Sundaram Laksshman, Bhat Rajendra Rana, Viswanath Vivek, Li Xiaolin
National Science Foundation Center for Big Learning, University of Florida, Gainesville, Florida.
Hum Mutat. 2017 Sep;38(9):1217-1224. doi: 10.1002/humu.23272. Epub 2017 Aug 1.
Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set.
双相情感障碍，也被称为躁郁症，是一种影响患者大脑结构的脑部疾病。它会导致极端的情绪波动，同时出现严重的抑郁状态和过度兴奋。据估计，美国约3%的人口（约530万成年人）患有双相情感障碍。像双胞胎研究这样的近期研究成果表明，该疾病具有很高的遗传因素，这使得基因组学成为检测和治疗双相情感障碍的一种可行选择，除了传统的冗长且昂贵的症状出现后的临床诊断方法。受这项研究的启发，我们利用几种新兴的深度学习算法，设计了一种端到端的深度学习架构（称为DeepBipolar），用于基于有限的基因组数据预测双相情感障碍。DeepBipolar采用深度卷积神经网络（DCNN）架构，该架构会自动从基因型信息中提取特征以预测双相情感障碍的表型。我们参加了基因组解释关键评估（CAGI）双相情感障碍挑战赛，并且独立评估者认为DeepBipolar是最成功的。在这项工作中，我们全面评估了DeepBipolar的性能，并分析了我们认为可能影响分类器将病例样本与对照组区分开来的信号类型。
