Dr David Kainer
Dr

David Kainer

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Background

I am a computational biologist with a centre-wide research role in the ARC Centre of Excellence for Plant Success in Nature and Agriculture, based here at UQ. I spend my time researching new computational techniques for predicting complex quantitative traits by integrating multiple layers of 'omics data (amongst dozens of other things!).

Areas of interest:

  • Machine Learning, AI and high performance computing to learn and exploit functional connectivity in biological data
  • Gene Expressions networks
  • Multiplex networks, information propagation and perturbation
  • Genomic Prediction

My goal is to aid crop and forestry breeders in selecting parental lines more accurately, which gives us a pathway to improving certain plant species. I also spend time developing new data analysis techniques that are being applied to human disease and conditions such as Autism and substance addiction.

David completed his PhD at Australian National University in 2017, focusing on the genome-wide basis of foliar terpene variation in Eucalyptus. He then undertook a postdoc at Oak Ridge National Laboratory, a US Dept of Energy lab with a focus on big data. After a stint as a staff scientist at Oak Ridge, David arrived at the Centre of Excellence in 2023 in the role of a Senior Research Fellow.

Availability

Dr David Kainer is:
Available for supervision
Media expert

Fields of research

Agricultural, Veterinary and Food Sciences Bioinformatics and computational biology Biological Sciences Biological network analysis Forestry sciences Genetics Genomics Genomics and transcriptomics Information and Computing Sciences Machine learning Neural networks Tree improvement (incl. selection and breeding)

Qualifications

  • Doctoral (Research) of Population, Ecological and Evolutionary Genetics, Australian National University
  • Member, Australian Bioinformatics and Computational Biology Society, Australian Bioinformatics and Computational Biology Society
  • Journal Editorial Board Member, Forestry Research, Forestry Research
  • Board Member, IUFRO Tree Biotech, IUFRO Tree Biotech

Research interests

  • Graph Neural Networks applied to biological data

    Biological interaction data is often best formatted as networks. These can get large and complex, with multiple types of node entities (genes, metabolites etc) and edge relationships. Graph Neural Networks (GNNs) are currently the most powerful way to make predictions on such networks and gain insights that would be hidden from traditional linear models.

  • Network perturbation and phenotype prediction

    What is the effect of editing a gene, or multiple genes? I am applying AI on biological networks to predict the outcomes of gene knockouts or expression tweaks.

  • Tree genomics

    All aspects of tree multi-omics, especially genomics. I have worked with data from diverse tree species: Eucalypts, Poplars, Oak as well as tree crops such as Mango.

Search Professor David Kainer’s works on UQ eSpace

41 works between 2014 and 2024
All (41) Journal Article (37) Conference Publication (2) Other Outputs (2)

41 - 41 of 41 works

2014

Journal Article

Selecting optimal partitioning schemes for phylogenomic datasets

Lanfear, Robert, Calcott, Brett, Kainer, David, Mayer, Christoph and Stamatakis, Alexandros (2014). Selecting optimal partitioning schemes for phylogenomic datasets. BMC Evolutionary Biology, 14 (1) 82, 1-14. doi: 10.1186/1471-2148-14-82

Selecting optimal partitioning schemes for phylogenomic datasets

Availability

Dr David Kainer is:
Available for supervision

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Enquiries

Contact Dr David Kainer directly for media enquiries about:

  • biofuel
  • bioinformatics
  • computational biology
  • genomics
  • GWAS
  • network biology
  • Tree Genetics

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