book

Deep Learning for Genomics

Name: Deep Learning for Genomics
Author: Upendra Kumar Devisetty
ISBN: 9781804615447

by Upendra Kumar Devisetty

November 2022

Intermediate to advanced

270 pages

7h 43m

English

Packt Publishing

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Deep Learning for Genomics
ContributorsAbout the authorAbout the reviewer
Preface
Who is this book for?What this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchReviewsShare Your ThoughtsDownload a free PDF copy of this book
Part 1 – Machine Learning in Genomics
Chapter 1: Introducing Machine Learning for Genomics
What is machine learning?Why machine learning for genomics?Machine learning for genomics in life sciences and biotechnologyExploring machine learning softwarePython programming languageVisualizationBiopythonScikit-learnSummary
Chapter 2: Genomics Data Analysis
Technical requirementsInstalling BiopythonMatplotlibWhat is a genome?Genome sequencingSanger sequencing of nucleic acidsEvolution of next-generation sequencingAnalysis of genomic dataSteps in genomics data analysisIntroduction to Biopython for genomic data analysisWhat is Biopython?Genomic data analysis use case – Sequence analysis of Covid-19Calculating GC contentCalculating nucleotide contentDinucleotide contentModelingMotif finderSummary
Chapter 3: Machine Learning Methods for Genomic Applications
Technical requirementsPython packagesML librariesGenomics big dataSupervised and unsupervised MLSupervised MLUnsupervised MLML for genomicsThe basic workflow of ML in genomicsAn ML use case for genomics – Disease predictionData collectionData preprocessingEDAData transformationData splittingModel trainingModel evaluationML challenges in genomicsSummary
Part 2 – Deep Learning for Genomic Applications
Chapter 4: Deep Learning for Genomics
Understanding what deep learning is and how it worksNeural network definitionAnatomy of deep neural networksKey concepts of DNNsAn example of how neural networks workDNN architecturesDNNs for genomicsDeep learning workflow for genomicsBroad application of DNNs in genomicsProtein structure predictionsRegulatory genomicsGene regulatory networksSingle-cell RNA sequencingIntroducing deep learning algorithms and Python librariesGeneral deep learning librariesDeep learning libraries for genomicsSummary
Chapter 5: Introducing Convolutional Neural Networks for Genomics
Introduction to CNNsWhat are CNNs?Transfer LearningCNNs for genomicsApplications of CNNs in genomicsDeepBindDeepInsightDeepChromeDeepVariantSummary
Chapter 6: Recurrent Neural Networks in Genomics
What are RNNs?Introducing RNNsHow do RNNs work?Different RNN architecturesBidirectional RNNs (BiLSTM )LSTMs and GRUsDifferent types of RNNsApplications and use cases of RNNs in genomicsDeepNanoProLanGoDanQUnderstanding RNNs through Transcription Factor Binding Site (TFBS) predictionsSummary

Chapter 7: Unsupervised Deep Learning with Autoencoders
What is unsupervised DL?Types of unsupervised DLClusteringAnomaly detectionAssociationWhat are autoencoders?Properties of autoencodersHow do autoencoders work?Architecture of autoencodersTypes of autoencodersAutoencoders for genomicsGene expressionUse case – Predicting gene expression from TCGA pan-cancer RNA-Seq data using denoising autoencodersSummary
Chapter 8: GANs for Improving Models in Genomics
What are GANs?Differences between Discriminative and Generative modelsIntuition about GANsHow do GANs work?Challenges working with genomics datasetsWhat is synthetic data?How can GANs help improve models?Practical applications of GANs in genomicsAnalysis of ScRNA-Seq dataGeneration of DNAUsing GANs for augmenting population-scale genomics dataSummary
Part 3 – Operationalizing models
Chapter 9: Building and Tuning Deep Learning Models
Technical requirementsDL life cycleData processingData collectionData wranglingFeature engineeringDeveloping modelsSelecting an appropriate algorithmModel trainingTuning the modelsHyperparameter tuningHyperparameter tuning librariesClassification metrics or performance statisticsVisualizing performanceRegression metricsUse case – Predicting the binding site location of the JunD TFFraming the TFBS prediction problem in terms of DLProcessing the dataModel trainingSummary
Chapter 10: Model Interpretability in Genomics
What is model interpretability?Black-box model interpretabilityUnlocking business value from model interpretabilityBetter business decisionsBuilding trustProfitabilityModel interpretability methods in genomicsPartial dependence plotIndividual conditional expectationPermuted feature importanceGlobal surrogateLIMEShapley valueExSumSaliency mapUse case – Model interpretability for genomicsData collectionFeature extractionTarget labelsTrain-test splitCreating a CNN architectureSummary
Chapter 11: Model Deployment and Monitoring
Technical requirementsStreamlitHugging FaceIntroducing model deploymentSteps in model deploymentTypes of model deploymentDeploying models as servicesA use case for deploying a DL model as a web service – building a Streamlit application of the CNN modelMonitoring models using advanced toolsWhy monitor models?Reasons for model degradationHow to monitor DL modelsAdvanced tools for model monitoringAddressing driftsSummary
Chapter 12: Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics
Deep learning challenges regarding genomicsLack of flexible toolsFewer biological samplesComputational resource requirementsExpertise in DL frameworksLack of high-quality labeled dataLack of model interpretabilityCommon pitfalls for applying deep learning to genomicsConfoundingData leakageImbalanced dataImproper model comparisonsBest practices for applying deep learning to genomicsUnderstand the problem and know your data betterA simple model for a simple problemEstablish a baseline for your modelEnsure reproducibilityUsing pre-existing models for genomicsDo not reinvent the ruleTune hyperparameters automaticallyFocus on feature engineeringNormalize the dataAlways perform model interpretationAvoid overfittingSummary
Index
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Content preview from Deep Learning for Genomics

8 GANs for Improving Models in Genomics

One of the significant developments in the field of Deep learning (DL) has been the introduction of new generative models. The most popular generative models are Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), deep autoregressive models, style transfer, and so on. We learned about what VAEs are in the previous chapter. GANs have become a hot topic in the DL research community in the last few years. They were introduced by Ian Goodfellow in 2014 and are considered one of the most interesting ideas of the last 10 years by Yann LeCun, who is considered the father of modern DL. A GAN, as the name suggests, is a type of generative model that is trained in an adversarial setting to learn ...

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Deep Learning for Genomics

by Upendra Kumar Devisetty

8

GANs for Improving Models in Genomics

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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