[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118129-en":3,"doc-seo-118129-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118129,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Predicting Depression in Black Women - A Machine Learning Epigenetic Approach","Depression is a widespread and disabling mental health disorder, with Black women experiencing a disproportionate burden. Due to varied symptom presentation and cultural stigma, diagnosis is challenging and symptoms may be underreported. This dissertation investigates how social determinants of health relate to depressive symptoms by using DNA methylation data and machine learning to predict depressive symptoms in Black women. Chapter 2 reviews machine learning methods for omics classification and analyzes depression in Black mothers; Chapters 3A–3C examine socioeconomic deprivation, perceived income inadequacy, and differential methylation; Chapter 4 applies supervised models and feature selection to build prediction, highlighting discrimination’s mental health harms and DNA methylation’s association with symptoms.","Predicting Depression in Black Women: A Machine Learning Epigenetic Approach  \nBrittany N. Taylor  \nSubmitted in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nunder the Executive Committee  \nof the Graduate School of Arts and Sciences  \nCOLUMBIA UNIVERSITY  \n2024  \n© 2024 Brittany N. Taylor All Rights Reserved  \nAbstract  \nPredicting Depression in Black Women: A Machine Learning Epigenetic Approach  \nBrittany N. Taylor  \nDepression is one of the most widespread and disabling mental health disorders affecting adults worldwide, and Black women bear a disproportionate burden of this disorder. With its varied symptom presentation, depression can be difficult to diagnose. In addition, Black women may be less likely to report symptoms due to cultural stigma. The purpose of this dissertation is to examine the associations between social determinants of health and depressive symptoms using DNA methylation data and machine learning to predict depressive symptoms in Black women. Chapter 2 contains two comprehensive literature reviews: a scoping review of machine learning methods used to analyze omics data to classify depressed cases and healthy controls anda concept analysis of depression in Black mothers. Chapter 3 examines associations between social determinants of health, depressive symptoms, and DNA methylation. Chapter 3A focuses on socioeconomic deprivation; Chapter 3B focuses on perceived income inadequacy; and Chapter 3C identifies differential methylation associated with depressive symptoms. Chapter 4 utilizes supervised machine learning algorithms to predict depressive symptoms and perform feature selection. These chapters show the harmful effects that perceived discrimination can have on the mental health of Black women. Additionally, the results indicate that DNA methylation is associated with depressive symptoms, an area which requires further research.  \nTable of Contents  \nList of Tables……………………………………………………………………………….…viii  \nList of Supplemental Tables…………………….…………………………..…………..…..ix  \nList of Figures…………………………………………………………………………………….x  \nAcknowledgments .................................................................................................................. xi  \nFunding…………………………………………………………………………..……..xiii  \nDedication............................................................................................................................ xiv  \nChapter 1: Introduction............................................................................................................ 1  \n1.1 Depression Prevalence and Significance ......................................................................... 1  \n1.1.1 Racial Disparities in Depression ............................................................................... 1  \n1.1.2 Gender Disparities in Depression ............................................................................. 3  \n1.1.3 Intersectionality of Race and Gender as a Risk Factor for Depression ........................ 4  \n1.1.4 Depression Screening and Diagnosis ........................................................................ 5  \n1.2 Genetics of Depression................................................................................................... 5  \n1.2.1 DNA Methylation and Depression............................................................................ 7  \n1.3 Machine Learning .......................................................................................................... 8  \n1.3.1 Entanglement Mapping.......................................................................................... 12  \n1.4 InterGEN Study ........................................................................................................... 13  \n1.5 Dissertation Aims and Organization.............................................................................. 14  \n1.6 Theoretical Frameworks.....................................................","cbCaivjwN6H3UQ29","https://ap.wps.com/l/cbCaivjwN6H3UQ29","pdf",1615512,1,212,"English","en",105,"# Chapter 1: Introduction\n## Depression Prevalence and Significance\n## Genetics of Depression\n## Machine Learning\n## InterGEN Study\n## Dissertation Aims and Organization\n## Theoretical Frameworks\n# Chapter 2A: Identifying Depression Through Machine Learning Analysis of Omics Data: A Scoping Review\n## Introduction\n## Methods","[{\"question\":\"What is the main goal of the dissertation?\",\"answer\":\"To examine associations between social determinants of health and depressive symptoms using DNA methylation data and machine learning to predict depressive symptoms in Black women.\"},{\"question\":\"Which topics are covered in the literature review chapter?\",\"answer\":\"Chapter 2 includes two comprehensive literature reviews: a scoping review of machine learning methods for classifying depressed and healthy cases using omics data, and a concept analysis of depression in Black mothers.\"},{\"question\":\"How do the later chapters connect social factors, epigenetics, and prediction?\",\"answer\":\"Chapter 3 links social determinants of health with depressive symptoms and DNA methylation, including socioeconomic deprivation, perceived income inadequacy, and differential methylation; Chapter 4 uses supervised machine learning and feature selection to predict depressive symptoms and identify relevant features.\"}]","Predicting Depression in Black Women - A Machine Learning Epigenetic Approach | PDF",1785681753,534,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-depression-in-black-women-a-machine-learning-epigenetic-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-depression-in-black-women-a-machine-learning-epigenetic-approach/118129/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the dissertation?","Question",{"text":75,"@type":76},"To examine associations between social determinants of health and depressive symptoms using DNA methylation data and machine learning to predict depressive symptoms in Black women.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which topics are covered in the literature review chapter?",{"text":80,"@type":76},"Chapter 2 includes two comprehensive literature reviews: a scoping review of machine learning methods for classifying depressed and healthy cases using omics data, and a concept analysis of depression in Black mothers.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the later chapters connect social factors, epigenetics, and prediction?",{"text":84,"@type":76},"Chapter 3 links social determinants of health with depressive symptoms and DNA methylation, including socioeconomic deprivation, perceived income inadequacy, and differential methylation; 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