Presentation Transcript
Slide1 : Shooting only
Bangor Bioinformatics Mark A. Hooks
School of Biological Sciences
Slide2 : Bioinformatic Interests Zoological/Ecological
Tree-fitting
Population Structure Analysis
Genomics/Post-genomics
Genotyping
‘omics’
Health
Clinical
Image analysis
Bioengineering
Image analysis
Slide3 : The SBS Post-Genomics Platform Suite of instrumentation to facilitate investigations of
gene function. Capabilities:
Transcript profiling through arrays
Metabolite profiling by mass spec.
Protein profiling by 2D gels and mass spec.
Slide4 : Metabolomics Proteomics Detection limit = 10-18 moles
Mass accuracy < 1 PPM error Bruker Reflex IV
MALDI-ToF MS Bruker Apex IV
FTICRMS Automated
Spot Picking ProEXPRESS
Proteomic
Imaging System Arthur Multiwavelength
fluorescent scanner Investigor ProPic Peptide analysis Transcriptomics Liquid handling & spotting Slide Scanning (Affymetrix 428)
Image processing - Imagene Data Analysis - Genesight
Slide5 : Functional Genomics Investigating relationships between metabolite
and transcript levels
Gene function
Delineating functional pathways
How do metabolites influence gene expression?
What metabolites influence gene expression?
Stress or developmental transitions
Global regulators
Slide6 : If a gene is responsive to a metabolite, then it’s
expression will change in relation to that metabolite’s
concentration.
If a metabolite induces expression there will be a
positive correlation coefficient for transcript level vs
metabolite level and visa versa. Metabolite Transcript
Slide7 : Resolve through mutant
analysis Potential metabolic signal Explore mechanism
Slide8 : Grouping metabolites within interacting units Basic visual analysis - Clustered Image Maps Metabolites (clustered)
Slide9 : Slightly more complex system Gene Metabolite Protein
Slide10 : Classifiers Control Mutant Unsupervised
Supervised Feature(s) Discriminate
Slide11 : Combining classifiers Classifier ensembles linear quadratic neural networks Parzen logistic support vector machines fuzzy…………… naïve Bayes decision tree nearest neighbour histogram-based Dr. Ludmila
Kuncheva
(School of
Informatics)
Slide12 : Combiner The input vector The output label Classifier Classifier Classifier . . . x Combining classifiers:
Slide13 : Methods for building classifier ensembles 1. Optimize the combiner 3. Try different classifier models
e.g., decision trees, neural networks,
nearest neighbor, etc. 4. Train each classifier on different subset of features 5. Alter the training data: sample from the whole
data set, inject noise (bagging, boosting,
random forests) Classifier . . . x 2. Use error-correcting output codes (ECOC) Classifier Combiner
Slide14 : Scientific visualization School of Informatics
Prof. Nigel W. John
Dr. Thomas Varsamidis
Dr. Ik Soo Lim Medical Imaging
Biogeometry Human - Computer interfacing
Slide15 : A Biomedical Knowledge Grid:
Towards an Architecture for Data
Mining and Knowledge Discovery
for Biomedical Resources in a Grid Bill Teahan wjt@informatics.bangor.ac.uk
Artificial Intellegence Group Alex Colquhoun
HGMD Research group
(Cardiff University) Seamless integration of
geographically distributed
information resources
Slide16 : Professor Ron Pethig (Aura BioSystems Inc.)
Dr. Julian Burt Laboratory on a chip technology Dielectrophoresis for non-invasive cell characterisation of
cell properties
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