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--- research:publications [2024/11/19 17:03] – mikael
+++ research:publications [2025/02/15 19:06] (current) – mikael
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 ==== Pre-prints of note ====
-  - Tule S, Foley G and Bodén M. Do Protein Language Models Learn Phylogeny? [[https://www.biorxiv.org/content/10.1101/2024.09.23.614642v2|https://www.biorxiv.org/content/10.1101/2024.09.23.614642v2]]
+  - Chen R, Foley G and Bodén M. Learning the Language of Phylogeny with MSA Transformer. [[https://www.biorxiv.org/content/10.1101/2024.12.18.629037v1|https://www.biorxiv.org/content/10.1101/2024.12.18.629037v1]]
-  - Mora A, Schmidt C, Balderson B, Frezza C and Bodén M. SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer. [[https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1|https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1]]
+  - Sinniah E et al. Epigenetic constraint of cellular genomes evolutionarily links genetic variation to function [[https://doi.org/10.1101/2024.10.28.620690|https://doi.org/10.1101/2024.10.28.620690]]
-  - Shen S et al. An integrated cell barcoding and computational analysis pipeline for scalable analysis of differentiation at single-cell resolution. [[https://doi.org/10.1101/2022.10.12.511862|https://doi.org/10.1101/2022.10.12.511862]]
 ==== Journal papers ====
-  - Mora A, Schmidt C, Balderson B, Frezza C and Bodén M. (Accepted) SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer. //Genome Medicine//.[https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1|https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1]]
+  - Shen S, Werner T, Lukowski SW, Andersen S, Sun Y, Shim WJ, Mizikovsky D, Kobayashi S, Outhwaite J, Chin HS, Chen X, Chapman G, Martin E, Xia D, Pham D, Su Z, Kim D, Yang P, Tan MC, Sinniah E, Zhao Q, Negi S, Redd MA, Powell JE, Dunwoodie SL, Tam P, Bodén M, Ho JWK, Nguyen Q and Palpant NJ. (2025) Atlas of multilineage stem cell differentiation reveals TMEM88 as a developmental regulator of blood pressure. //Nature Communications//. DOI [[https://doi.org/10.1038/s41467-025-56533-2|10.1038/s41467-025-56533-2]] [[https://doi.org/10.1101/2022.10.12.511862|bioRxiv pre-print]]
-  - Joshi P et al. (Accepted). Phage Anti-Pycsar Proteins Efficiently Degrade β-Lactam Antibiotics. //Applied Biosciences//. [[https://doi.org/10.20944/preprints202407.0921.v1|Pre-print]]
+  - Tule S, Foley G and Bodén M. (2025) Do Protein Language Models Learn Phylogeny? //Briefings in Bioinformatics//. DOI [[https://doi.org/10.1093/bib/bbaf047|10.1093/bib/bbaf047]] [[https://www.biorxiv.org/content/10.1101/2024.09.23.614642v2|bioRxiv pre-print]]
-  - Prabhu A, Tule S, Chuvochina M, Bodén M, McIlroy SJ, Zaugg J and Rinke C. (Accepted) Machine learning and metagenomics identifies uncharacterized taxa inferred to drive biogeochemical cycles in a subtropical hypereutrophic estuary. //ISME Communications//. DOI [[https://doi.org/10.1093/ismeco/ycae067|10.1093/ismeco/ycae067]]
+  - Zhao QY, Shim WJ, Sun Y, Sinniah E, Shen S, Bodén M and Palpant N. (2025) TRIAGE: An R Package for Regulatory Gene Analysis. //Briefings in Bioinformatics//. DOI [[https://doi.org/10.1093/bib/bbaf004|10.1093/bib/bbaf004]]
-  - Tule S, Foley G, Zhao C, Forbes M and Bodén M. (Accepted) Optimal Phylogenetic Reconstruction of Insertion and Deletion Events. //Bioinformatics/ISMB 2024 Proceedings//. DOI [[https://doi.org/10.1093/bioinformatics/btae254|10.1093/bioinformatics/btae254]] [[https://doi.org/10.1101/2024.01.24.577130|Pre-print]]
+  - Mora A, Schmidt C, Balderson B, Frezza C and Bodén M. (2024) SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer. //Genome Medicine// 16(144). DOI [[https://doi.org/10.1186/s13073-024-01415-3|10.1186/s13073-024-01415-3]] [[https://rdcu.be/d2uim|Springer Nature SharedIt]]
-  - Balderson B, Fane M, Harvey TJ, Piper M, Smith A and Bodén M. (2024) Systematic analysis of the Transcriptional Landscape of Melanoma Reveals Drug-target Expression Plasticity. //Briefings in Functional Genomics//. DOI [[https://doi.org/10.1093/bfgp/elad055|10.1093/bfgp/elad055]]
+  - Joshi P et al. (2024) Phage Anti-Pycsar Proteins Efficiently Degrade β-Lactam Antibiotics. //Applied Biosciences// 3(4). DOI [[https://doi.org/10.3390/applbiosci3040028|10.3390/applbiosci3040028]]
-  - Teshima M, Sutiono S, Döring M, Beer B, Boden M, Schenk G and Sieber V (2023) Development of a Highly Selective NAD+-Dependent Glyceraldehyde Dehydrogenase and its Application in Minimal Cell-Free Enzyme Cascades. //ChemSusChem// DOI [[https://doi.org/10.1002/cssc.202301132|10.1002/cssc.202301132]]
+  - Prabhu A, Tule S, Chuvochina M, Bodén M, McIlroy SJ, Zaugg J and Rinke C. (2024) Machine learning and metagenomics identifies uncharacterized taxa inferred to drive biogeochemical cycles in a subtropical hypereutrophic estuary. //ISME Communications// 4(1). DOI [[https://doi.org/10.1093/ismeco/ycae067|10.1093/ismeco/ycae067]]
+  - Tule S, Foley G, Zhao C, Forbes M and Bodén M. (2024) Optimal Phylogenetic Reconstruction of Insertion and Deletion Events. //Bioinformatics// 40:i277–i286. DOI [[https://doi.org/10.1093/bioinformatics/btae254|10.1093/bioinformatics/btae254]]
+  - Balderson B, Fane M, Harvey TJ, Piper M, Smith A and Bodén M. (2024) Systematic analysis of the Transcriptional Landscape of Melanoma Reveals Drug-target Expression Plasticity. //Briefings in Functional Genomics// elad055. DOI [[https://doi.org/10.1093/bfgp/elad055|10.1093/bfgp/elad055]]
+  - Teshima M, Sutiono S, Döring M, Beer B, Boden M, Schenk G and Sieber V (2023) Development of a Highly Selective NAD+-Dependent Glyceraldehyde Dehydrogenase and its Application in Minimal Cell-Free Enzyme Cascades. //ChemSusChem// 17(4). DOI [[https://doi.org/10.1002/cssc.202301132|10.1002/cssc.202301132]]
   - Balderson B, Piper M, Thor S and Bodén M. (2023) Cytocipher detects significantly different populations of cells in single cell RNA-seq data. //Bioinformatics//. 39(7):btad435. DOI [[https://doi.org/10.1093/bioinformatics/btad435|10.1093/bioinformatics/btad435]][[https://www.biorxiv.org/content/10.1101/2022.08.12.503759v2|biorxiv]]
   - Sun Y, Shim W, Shen S, Sinniah E, Pham D, Su Z, Mizikovsky D, White MD, Ho JWK, Nguyen Q, Bodén M, Palpant NJ. (2023) Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity. //Nucleic Acids Research//. DOI [[https://doi.org/10.1093/nar/gkad307|10.1093/nar/gkad307]]
@@ Line 85: / Line 87: @@
   - Hawkins J, Mahony D, Maetschke S, Wakabayashi M, Teasdale RD and Bodén M (2007) Identifying novel peroxisomal proteins. //Proteins// 69(3): 606-616.
   - Hawkins J, Davis L and Bodén M (2007) //Predicting nuclear localization//. J Proteome Res 6(4): 1402-1409.
-  - Bauer, D., Bodén M., Thier, R. and Gillam, E. M. STAR: Predicting recombination sites from amino acid sequence. //BMC Bioinformatics//, 7:437, 2006. (Open access.)
+  - Bauer D, Bodén M, Thier R and Gillam EM. STAR: Predicting recombination sites from amino acid sequence. //BMC Bioinformatics//, 7:437, 2006. (Open access.)
-  - Bodén, M. and Bailey, T. L. Identifying sequence regions undergoing conformational change via predicted continuum secondary structure. //Bioinformatics//. 22(15): 1809-1814, 2006. (Open access.)
+  - Bodén M and Bailey TL. Identifying sequence regions undergoing conformational change via predicted continuum secondary structure. //Bioinformatics//. 22(15): 1809-1814, 2006. (Open access.)
-  - Bodén, M., Yuan, Z. and Bailey, T. L. Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures. //BMC Bioinformatics//. 7:68, 2006. (Open access.)
+  - Bodén M, Yuan Z and Bailey TL. Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures. //BMC Bioinformatics//. 7:68, 2006. (Open access.)
-  - Yuan, Z., Zhang, F., Davis, M. J., Bodén  M. and Teasdale, R. D. Predicting the solvent accessibility of transmembrane residues from protein sequence. //Journal of Proteome Research//. 5(5):1063-1070, 2006.
+  - Yuan Z, Zhang F, Davis MJ, Bodén M and Teasdale RD. Predicting the solvent accessibility of transmembrane residues from protein sequence. //Journal of Proteome Research//. 5(5):1063-1070, 2006.
-  - Hawkins, J. and Bodén  M. Detecting and sorting targeting peptides with recurrent networks and support vector machines. //Journal of Bioinformatics and Computational Biology//, 4(1), 2006.
+  - Hawkins J and Bodén M. Detecting and sorting targeting peptides with recurrent networks and support vector machines. //Journal of Bioinformatics and Computational Biology//, 4(1), 2006.
-  - Bodén, M. and Hawkins, J. Prediction of subcellular localisation using sequence-biased recurrent networks. //Bioinformatics//. 21(10):2279-2286, 2005.
+  - Bodén M and Hawkins J. Prediction of subcellular localisation using sequence-biased recurrent networks. //Bioinformatics//. 21(10):2279-2286, 2005.
-  - Hawkins, J. and Bodén  M. The applicability of recurrent neural networks for biological sequence analysis. //IEEE/ACM Transactions on Computational Biology and Bioinformatics//, 2(3): 243-253, 2005.
+  - Hawkins J and Bodén M. The applicability of recurrent neural networks for biological sequence analysis. //IEEE/ACM Transactions on Computational Biology and Bioinformatics//, 2(3): 243-253, 2005.
-  - Bodén, M. and Hawkins, J. Improved access to sequential motifs: A note on the architectural bias of recurrent networks. //IEEE Transactions on Neural Networks//. 16(2), 2005.
+  - Bodén M and Hawkins J. Improved access to sequential motifs: A note on the architectural bias of recurrent networks. //IEEE Transactions on Neural Networks//. 16(2), 2005.
-  - Bodén, M. //Generalization by symbolic abstraction in cascaded recurrent networks, Neurocomputing//, 57, pp. 87-104, 2004.
+  - Bodén M. //Generalization by symbolic abstraction in cascaded recurrent networks, Neurocomputing//, 57, pp. 87-104, 2004.
-  - Bodén, M. and Blair, A. Learning the dynamics of embedded clauses, Applied Intelligence: //Special issue on natural language and machine learning//, 19(1/2), pp. 51-63, 2003.
+  - Bodén M and Blair A. Learning the dynamics of embedded clauses, Applied Intelligence: //Special issue on natural language and machine learning//, 19(1/2), pp. 51-63, 2003.
-  - Bodén, M. and Wiles, J. On learning context free and context sensitive languages, //IEEE Transactions on Neural Networks//. 13(2), pp. 491-493, 2002.
+  - Bodén M and Wiles J. On learning context free and context sensitive languages, //IEEE Transactions on Neural Networks//. 13(2), pp. 491-493, 2002.
-  - Bodén, M. and Wiles, J., Context-free and context-sensitive dynamics in recurrent neural networks, //Connection Science//, 12 (3/4), pp. 197-210, 2000.
+  - Bodén M and Wiles J. Context-free and context-sensitive dynamics in recurrent neural networks, //Connection Science//, 12 (3/4), pp. 197-210, 2000.
-  - Bodén, M. and Niklasson, L., Semantic systematicity and context in connectionist Networks, //Connection Science//, 12 (2), pp. 111-142, 2000.
+  - Bodén M and Niklasson L. Semantic systematicity and context in connectionist Networks, //Connection Science//, 12 (2), pp. 111-142, 2000.
 ==== Refereed conference papers (since 2000) ====