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Program

Machine Learning in Genome Biology Symposium

Kiama 9th-11th April 2019


Tuesday 9th of April

16:20
Opening Address
16:30
Session 1: Genotype by Phenotype Association I
Keynote
Denis Bauer (CSIRO H&B)
“How Machine Learning and the Cloud transforms Life Science Research”
17:00-
Welcome mixer event
18:30
Drinks and canapés

Wednesday 10th of April

08:30
Welcome
08:35
Session 1 continued: Genotype by Phenotype Association I
Chair – Cheng Soon Ong
08:40
Rob Dunne (CSIRO Data61)
“Interpreting Variable Importances from a Random Forest”
09:00
Marek Cmero (MCRI)
“SVclone: inferring structural variant cancer cell fraction”
09:20
Natalie Twine (CSIRO H&B)
“Novel software ‘TRIBES’ enables distant relationship and disease variant discovery in amyotrophic lateral sclerosis”
09:40
Utpal Bose (CSIRO A&F)
“Analysing proteome-wide alteration – A case study examining the effect of hordein suppression in barley on the grain proteome”
10:00
Morning tea
10:40
Session 2: Genotype by Phenotype Association II
Chair – Klara Verbyla
10:45
Cheng Soon Ong (CSIRO Data61)
“Machine Learning guided Experimental Design”
11:05
Zitong Li (CSIRO A&F)
“Bayesian Gaussian process regression for analysing time course quantitative genetic data”
11:25
Maciej Holowko (CSIRO L&W)
“Using Machine Learning for analysis of big sets of synthetic biology data created in a biofoundry”
11:45
David Deery (CSIRO A&F)
“Phenome to genome opportunities for indirect selection for yield in early generation plant breeding”
12:05
Lunch (meeting spaces available for breakout discussions)
14:00
Session 3: Gene-Gene and Gene-Environment Interaction
Chair – Jen Taylor
14:05
Keynote
Mikael Boden (UQ)
“A case of not just predicting: using machine learning to explain genome-wide observations”
14:35
Michael Vacher (CSIRO H&B)
“Predicting heterosis through constraint-based modelling”
14:55
Arash Bayat (CSIRO H&B)
“VariantSpark: A cloud-based machine learning approach for big genomic data”
15:15
Tony Vuocolo (CSIRO A&F)
“A life well lived; an epigenetic biomarker reporting on an animal’s welfare profile”
15:35
Afternoon tea
16:05
Session 4: Modeling Biological Outcomes I
Chair – Saul Newman
16:10
Keynote
Jenifer Spindel (Bayer)
“Genomic Selection plus High Density Phenotype Genome-Wide Association (HDP-GWAS) for Rapid Genetic Gain”
16:40
Jana Sperschneider (CSIRO A&F)
“Using machine learning to understand how plant pathogens cause disease”
17:00
James Broadbent (CSIRO A&F)
“Wheat pan-proteomics: Unifying data-independent LC-MS proteome measurements across diverse genetic backgrounds for predicting flowering outcome”
17:20
Yutao Li (CSIRO A&F)
“Using Machine Learning Method – Random Forests to Identify the Problematic SNPs That Increase the Error Variance and Decrease Accuracy of Genomic Prediction”
17:40
Nick Wade (CSIRO A&F)
“Prawn Hyperspectral Imaging”
18:00
Free time
18:30-
21:30
Symposium Diner
Old School House

Thursday 11th of April

08:30
Session 5: Modeling Biological Outcomes II
Chair – Yutao Li
08:35
Keynote
Christian Maltecca (NCSU)
“Peeling the onion: Application of machine learning at different stages of livestock production”
09:05
Keynote
Alex Calderwood (JIC)
“from model to crop: Gaussian processes for the analysis of gene expression“
9:35
Shannon Dillon (CSIRO A&F)
“OzWheat: a multi data platform for genome to phenome in Australian wheat”
9:55
Saul Newman (ANU)
“Using environmental data, from cells to satellites, to untangle genomic interactions”
10:15
Morning tea
10:55
Session 6: Genome Characterisation
Chair – Jana Sperschneider
11:00
Marina Naval-Sanchez (CSIRO A&F)
“Machine Learning Applications in functional genomics”
11:20
Benjamin Mayne (CSIRO Environomics FSP)
“Animal lifespan is predicted by promoter CpG density in a suite of conserved genes”
11:40
Alex Essebier (UQ)
“A Bayesian Network approach to predicting transcription factor binding sites in vivo”
12:00
Natasha Botwright (CSIRO A&F)
“The hidden genome of a marine amoeba Neoparamoeba perurans”
12:20
Lunch (meeting spaces available for breakout discussions)
14:00
Session 7: “Un-conference”
Chair – Eric Stone
Unpack a set of key challenges that machine learning can address in genome biology, as drawn out during the symposium.
15:00
Afternoon tea
15:30
Symposium close