Skip to main content
School of Mathematical Sciences

Dr Anthony Baptista

Postdoctoral Research Assistant

Email: anthony.baptista@qmul.ac.uk
Room Number: MB-202, desk 10
Twitter: @AnthBaptista

Profile

Anthony Baptista is a Postdoctoral Research Assistant in the group Complex Systems & Networks at the Queen Mary University of London (QMUL) and the Alan Turing Institute. He is involved in the project "Learning from data with structured missingness" which is part of the Turing-Roche partnership. He will closely work with Prof. Dr. Ginestra Bianconi (QMUL) and Dr. Ruben Sanchez-Garcia (University of Southampton), and all the people engaged in the project at the Alan Turing Institute. 

During his Ph.D., he worked on network theory, especially on the development of new multilayer network exploration methods to study biological networks. The methods developed were closely related to the integrated exploration of large multidimensional datasets that remain a major challenge in many scientific fields. In this context, he developed a new mathematical framework based on random walk with restart algorithm which is currently used for exploring the whole topology of large-scale networks. He also applied this kind of method to several biological questions such as the prioritization of gene and drug candidates involved in different disorders, gene-disease association predictions, and the integration of 3D DNA conformation information with gene and disease networks. During his Ph.D., he was also interested in the extension of several other methods to multilayer networks. In particular, the generalization of the Katz similarity measure to multilayer networks. He also developed a new method of community detection. Finally, he studied network embedding, especially in the case of shallow embedding methods.

Before his Ph.D., he worked on the statistical physics of liquids. These works aimed to predict both theoretically with higher-order correlation functions and numerically with molecular dynamics simulation, the complex behavior of water and alcohol mixtures.

Teaching

  • Teacher assistant of the course: ”Mathematics tools for bioinformatics”, in Master of Science (bioinformatics), 2019-2022.
  • Teacher assistant of the course: ”Introduction to computer programming in Python”, in Bachelor of Honor (bioinformatics), 2019-2022.
  • Teacher assistant of the course: ”Biological interaction networks”, in Master of science (bioinformatics and computational biology), 2020-2022.
  •  Teacher assistant of the course: ”Introduction to computer programming in Python”, in Bachelor of science (biology), 2019-2020.

Research

Research Interests:

Network theory

  • Multilayer networks
  • Higher-order networks
  • Triadic interactions
  • Network embedding
  • Community detection
  • Information theory for networks
  • Network geometry and topology

Systems Biology

  • Biological networks
  • Genomic information (HiC, PC-HiC)
  • Comorbidity detection
  • Drug Repurposing
  • Multi-omics data

Statistical physics

  • Statistical Physics of liquids
  • Micro-heterogeneity
  • Phase transition

Publications

  1. A.Baptista and A.Baudot, Biological Applications of Universal Multilayer Networks, In preparation (2022)
  2. A.Baptista, A.Gonzalez, A.Baudot, Universal Multilayer Network Exploration by Random Walk with Restart, Commun. Phys. 5, 170 (2022)
  3. L.Almàsy, A.I.Kuklin, M.Pozar, A.Baptista, A.Perera, Microscopic origin of the scattering pre-peak in aqueous propylamine mixtures: X-Ray and Neutron experiments versus simulations, Phys. Chem. Chem. Phys, 21, 9317-9325, (2019)
  4.  A. Batista and A. Perera, Modeling micro-heterogeneity in mixtures: the role of many body correlations, J. Chem. Phys. 150, 064504, (2019)

  5. A.Baptista, R. Sanchez-Garcia, G.Bianconi A.Baudot, Zoo Guide for Network Embedding, In preparation

Supervision

  • Yorgo El Moubayed (Bioinformatics master student): ”Analysis of Hi-C and promoter capture Hi-C data”, April-June 2020
  • Loumi Trémas (Machine Learning master student): ”Prioritizing nodes by Katz based Model in Multilayer networks”, April-June 2021
Back to top