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

Read now

Unlock full access

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
Why subscribe?
Other Books You May EnjoyPackt is searching for authors like youShare Your ThoughtsDownload a free PDF copy of this book

Overview

"Deep Learning for Genomics" by Upendra Kumar Devisetty is your guide to applying deep learning techniques in genomic data analysis, a vital field in life sciences and biotechnology. You'll learn practical approaches to analyze and extract meaningful insights from genomic datasets, using modern deep learning methods to solve complex biological problems.

What this Book will help me do

Understand and implement machine learning techniques for genomic applications
Master key supervised and unsupervised deep learning algorithms tailored for genomics
Gain expertise in operationalizing genomic deep learning models, from building to deployment
Identify challenges and apply best practices in genomic deep learning workflows
Effectively communicate biological insights derived from complex genomic datasets

Author(s)

Upendra Kumar Devisetty is a computational biologist with hands-on experience in genomics and biotechnology. He has spent years researching the applications of big data analytics, machine learning, and deep learning in the life sciences. This blend of expertise informs his practical, practitioner-oriented approach in this book.

Who is it for?

This book is designed for data scientists, bioinformaticians, and ML engineers looking to specialize in genomic data analysis and deep learning. It requires familiarity with Python programming and genomic concepts, making it ideal for those with intermediate technical skills seeking to bridge the gap between genomics and AI.

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,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781804615447

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills