Many causal systems such as biological processes in cells can only be
observed indirectly via measurements, such as gene expression. Causal
representation learning -- the task of correctly mapping low-level observations
to latent causal variables -- could advance scientific understanding by
enabling inference of latent variables such as pathway activation. In this
paper, we develop methods for inferring latent variables from multiple related
datasets (environments) and tasks. As a running example, we consider the task
of predicting a phenotype from gene expression, where we often collect data
from multiple cell types or organisms that are related in known ways. The key
insight is that the mapping from latent variables driven by gene expression to
the phenotype of interest changes sparsely across closely related environments.
To model sparse changes, we introduce Tree-Based Regularization (TBR), an
objective that minimizes both prediction error and regularizes closely related
environments to learn similar predictors. We prove that under assumptions about
the degree of sparse changes, TBR identifies the true latent variables up to
some simple transformations. We evaluate the theory empirically with both
simulations and ground-truth gene expression data. We find that TBR recovers
the latent causal variables better than related methods across these settings,
even under settings that violate some assumptions of the theory.

通过从多个相关数据集和任务中推断潜在变量，我们开发了一种从基因表达预测表型等任务中学习潜在因果变量的方法，其中关键观点是基于基因表达引起的潜在变量与感兴趣表型之间的映射在密切相关的环境下变化稀疏。我们引入了树状正则化来建模稀疏变化，通过最小化预测误差并对密切相关的环境进行正则化以学习相似的预测器，理论证明了在稀疏变化程度的某些简单转换下，树状正则化能够识别出真正的潜在变量。我们在模拟实验和真实的基因表达数据上进行了理论验证，发现在这些设置下，相比相关方法，树状正则化更好地恢复了潜在因果变量，即使在违背某些理论假设的情况下也是如此。

利用环境结构：系统发展正则化鼓励解开的表征

Leveraging Structure Between Environments: Phylogenetic Regularization  Incentivizes Disentangled Representations

Detailed phenotype information is fundamental to accurate diagnosis and risk
estimation of diseases. As a rich source of phenotype information, electronic
health records (EHRs) promise to empower diagnostic variant interpretation.
However, how to accurately and efficiently extract phenotypes from the
heterogeneous EHR data remains a challenge. In this work, we present PheME, an
Ensemble framework using Multi-modality data of structured EHRs and
unstructured clinical notes for accurate Phenotype prediction. Firstly, we
employ multiple deep neural networks to learn reliable representations from the
sparse structured EHR data and redundant clinical notes. A multi-modal model
then aligns multi-modal features onto the same latent space to predict
phenotypes. Secondly, we leverage ensemble learning to combine outputs from
single-modal models and multi-modal models to improve phenotype predictions. We
choose seven diseases to evaluate the phenotyping performance of the proposed
framework. Experimental results show that using multi-modal data significantly
improves phenotype prediction in all diseases, the proposed ensemble learning
framework can further boost the performance.

本文提出了 PheME 这一基于多模态数据应用深度神经网络和模型融合技术的方法来准确地从电子病历中提取表型信息，以改善表型预测的性能。经过实验验证，PheME 方法在使用多模态数据的情况下，在七种疾病的表型预测中都获得了显著的提高。