Deep Learning Methods for Predicting Disease Status Using Genomic Data

Journal of Biometrics & Biostatistics

article

QianfanWu¹  AdelBoueiz²  AlicanBozkurt⁴  Arya Masoomi⁵  AryaMasoomi⁴  AllanWang⁵  DawnL DeMeo²  ScottT Weiss²  WeiliangQiu² 

[1] Questrom School of Business, Boston University;Channing Division of Network Medicine, Brigham and Women’s Hospital/Harvard Medical School;Department of Medicine, Pulmonary and Critical Care Division, Brigham and Women’s Hospital, Harvard Medical School;Department of Computer Science, Northeastern University;Belmont High School

关键词: Deep learning;    Auto-encoders;    Genomic data;    Disease prediction;    Dimension reduction;   

DOI  :  10.4172/2155-6180.1000417

来源: Hilaris Publisher

【 摘 要 】

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature search. All four articles first used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. These deep learning approaches outperformed existing prediction methods, such as prediction based on transcript-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.

【 授权许可】

Unknown