用动态奖励学习重构复杂潜变量结构，提升生物数据建模精度
Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
- 基于动态重标奖励的策略学习框架，直接优化观测数据似然
- 在模拟潜集和潜图上重建准确率显著优于基线方法
- 成功从短读长测序数据中重构更接近长读长验证的转录本
许多科学问题需要从间接观测中推断未观测到的机制性潜变量状态。传统方法如期望最大化难以处理组合爆炸的潜空间，而深度学习方法如变分自编码器通常生成人工潜变量而非真实机制状态。本文提出GReinSS，一种利用动态重标奖励的策略学习框架，可学习最大化观测数据似然的潜状态分布。实验表明，GReinSS能准确重建模拟的潜集与潜图，性能优于其他策略学习与生成建模基线。此外，它从真实短读长RNA测序数据中重构的转录本，与独立长读长测序检测结果的匹配度高于标准RSEM算法。整体而言，GReinSS是一种理论严谨且实用高效的组合潜变量生成建模与推断方法。
原文摘要 · Abstract (English)
Many scientific problems require inferring unobserved mechanistic latent states from indirect observations. While classical approaches, including expectation maximization, do not scale to combinatorially large spaces, deep learning approaches such as variational autoencoders typically form artificial latent states rather than reconstructing the mechanistic ground-truth states. Here, we introduce GReinSS, a policy learning framework that uses dynamically rescaled rewards to learn latent state distributions that maximize the observed data likelihood. We show that GReinSS accurately reconstructs simulated latent sets and latent graphs, outperforming alternative policy learning and generative modeling baselines. Additionally, GReinSS reconstructs isoforms from real short-read RNA sequencing data that better match isoforms detected by orthogonal long-read sequencing than the standard RSEM algorithm. Overall, GReinSS is a principled and practically effective approach for generative modeling and inference of combinatorial latent states from indirect observations.
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