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Yingfan Ma

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3 papers

2 author rows

JBHI Journal 2026 Journal Article

Enhancing Protein Structure Learning Using a Size-Guided Conditional Mixture-of-Experts

Mingzhi Yuan
Ao Shen
Siqi Yin
Yingfan Ma
Qiao Huang
Manning Wang

In recent years, deep learning on protein structures has attracted widespread attention, as structures determine proteins’ function. A series of structure-based protein property prediction methods have been proposed, achieving remarkable performance. However, these methods often neglect the importance of the protein size and fail to fully leverage it, leading to biases toward certain sizes and suboptimal overall performance. To address this issue, we propose a protein size-guided conditional mixture-of-experts for improving deep learning on protein structures. It can adaptively activate the sub-networks with the guidance of protein sizes and network features. Its flexible combinations of sub-networks help mitigate biases toward certain protein sizes, while the deliberate incorporation of protein size guidance enables the network to effectively capture both universal and size-specific characteristics, resulting in more accurate predictive performance. Based on it, we propose a framework for protein property prediction and benchmark it on eight tasks with two representation forms of proteins and three different dataset splits, a total of forty-eight tests. Experiments show that our method can be seamlessly integrated into numerous existing models and achieve performance improvement across tasks under almost all settings. More importantly, our experiments reveal that although often overlooked, protein size serves as an important prior knowledge in deep learning on protein structures.

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ICML Conference 2025 Conference Paper

Drug-TTA: Test-Time Adaptation for Drug Virtual Screening via Multi-task Meta-Auxiliary Learning

Ao Shen
Mingzhi Yuan
Yingfan Ma
Jie Du
Qiao Huang
Manning Wang

Virtual screening is a critical step in drug discovery, aiming at identifying potential drugs that bind to a specific protein pocket from a large database of molecules. Traditional docking methods are time-consuming, while learning-based approaches supervised by high-precision conformational or affinity labels are limited by the scarcity of training data. Recently, a paradigm of feature alignment through contrastive learning has gained widespread attention. This method does not require explicit binding affinity scores, but it suffers from the issue of overly simplistic construction of negative samples, which limits their generalization to more difficult test cases. In this paper, we propose Drug-TTA, which leverages a large number of self-supervised auxiliary tasks to adapt the model to each test instance. Specifically, we incorporate the auxiliary tasks into both the training and the inference process via meta-learning to improve the performance of the primary task of virtual screening. Additionally, we design a multi-scale feature based Auxiliary Loss Balance Module (ALBM) to balance the auxiliary tasks to improve their efficiency. Extensive experiments demonstrate that Drug-TTA achieves state-of-the-art (SOTA) performance in all five virtual screening tasks under a zero-shot setting, showing an average improvement of 9. 86% in AUROC metric compared to the baseline without test-time adaptation.

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AAAI Conference 2024 Conference Paper

Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification

Yingfan Ma
Xiaoyuan Luo
Kexue Fu
Manning Wang

Pathological images play a vital role in clinical cancer diagnosis. Computer-aided diagnosis utilized on digital Whole Slide Images (WSIs) has been widely studied. The major challenge of using deep learning models for WSI analysis is the huge size of WSI images and existing methods struggle between end-to-end learning and proper modeling of contextual information. Most state-of-the-art methods utilize a two-stage strategy, in which they use a pre-trained model to extract features of small patches cut from a WSI and then input these features into a classification model. These methods can not perform end-to-end learning and consider contextual information at the same time. To solve this problem, we propose a framework that models a WSI as a pathologist's observing video and utilizes Transformer to process video clips with a divide-and-conquer strategy, which helps achieve both context-awareness and end-to-end learning. Extensive experiments on three public WSI datasets show that our proposed method outperforms existing SOTA methods in both WSI classification and positive region detection.

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