis.herokuapp.com/

Community Finding with Applications on Phylogenetic Networks

The aim of the thesis was to develop an improved version of PHYLOViZ Online by implementing community finding (CF) algorithms, as well as, adding new tools for data visualization. Similar algorithms are being used in other domains. In phylogenetics, it is a step forward for handling infectious diseases.

This tool is intended to assist distinct health professionals, including doctors and bioinformaticians, and is designed to enable medical and research purposes.

Implemented Algorithms

• Louvain;
• Infomap;
• Layered Label Propagation (LLP);
• Girvan-Newman (GN) Benchmark Network Generator;
• Normalized Mutual Information (NMI);
• Hamming Distance.

Benchmark & Testing

Synthetic Networks

• Girvan-Newman;
• Lancichinetti-Fortunato-Radicchi (LFR).

Real Networks

• Amazon;
• Zachary's Karate Club;
• Staphylococcus aureus.

Parameters

Accuracy

Congruence of each partition inferred by Louvain, Infomap and LLP was determined using NMI.

Speed

Time required to run Louvain, Infomap, LLP and GN Benchmark Network Generator, in GN and LFR networks, was measured.

Visualization Interface

• Cytoscape.js;
• D3.js (Version 4 & SVG);
• D3.js (Version 4 & Canvas).

Phyl

Web application which integrates all the previous components. Image available in Docker Hub.

User Guide

1. Insert text file (.txt) with data in this format and "Input.txt" name. Or, just use a standard network included in the app;
2. Choose visualization interface;
3. Choose CF algorithm;
4. Run;
5. Generated networks are draggable, zoomable and pannable.

Supervision Team

Bruno Gonçalves (iMM) | Alexandre Francisco (INESC-ID & IST) | João Carriço (iMM & IST) | Vítor Borges (INSA)

Roadmap -> Wiki