High throughput genome sequencing technologies such as RNA-Seq and Microarray
have the potential to transform clinical decision making and biomedical
research by enabling high-throughput measurements of the genome at a granular
level. However, to truly understand causes of disease and the effects of
medical interventions, this data must be integrated with phenotypic,
environmental, and behavioral data from individuals. Further, effective
knowledge discovery methods that can infer relationships between these data
types are required. In this work, we propose a pipeline for knowledge discovery
from integrated genomic and clinical data. The pipeline begins with a novel
variable selection method, and uses a probabilistic graphical model to
understand the relationships between features in the data. We demonstrate how
this pipeline can improve breast cancer outcome prediction models, and can
provide a biologically interpretable view of sequencing data.

本文提出了一种通过将表型，环境和行为数据与基因组数据整合的流程，使用一种概率图模型来理解数据中特征之间的关系，并展示了该流程如何改善乳腺癌预测模型，提供一种生物学可解释性的视图。

基因组和临床数据的理论与数据驱动建模的流水线

A Pipeline for Integrated Theory and Data-Driven Modeling of Genomic and  Clinical Data

Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical
courses that may vary from spontaneous regression to fatal progression; an
accurate patient's risk estimation at diagnosis is essential to design
appropriate tumor treatment strategies. Neuroblastoma is a paradigm disease
where different diagnostic and prognostic endpoints should be predicted from
common molecular and clinical information, with increasing complexity, as shown
in the FDA MAQC-II study. Here we introduce the novel multiobjective deep
learning architecture CDRP (Concatenated Diagnostic Relapse Prognostic)
composed by 8 layers to obtain a combined diagnostic and prognostic prediction
from high-throughput transcriptomics data. Two distinct loss functions are
optimized for the Event Free Survival (EFS) and Overall Survival (OS)
prognosis, respectively. We use the High-Risk (HR) diagnostic information as an
additional input generated by an autoencoder embedding. The latter is used as
network regulariser, based on a clinical algorithm commonly adopted for
stratifying patients from cancer stage, age at insurgence of disease, and MYCN,
the specific molecular marker. The architecture was applied to Illumina
HiSeq2000 RNA-Seq for 498 neuroblastoma patients (176 at high risk) from the
Sequencing Quality Control (SEQC) study, obtaining state-of-art on the
diagnostic endpoint and improving prediction of prognosis over the HR cohort.

使用深度学习架构 CDRP 并结合高通量转录组数据进行诊断和预测，优于临床算法对于神经母细胞瘤的预后，为正确的肿瘤治疗策略提供了重要的分析依据。