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4 events found
Event types: Conference
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Introduction to Machine Learning in Bioinformatics
12 - 15 November 2023
Woolloongabba, Australia
Introduction to Machine Learning in Bioinformatics https://incob.apbionet.org/incob23/ https://dresa.org.au/events/introduction-to-machine-learning-in-bioinformatics **This workshop is jointly hosted by [Sonika Tyagi Lab](https://bioinformaticslab.erc.monash.edu/), [COMBINE](combine.org.au) and [Australian BioCommons](https://www.biocommons.org.au/).** Abstract: DNA is the blueprint defining all living organisms. Therefore, understanding the nature and function of DNA is at the core of all biological studies. Rapid advances in DNA sequencing and computing technologies over the past few decades resulted in large quantities of DNA generated for diverse experiments, exceeding the growth of all major social media platforms and astronomy data combined [1]. However, biological data is both complex and high-dimensional, and is difficult to analyse with conventional methods. Machine learning is naturally well suited to problems with a large volume of data and complexity [2]. In particular, applying Natural Language Processing to the genome is intuitive, since DNA is a natural language. Unique challenges exist in Genome-NLP over natural languages, including the difficulty of word segmentation or corpus comparison. To tackle these challenges, we developed the first automated and open-source genomeNLP workflow that enables efficient and accurate knowledge extraction on biological data [1], automating and abstracting preprocessing steps unique to biology. This lowers the barrier to perform knowledge extraction by both machine learning practitioners and computational biologists. In this tutorial, we will demonstrate how our workflow can be used to address the above challenges, with implications in fields such as personalised medicine [3-4]. [1] [preprint] Chen, T., Tyagi, N., Chauhan, S., Peleg, A.Y. and Tyagi, S., 2023. genomicBERT and data-free deep-learning model evaluation. bioRxiv, pp.2023-05. [https://doi.org/10.1101/2023.05.31.542682](https://doi.org/10.1101/2023.05.31.542682) (This article is a preprint and has not been certified by peer review) [2] Chen, T., Tyagi, S. Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, [https://doi.org/10.1093/gigascience/giaa064](https://doi.org/10.1093/gigascience/giaa064) [3] Chen, T., Philip, M., Lê Cao, K-A., Tyagi, S. A multi-modal data harmonisation approach for discovery of COVID-19 drug targets, Briefings in Bioinformatics, Volume 22, Issue 6, November 2021, bbab185, [https://doi.org/10.1093/bib/bbab185](https://doi.org/10.1093/bib/bbab185) [4] Mu, A., Klare, W.P., Baines, S.L. et al. Integrative omics identifies conserved and pathogen-specific responses of sepsis-causing bacteria. Nat Commun 14, 1530 (2023). [https://doi.org/10.1038/s41467-023-37200-w](https://doi.org/10.1038/s41467-023-37200-w) 2023-11-12 09:00:00 UTC 2023-11-15 17:00:00 UTC 22nd International Conference on Bioinformatics (InCoB 2023) Translational Research Institute, 37 Kent Street, Woolloongabba, Australia Translational Research Institute, 37 Kent Street Woolloongabba Australia 4102 Translational Research InstituteQueensland University of TechnologyAsia Pacific Bioinformatics Network tyrone.chen@monash.edu, sonika.tyagi@rmit.edu.au, christina.hall@biocommons.org.au, melissa.burke@biocommons.org.au, combine@combine.org.au [] bioinformaticiansLife scientistsData scientists 40 workshopconference by_invitation Deep learningMachine learningNatural language processingBioinformaticsComputational Biology -
2023 Vocabulary Symposium: FAIR Vocabularies For All
14 - 15 November 2023
Acton, Australia
2023 Vocabulary Symposium: FAIR Vocabularies For All https://www.eventbrite.com.au/e/2023-vocabulary-symposium-fair-vocabularies-for-all-tickets-641760903137 https://dresa.org.au/events/2023-vocabulary-symposium-fair-vocabularies-for-all Spanning research, government & industry. Discussing approaches to using, developing, publishing, and maintaining vocabularies 2023-11-14 09:00:00 UTC 2023-11-15 17:00:00 UTC Australian Research Data Commons 146 Ellery Crescent, Acton, Australia 146 Ellery Crescent Acton Australia 2601 Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 420 conference open_to_all Other -
ResBaz Queensland 2023
21 - 23 November 2023
Saint Lucia, Australia
ResBaz Queensland 2023 https://resbaz.github.io/resbaz2023qld/ https://dresa.org.au/events/resbaz-queensland-2023 The 2023 Queensland Research Bazaar will be a three-day intensive conference where researchers will come together to up-skill in 'next generation digital research tools and skills', hear high-profile keynote speakers, join in networking and career development activities, and connect with their peers from across the SE Qld area. ResBaz is a highly participatory event where researchers from many different disciplines can learn, share knowledge and skills, and have fun! 2023-11-21 09:00:00 UTC 2023-11-23 17:00:00 UTC QCIF Michie Building (9), 9 Chancellors Place, The University of Queensland, Saint Lucia, Australia Michie Building (9), 9 Chancellors Place, The University of Queensland Saint Lucia Australia 4072 QCIFthe University of Queensland resbaz@qcif.edu.au [] HDR studentsPhD studentsECRsAll researchersProfessional/support staff LibrariansData custodians 500 workshopconferencemeeting open_to_all data sciencedigital skillsdata managementcareer development -
Decoding the grammar of DNA using Natural Language Processing
5 - 7 December 2023
Pensacola, United States of America
Decoding the grammar of DNA using Natural Language Processing https://www.k-cap.org/2023/tutorials.html https://dresa.org.au/events/decoding-the-grammar-of-dna-using-natural-language-processing **This workshop is jointly hosted by [Sonika Tyagi Lab](https://bioinformaticslab.erc.monash.edu/), and [Australian BioCommons](https://www.biocommons.org.au/).** Abstract: DNA is the blueprint defining all living organisms. Therefore, understanding the nature and function of DNA is at the core of all biological studies. Rapid advances in DNA sequencing and computing technologies over the past few decades resulted in large quantities of DNA generated for diverse experiments, exceeding the growth of all major social media platforms and astronomy data combined [1]. However, biological data is both complex and high-dimensional, and is difficult to analyse with conventional methods. Machine learning is naturally well suited to problems with a large volume of data and complexity [2]. In particular, applying Natural Language Processing to the genome is intuitive, since DNA is a natural language. Unique challenges exist in Genome-NLP over natural languages, including the difficulty of word segmentation or corpus comparison. To tackle these challenges, we developed the first automated and open-source genomeNLP workflow that enables efficient and accurate knowledge extraction on biological data [1], automating and abstracting preprocessing steps unique to biology. This lowers the barrier to perform knowledge extraction by both machine learning practitioners and computational biologists. In this tutorial, we will demonstrate how our workflow can be used to address the above challenges, with implications in fields such as personalised medicine [3-4]. [1] [preprint] Chen, T., Tyagi, N., Chauhan, S., Peleg, A.Y. and Tyagi, S., 2023. genomicBERT and data-free deep-learning model evaluation. bioRxiv, pp.2023-05. [https://doi.org/10.1101/2023.05.31.542682](https://doi.org/10.1101/2023.05.31.542682) (This article is a preprint and has not been certified by peer review) [2] Chen, T., Tyagi, S. Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, [https://doi.org/10.1093/gigascience/giaa064](https://doi.org/10.1093/gigascience/giaa064) [3] Chen, T., Philip, M., Lê Cao, K-A., Tyagi, S. A multi-modal data harmonisation approach for discovery of COVID-19 drug targets, Briefings in Bioinformatics, Volume 22, Issue 6, November 2021, bbab185, [https://doi.org/10.1093/bib/bbab185](https://doi.org/10.1093/bib/bbab185) [4] Mu, A., Klare, W.P., Baines, S.L. et al. Integrative omics identifies conserved and pathogen-specific responses of sepsis-causing bacteria. Nat Commun 14, 1530 (2023). [https://doi.org/10.1038/s41467-023-37200-w](https://doi.org/10.1038/s41467-023-37200-w) 2023-12-05 09:00:00 UTC 2023-12-07 17:00:00 UTC The Twelfth International Conference on Knowledge Capture Pensacola, Pensacola, United States of America Pensacola Pensacola United States of America ACM Special Interest Group on Artificial IntelligenceFlorida Institute for Human & Machine Cognition (IHMC) tyrone.chen@monash.edu, sonika.tyagi@rmit.edu.au, christina.hall@biocommons.org.au, melissa.burke@biocommons.org.au [] bioinformaticiansLife scientistsData scientists 40 workshopconference by_invitation Deep learningMachine learningNatural language processingBioinformaticsComputational Biology
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Introduction to Machine Learning in Bioinformatics
12 - 15 November 2023
Woolloongabba, Australia
Introduction to Machine Learning in Bioinformatics https://incob.apbionet.org/incob23/ https://dresa.org.au/events/introduction-to-machine-learning-in-bioinformatics **This workshop is jointly hosted by [Sonika Tyagi Lab](https://bioinformaticslab.erc.monash.edu/), [COMBINE](combine.org.au) and [Australian BioCommons](https://www.biocommons.org.au/).** Abstract: DNA is the blueprint defining all living organisms. Therefore, understanding the nature and function of DNA is at the core of all biological studies. Rapid advances in DNA sequencing and computing technologies over the past few decades resulted in large quantities of DNA generated for diverse experiments, exceeding the growth of all major social media platforms and astronomy data combined [1]. However, biological data is both complex and high-dimensional, and is difficult to analyse with conventional methods. Machine learning is naturally well suited to problems with a large volume of data and complexity [2]. In particular, applying Natural Language Processing to the genome is intuitive, since DNA is a natural language. Unique challenges exist in Genome-NLP over natural languages, including the difficulty of word segmentation or corpus comparison. To tackle these challenges, we developed the first automated and open-source genomeNLP workflow that enables efficient and accurate knowledge extraction on biological data [1], automating and abstracting preprocessing steps unique to biology. This lowers the barrier to perform knowledge extraction by both machine learning practitioners and computational biologists. In this tutorial, we will demonstrate how our workflow can be used to address the above challenges, with implications in fields such as personalised medicine [3-4]. [1] [preprint] Chen, T., Tyagi, N., Chauhan, S., Peleg, A.Y. and Tyagi, S., 2023. genomicBERT and data-free deep-learning model evaluation. bioRxiv, pp.2023-05. [https://doi.org/10.1101/2023.05.31.542682](https://doi.org/10.1101/2023.05.31.542682) (This article is a preprint and has not been certified by peer review) [2] Chen, T., Tyagi, S. Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, [https://doi.org/10.1093/gigascience/giaa064](https://doi.org/10.1093/gigascience/giaa064) [3] Chen, T., Philip, M., Lê Cao, K-A., Tyagi, S. A multi-modal data harmonisation approach for discovery of COVID-19 drug targets, Briefings in Bioinformatics, Volume 22, Issue 6, November 2021, bbab185, [https://doi.org/10.1093/bib/bbab185](https://doi.org/10.1093/bib/bbab185) [4] Mu, A., Klare, W.P., Baines, S.L. et al. Integrative omics identifies conserved and pathogen-specific responses of sepsis-causing bacteria. Nat Commun 14, 1530 (2023). [https://doi.org/10.1038/s41467-023-37200-w](https://doi.org/10.1038/s41467-023-37200-w) 2023-11-12 09:00:00 UTC 2023-11-15 17:00:00 UTC 22nd International Conference on Bioinformatics (InCoB 2023) Translational Research Institute, 37 Kent Street, Woolloongabba, Australia Translational Research Institute, 37 Kent Street Woolloongabba Australia 4102 Translational Research InstituteQueensland University of TechnologyAsia Pacific Bioinformatics Network tyrone.chen@monash.edu, sonika.tyagi@rmit.edu.au, christina.hall@biocommons.org.au, melissa.burke@biocommons.org.au, combine@combine.org.au [] bioinformaticiansLife scientistsData scientists 40 workshopconference by_invitation Deep learningMachine learningNatural language processingBioinformaticsComputational Biology -
2023 Vocabulary Symposium: FAIR Vocabularies For All
14 - 15 November 2023
Acton, Australia
2023 Vocabulary Symposium: FAIR Vocabularies For All https://www.eventbrite.com.au/e/2023-vocabulary-symposium-fair-vocabularies-for-all-tickets-641760903137 https://dresa.org.au/events/2023-vocabulary-symposium-fair-vocabularies-for-all Spanning research, government & industry. Discussing approaches to using, developing, publishing, and maintaining vocabularies 2023-11-14 09:00:00 UTC 2023-11-15 17:00:00 UTC Australian Research Data Commons 146 Ellery Crescent, Acton, Australia 146 Ellery Crescent Acton Australia 2601 Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 420 conference open_to_all Other -
ResBaz Queensland 2023
21 - 23 November 2023
Saint Lucia, Australia
ResBaz Queensland 2023 https://resbaz.github.io/resbaz2023qld/ https://dresa.org.au/events/resbaz-queensland-2023 The 2023 Queensland Research Bazaar will be a three-day intensive conference where researchers will come together to up-skill in 'next generation digital research tools and skills', hear high-profile keynote speakers, join in networking and career development activities, and connect with their peers from across the SE Qld area. ResBaz is a highly participatory event where researchers from many different disciplines can learn, share knowledge and skills, and have fun! 2023-11-21 09:00:00 UTC 2023-11-23 17:00:00 UTC QCIF Michie Building (9), 9 Chancellors Place, The University of Queensland, Saint Lucia, Australia Michie Building (9), 9 Chancellors Place, The University of Queensland Saint Lucia Australia 4072 QCIFthe University of Queensland resbaz@qcif.edu.au [] HDR studentsPhD studentsECRsAll researchersProfessional/support staff LibrariansData custodians 500 workshopconferencemeeting open_to_all data sciencedigital skillsdata managementcareer development -
Decoding the grammar of DNA using Natural Language Processing
5 - 7 December 2023
Pensacola, United States of America
Decoding the grammar of DNA using Natural Language Processing https://www.k-cap.org/2023/tutorials.html https://dresa.org.au/events/decoding-the-grammar-of-dna-using-natural-language-processing **This workshop is jointly hosted by [Sonika Tyagi Lab](https://bioinformaticslab.erc.monash.edu/), and [Australian BioCommons](https://www.biocommons.org.au/).** Abstract: DNA is the blueprint defining all living organisms. Therefore, understanding the nature and function of DNA is at the core of all biological studies. Rapid advances in DNA sequencing and computing technologies over the past few decades resulted in large quantities of DNA generated for diverse experiments, exceeding the growth of all major social media platforms and astronomy data combined [1]. However, biological data is both complex and high-dimensional, and is difficult to analyse with conventional methods. Machine learning is naturally well suited to problems with a large volume of data and complexity [2]. In particular, applying Natural Language Processing to the genome is intuitive, since DNA is a natural language. Unique challenges exist in Genome-NLP over natural languages, including the difficulty of word segmentation or corpus comparison. To tackle these challenges, we developed the first automated and open-source genomeNLP workflow that enables efficient and accurate knowledge extraction on biological data [1], automating and abstracting preprocessing steps unique to biology. This lowers the barrier to perform knowledge extraction by both machine learning practitioners and computational biologists. In this tutorial, we will demonstrate how our workflow can be used to address the above challenges, with implications in fields such as personalised medicine [3-4]. [1] [preprint] Chen, T., Tyagi, N., Chauhan, S., Peleg, A.Y. and Tyagi, S., 2023. genomicBERT and data-free deep-learning model evaluation. bioRxiv, pp.2023-05. [https://doi.org/10.1101/2023.05.31.542682](https://doi.org/10.1101/2023.05.31.542682) (This article is a preprint and has not been certified by peer review) [2] Chen, T., Tyagi, S. Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, [https://doi.org/10.1093/gigascience/giaa064](https://doi.org/10.1093/gigascience/giaa064) [3] Chen, T., Philip, M., Lê Cao, K-A., Tyagi, S. A multi-modal data harmonisation approach for discovery of COVID-19 drug targets, Briefings in Bioinformatics, Volume 22, Issue 6, November 2021, bbab185, [https://doi.org/10.1093/bib/bbab185](https://doi.org/10.1093/bib/bbab185) [4] Mu, A., Klare, W.P., Baines, S.L. et al. Integrative omics identifies conserved and pathogen-specific responses of sepsis-causing bacteria. Nat Commun 14, 1530 (2023). [https://doi.org/10.1038/s41467-023-37200-w](https://doi.org/10.1038/s41467-023-37200-w) 2023-12-05 09:00:00 UTC 2023-12-07 17:00:00 UTC The Twelfth International Conference on Knowledge Capture Pensacola, Pensacola, United States of America Pensacola Pensacola United States of America ACM Special Interest Group on Artificial IntelligenceFlorida Institute for Human & Machine Cognition (IHMC) tyrone.chen@monash.edu, sonika.tyagi@rmit.edu.au, christina.hall@biocommons.org.au, melissa.burke@biocommons.org.au [] bioinformaticiansLife scientistsData scientists 40 workshopconference by_invitation Deep learningMachine learningNatural language processingBioinformaticsComputational Biology
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