Dr. Manuel Gil

Netzwerk

Mitglied in Netzwerken

Swiss Institute of Bioinformatics

Projekte

• Inferring the molecular evolutionary history with multiple-character indels: from theory to practice / Co-Projektleiter:in / laufend
• Novel Markov chain Monte Carlo algorithms for Bayesian inference / Teammitglied / abgeschlossen
• Computational literature-based natural product drug discovery / Projektleiter:in / abgeschlossen
• Frequentist estimation of the evolutionary history of sequences with substitutions and indels / Co-Projektleiter:in / abgeschlossen
• Radiosands / Teammitglied / abgeschlossen
• Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data / Teammitglied / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• Seppey, M. et al. (2025) 'Impact of phylogenetic method choice on Iindel analyses in HIV-1 subtype B', Genome Biology and Evolution, 17(6), p. evaf119. doi: 10.1093/gbe/evaf119.
• Jowkar, G. et al. (2024) 'Single-character insertion–deletion model preserves long indels in ancestral sequence reconstruction', BMC Bioinformatics, 25(370). doi: 10.1186/s12859-024-05986-1.
• Jowkar, G. et al. (2022) 'ARPIP : ancestral sequence reconstruction with insertions and deletions under the poisson indel process', Systematic Biology, 72(2), pp. 307–318. doi: 10.1093/sysbio/syac050.
• Lardos, A. et al. (2022) 'Computational literature-based discovery for natural products research : current state and future prospects', Frontiers in Bioinformatics, 2(827207). doi: 10.3389/fbinf.2022.827207.
• Maiolo, M. et al. (2021) 'ProPIP : a tool for progressive multiple sequence alignment with Poisson Indel Process', BMC Bioinformatics, 22(1), p. 518. doi: 10.1186/s12859-021-04442-8.
• Liang, S. et al. (2021) 'Querying knowledge graphs in natural language', Journal of Big Data, 8(3). doi: 10.1186/s40537-020-00383-w.
• Maiolo, M. et al. (2020) 'Accelerating phylogeny-aware alignment with indel evolution using short time Fourier transform', NAR Genomics and Bioinformatics, 2(4), p. lqaa092. doi: 10.1093/nargab/lqaa092.
• Sima, A.-C. et al. (2019) 'Enabling semantic queries across federated bioinformatics databases', Database: The Journal of Biological Databases and Curation, 2019(baz106). doi: 10.1093/database/baz106.
• Maiolo, M. et al. (2018) 'Progressive multiple sequence alignment with indel evolution', BMC Bioinformatics, 19(331). doi: 10.1186/s12859-018-2357-1.
• Tan, G. et al. (2015) 'Simple chained guide trees give poorer multiple sequence alignments than inferred trees in simulation and phylogenetic benchmarks', Proceedings of the National Academy of Sciences of the United States of America, 112(2), pp. E99–E100. doi: 10.1073/pnas.1417526112.
• Anisimova, M. et al. (2015) 'The SIB Swiss Institute of Bioinformatics' resources : focus on curated databases', Nucleic Acids Research, 44(D1), pp. D27–D37. doi: 10.1093/nar/gkv1310.
• Tan, G. et al. (2015) 'Current methods for automated filtering of multiple sequence alignments frequently worsen single-gene phylogenetic inference', Systematic Biology, 64(5), pp. 778–791. doi: 10.1093/sysbio/syv033.

Buchbeiträge, peer-reviewed

• Sima, A.-C. et al. (2019) 'Semantic integration and enrichment of heterogeneous biological databases', in Anisimova, M. (ed.) Evolutionary genomics : statistical and computational methods. New York: Springer, pp. 655–690. doi: 10.1007/978-1-4939-9074-0_22.
• Gil, M. and Anisimova, M. (2015) 'Methodologies for phylogenetic inference', in Encyclopedia of life sciences. Hoboken: Wiley. doi: 10.1002/9780470015902.a0025545.

Schriftliche Konferenzbeiträge, peer-reviewed

• Aghaebrahimian, A., Anisimova, M. and Gil, M. (2022) 'Ontology-aware biomedical relation extraction', in Sojka, P. et al. (eds) Text, Speech, and Dialogue. Cham: Springer, pp. 160–171. doi: 10.1007/978-3-031-16270-1_14.
• Koroleva, A., Anisimova, M. and Gil, M. (2020) 'Towards creating a new triple store for literature-based discovery', in Lu, W. and Zhu, K. Q. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2020. Cham: Springer, pp. 41–50. doi: 10.1007/978-3-030-60470-7_5.

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