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Thanks to major advances in biotechnology and instrumentation, biology is becoming an information centred science. The field of bioinformatics draws on computer science, math and statistics to enable discoveries in biological data sets. Our research aims to develop, investigate and apply bioinformatics methodologies to understand and resolve a range of open problems in genomics, molecular and systems biology. Recent applications involve protein sorting, nuclear protein organisation, mechanisms of transcriptional regulation, sequence and structure determinants of protein function and modification, and protein engineering. Biological data are now available at scales that challenges our ability to process and analyse them. On the flip side, greater scale gives statistical power to distinguish biologically meaningful signals from mere noise or artefacts, i.e. to identify "drivers" and "determinants" of function and structure. Sometimes the number of features (that describe each observation) is so great that we must use (biological) expertise to constrain the search for signals. Broadly put, our research aims to 1. effectively manage the complexity of operations involved in analysing millions of sequence reads, thousands of genomes, and proteomes of thousands of dynamically regulated molecules, etc 2. enable the seamless aggregation (or integration) of uncertain and incomplete data, typical of the next wave of biotechnology, across genomics, proteomics, structural biology, etc, and of using biological expertise 3. empower the interpretation of "whole system" data, aimed at understanding of basis of disease and other scientifically relevant phenotypes, using statistics and machine learning
Thanks to major advances in biotechnology and instrumentation, biology is becoming an information centred science. The field of bioinformatics draws on computer science, math and statistics to enable discoveries in biological data sets. Our research aims to develop, investigate and apply bioinformatics methodologies to understand and resolve a range of open problems in genomics, molecular and systems biology. Recent applications involve protein sorting, nuclear protein organisation, mechanisms of transcriptional regulation, sequence and structure determinants of protein function and modification, and protein engineering. Biological data are now available at scales that challenges our ability to process and analyse them. On the flip side, greater scale gives statistical power to distinguish biologically meaningful signals from mere noise or artefacts, i.e. to identify "drivers" and "determinants" of function and structure. Sometimes the number of features (that describe each observation) is so great that we must use (biological) expertise to constrain the search for signals. Broadly put, our research aims to 1. effectively manage the complexity of operations involved in analysing millions of sequence reads, thousands of genomes, and proteomes of thousands of dynamically regulated molecules, etc 2. enable the seamless aggregation (or integration) of uncertain and incomplete data, typical of the next wave of biotechnology, across genomics, proteomics, structural biology, etc, and of using biological expertise 3. empower the interpretation of "whole system" data, aimed at understanding of basis of disease and other scientifically relevant phenotypes, using statistics and machine learning
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Bioinformatics (Oxford, England)no. Supplement_1 (2024): i277-i286
CHEMSUSCHEMno. 4 (2024): e202301132-e202301132
Bioinformatics (Oxford, England)no. 7 (2023)
J. Afonso, W.J. Shim,M. Boden,N. Palpant,M.R.S. Fortes,W.J.S. Diniz, A.O. Lima,M.I.P. Rocha,T.F. Cardoso,J.J. Bruscadin,C.F. Gromboni,A.R.A. Nogueira,
Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) (2022)
Bioinformaticsno. 7 (2022)
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