Author name cluster

Jiamin Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

JBHI Journal 2025 Journal Article

Drug Repositioning Based on Expert Knowledge Augmented Graph Neural Network

Zhenpeng Wu
Cheng Yan
Jiamin Chen
Siyang Xiao
Jianliang Gao

Drug repositioning is critical in accelerating drug discovery, which identifies new indications for existing drugs by modeling drug–disease associations. Compared to traditional methods, graph neural networks (GNNs) have recently gained widespread attention due to their ability to effectively aggregate information from neighboring nodes in drug–disease heterogeneous graphs. The GNN-based methods need effective node embeddings for information aggregation. However, they generate the node embeddings by random initialization, rather than incorporating the high-quality expert knowledge involving biological mechanisms in the databases. This limits their capacity to generate interpretable node embeddings aligned with expert knowledge. To bridge this gap, we develop a novel framework dubbed DReKGNN ( D rug Re positioning based on expert K nowledge augmented G raph N eural N etwork). To be specific, DReKGNN will first adopt large language models (LLMs) as a semantic bridge between expert knowledge and GNNs. To ensure the accuracy of expert knowledge, DReKGNN does not rely on prompt templates in LLMs to generate knowledge descriptions for drugs and diseases. Instead, it extracts expert knowledge directly from the DrugBank and OMIM databases. The effective node embeddings with interpretable semantic information will be generated from expert knowledge descriptions involving biological mechanisms by LLMs. Then, we demonstrate that there is a need to mitigate noise when LLM node embeddings serving drug repositioning prediction tasks. Considering this design need, we integrate GNNs with LLM node embeddings by a mean aggregation strategy. The experiment results of performance comparison and case study show the effectiveness of DReKGNN in predicting drug–disease associations. The code is available at https://github.com/csubigdata-Organization/DReKGNN.

Details DOI

AAAI Conference 2025 Conference Paper

InstructOCR: Instruction Boosting Scene Text Spotting

Chen Duan
Qianyi Jiang
Pei Fu
Jiamin Chen
Shengxi Li
Zining Wang
Shan Guo
Junfeng Luo

In the field of scene text spotting, previous OCR methods primarily relied on image encoders and pre-trained text information, but they often overlooked the advantages of incorporating human language instructions. To address this gap, we propose InstructOCR, an innovative instruction-based scene text spotting model that leverages human language instructions to enhance the understanding of text within images. Our framework employs both text and image encoders during training and inference, along with instructions meticulously designed based on text attributes. This approach enables the model to interpret text more accurately and flexibly. Extensive experiments demonstrate the effectiveness of our model and we achieve state-of-the-art results on widely used benchmarks. Furthermore, the proposed framework can be seamlessly applied to scene text VQA tasks. By leveraging instruction strategies during pre-training, the performance on downstream VQA tasks can be significantly improved, with a 2.6% increase on the TextVQA dataset and a 2.1% increase on the ST-VQA dataset. These experimental results provide insights into the benefits of incorporating human language instructions for OCR-related tasks.

PDF Details DOI

JBHI Journal 2024 Journal Article

AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural Network

Jianliang Gao
Zhenpeng Wu
Raeed Al-Sabri
Babatounde Moctard Oloulade
Jiamin Chen

Drug–drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, researchers usually treat the molecular structure of drugs as a molecule graph. Then, previous studies can use the handcrafted graph neural network (GNN) model to learn the molecular graph representations of drugs for DDI prediction. However, in the field of bioinformatics, manually designing GNN architectures for specific molecular structure datasets is time-consuming and depends on expert experience. To address this problem, we propose an automatic drug–drug interaction prediction method named AutoDDI that can efficiently and automatically design the GNN architecture for drug–drug interaction prediction without manual intervention. To this end, we first design an effective search space for drug–drug interaction prediction by revisiting various handcrafted GNN architectures. Then, to efficiently and automatically design the optimal GNN architecture for each drug dataset from the search space, a reinforcement learning search algorithm is adopted. The experiment results show that AutoDDI can achieve the best performance on two real-world datasets. Moreover, the visual interpretation results of the case study show that AutoDDI can effectively capture drug substructure for drug–drug interaction prediction.

Details DOI

ICML Conference 2024 Conference Paper

GiLOT: Interpreting Generative Language Models via Optimal Transport

Xuhong Li 0002
Jiamin Chen
Yekun Chai
Haoyi Xiong

While large language models (LLMs) surge with the rise of generative AI, algorithms to explain LLMs highly desire. Existing feature attribution methods adequate for discriminative language models like BERT often fail to deliver faithful explanations for LLMs, primarily due to two issues: (1) For every specific prediction, the LLM outputs a probability distribution over the vocabulary–a large number of tokens with unequal semantic distance; (2) As an autoregressive language model, the LLM handles input tokens while generating a sequence of probability distributions of various tokens. To address above two challenges, this work proposes GiLOT that leverages Optimal Transport to measure the distributional change of all possible generated sequences upon the absence of every input token, while taking into account the tokens’ similarity, so as to faithfully estimate feature attribution for LLMs. We have carried out extensive experiments on top of Llama families and their fine-tuned derivatives across various scales to validate the effectiveness of GiLOT for estimating the input attributions. The results show that GiLOT outperforms existing solutions on a number of faithfulness metrics under fair comparison settings. Source code is publicly available at https: //github. com/holyseven/GiLOT.

Details

ECAI Conference 2024 Conference Paper

Relation Time-Aware Heterogeneous Dynamic Graph Neural Networks

Yili Wang 0005
Jiamin Chen
Qiutong Li
Changlong He
Jianliang Gao

Heterogeneous dynamic graph neural networks (HDGNNs) are effective methods for processing heterogeneous temporal graphs (HTGs), which serve as ubiquitous data structures in real-world scenarios. The previous HDGNN paradigm obtains representations of future target nodes by mining the spatial heterogeneity and temporal dependence of node attributes, ignoring the learning of relation temporal dependence. However, through experience, we find that the learning of relation temporal dependence, which describes the evolving trends in the importance of neighbors under a certain relation, is beneficial for representation learning of HTGs. To bridge this gap, we propose a novel end-to-end heterogeneous temporal graph learning paradigm called Relation Time-aware Heterogeneous Dynamic Graph Neural Networks (ReTag). Compared to previous HDGNNs, ReTag extracts the temporal dependence of relations from historical relation information and the evolving node attributes to drive subsequent spatio-temporal representation learning. As far as we know, ReTag is the first attempt to perform learning of the temporal dependence of relation, which can generate a more effective representation for different downstream tasks of HTGs. The experimental results of different downstream tasks of HTGs based on multiple benchmark datasets show that ReTag can obtain obvious performance advantages compared with the sota baseline method.

Details

NeurIPS Conference 2023 Conference Paper

$\mathcal{M}^4$: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models

Xuhong Li
Mengnan Du
Jiamin Chen
Yekun Chai
Himabindu Lakkaraju
Haoyi Xiong

While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for various models and the lack of ground-truth explanations. This paper introduces an XAI benchmark named $\mathcal{M}^4$, which allows evaluating various input feature attribution methods using the same set of faithfulness metrics across multiple data modalities (images and texts) and network structures (ResNets, MobileNets, Transformers). A taxonomy for the metrics has been proposed as well. We first categorize commonly used XAI evaluation metrics into three groups based on the ground truth they require. We then implement classic and state-of-the-art feature attribution methods using InterpretDL and conduct extensive experiments to compare methods and gain insights. Extensive experiments have been conducted to provide holistic evaluations as benchmark baselines. Several interesting observations are noticed for designing attribution algorithms. The implementation of state-of-the-art explanation methods and evaluation metrics of $\mathcal{M}^4$ is publicly available at \url{https: //github. com/PaddlePaddle/InterpretDL}.

PDF Details

TMLR Journal 2023 Journal Article

Beyond Intuition: Rethinking Token Attributions inside Transformers

Jiamin Chen
Xuhong Li
Lei Yu
Dejing Dou
Haoyi Xiong

The multi-head attention mechanism, or rather the Transformer-based models have always been under the spotlight, not only in the domain of text processing, but also for computer vision. Several works have recently been proposed around exploring the token attributions along the intrinsic decision process. However, the ambiguity of the expression formulation can lead to an accumulation of error, which makes the interpretation less trustworthy and less applicable to different variants. In this work, we propose a novel method to approximate token contributions inside Transformers. We start from the partial derivative to each token, divide the interpretation process into attention perception and reasoning feedback with the chain rule and explore each part individually with explicit mathematical derivations. In attention perception, we propose the head-wise and token-wise approximations in order to learn how the tokens interact to form the pooled vector. As for reasoning feedback, we adopt a noise-decreasing strategy by applying the integrated gradients to the last attention map. Our method is further validated qualitatively and quantitatively through the faithfulness evaluations across different settings: single modality (BERT and ViT) and bi-modality (CLIP), different model sizes (ViT-L) and different pooling strategies (ViT-MAE) to demonstrate the broad applicability and clear improvements over existing methods.

PDF Details