Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction

TL;DR

Prism, a framework that learns multiple combinations of high-dimensional epigenomic features to represent distinct background chromatin states and uses backdoor adjustment to mitigate confounding effects, is proposed.

Abstract

Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of kilobases away. Our work first reveals that for current models, long sequence modeling can decrease performance. Even carefully designed algorithms only mitigate the performance degradation caused by long sequences. Instead, we find that proximal multimodal epigenomic signals near target genes prove more essential. Hence we focus on how to better integrate these signals, which has been overlooked. We find that different signal types serve distinct biological roles, with some directly marking active regulatory elements while others reflect background chromatin patterns that may introduce confounding effects. Simple concatenation may lead models to develop spurious associations with these background patterns. To address this challenge, we propose Prism, a framework that learns multiple combinations of high-dimensional epigenomic features to represent distinct background chromatin states and uses backdoor adjustment to mitigate confounding effects. Our experimental results demonstrate that proper modeling of multimodal epigenomic signals achieves state-of-the-art performance using only short sequences for gene expression prediction.

Figures (6)

• Figure 1: (a) Long-range regulatory interactions through chromatin looping. (b) Current long-sequence models suffer from technical limitations. (c) Multimodal epigenomic signals provide cell-type specific regulatory information. (d) Performance of Seq2Exp su2025learning and Caduceus caduceus with varying input sequence lengths. (e) Different signals show varying contributions. (f) Performance degradation when specific signals are removed during testing from a model trained with all signals.
• Figure 2: Shortening input length at test time.
• Figure 3: The SCM.
• Figure 4: Architecture of Prism. Epigenomic signals $S$ are processed by two encoders: a signal encoder $g_{\theta}$ extracts high-dimension epigenomic features $H$, while a confounder encoder $g_{\omega}$ learns $n$ distinct weights representing the confounder $C$. A final predictor $h_{\phi}$ uses these weighted features along with the DNA sequence $X$ to make a prediction.
• Figure 5: Visualization of learned confounder weights ($a_1, a_2$) for three sampled genes.