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DeepLearninginBioinformatics
Artem Bachynskyi02.12.2016
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OrdinaryNNvsConvolutionalNN
Source:http://cs231n.github.io/convolutional-networks/
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Convolutionprocedure
Source:https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
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Pullingprocedure
Source:http://cs231n.stanford.edu/slides/winter1516_lecture1.pdf
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Source:http://cs231n.github.io/convolutional-networks/
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FirstsuccessfulapplicationofCNN
Source:http://cs231n.stanford.edu/slides/winter1516_lecture1.pdf
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Stateoftheartmodel
Source:http://cs231n.stanford.edu/slides/winter1516_lecture1.pdf
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DNNbyGoogle
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RecurrentNN
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Genetic dataInputdata
1. sequencingdata(DNA-seq,RNA-seq,ChIP-seq,DNase-seq)
2. featuresfromgenomicsequence:• positionspecificscoringmatrix(PSSM)• physicochemicalproperties(stericparameter,
volume)• Atchley factors(FAC)• 1-dimensionalstructuralproperties
3. contactmap(distanceofaminoacidpairsin3Dstructure)
4. microarraygeneexpression
Results
Protein structure prediction:• 1-dimensional structural properties• contact map• structure model quality assessment
Gene expression regulation:• splice junction• genetic variants affecting splicing• sequence specificity
Protein classification:• super family• subcellular localization
Anomaly classification:• cancer
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A
B
Source:http://www.nature.com/nbt/journal/v33/n8/fig_tab/nbt.3300_F2.html
scoretheeffectofnovelmutation
identifybinding
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Biomedical imagingInputdata
• magneticresonanceimage(MRI)
• radiographicimagepositronemissiontomography(PET)
• histopathology image
• volumetricelectronmicroscopyimage
• retinalimage
• insituhybridization(ISH)image
• X-rayimages
Results
Anomalyclassification• geneexpressionpattern• cancer• Alzheimer'sdisease• Schizophrenia
Segmentation• cellstructure• neuronalstructure• vesselmap• braintumor
Recognition• cellnuclei• fingerjoint• anatomicalstructure
Braindecoding• behavior
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Source:https://pdfs.semanticscholar.org/b8e6/6cb1544e5bb53400ad6c3a2a4c3748b118b5.pdf
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BiomedicalsignalprocessingInputdata
ECoG, ECG, EMG, EOG
EEG:• raw• wavelet• frequency• differential entropy
extracted features from EEG:• normalized decay• peak variation
Results
Brain decoding• behavior• emotion
Anomaly classification• Alzheimer's disease• seizure• sleep stage
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Source:https://pdfs.semanticscholar.org/b8e6/6cb1544e5bb53400ad6c3a2a4c3748b118b5.pdf
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HeartdiseasedetectionusingCNN
ECG ECGwavelets CNN
DiseasePrediction
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Kaggle competition
CNN
Predictionofdiseasestage
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[email protected],10:00am
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Homework1. VisitthisonlineCNNplatform:http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html2. Byvariating learning rate,momentum, batchsize,weightdecay,trytoachieve0.96accuracy.3. Provideascreenshotofyourresult,please.4. Afterreaching0.96accuracy,press“pause”buttonandscrolldownto“ExamplepredictionsonTestset”,report2-3worstpredictedexamples(withscreenshots).
Additionally, youcanmodifynetworkbychanging JSONfile.
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