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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Tran, Hoai-Nhan Nguyen, Phuc-Xuan-Quynh Peng, Xiaoqing Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

High-throughput biological and large-scale experiments for protein-protein interaction (PPI) identification provides valuable information about PPI networks but are time-consuming and limited in determining PPI between different species. We propose the integration of deep learning with feature fusion for PPI prediction, combining handcrafted features and protein sequence embedding for protein sequence representation. Our proposed method is evaluated on the Yeast full, Yeast core, Human, and eight independent datasets. The experimental results show that our method achieves 95.8% accuracy on the Yeast core, 99.2% accuracy on Human, and 100% accuracy on eight independent datasets. We also perform extensive comparisons with other existing outstanding methods and demonstrate the superior ability of the proposed method. © 2022 IEEE.

关键词： Yeast

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ASNet: An Adversarial Sparse Network for Multi-task Biomedical Named Entity Recognition

ASNet: An Adversarial Sparse Network for Multi-task Biomedic...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Duan, Junwen Guo, Huai Zeng, Min Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

Biomedical named entity recognition (BioNER) is to extract entities, such as genes and proteins, from biomedical texts, where there is often a lack of high-quality training data. Recent work addresses this issue by multi-task learning with multiple datasets. However, these methods are usually over-parameterized, and some even suffer from negative transfer issues. To address above problems, we propose adversarial sparse sharing mechanism, which trains a sparsely shared encoder on multiple tasks with both task-agnostic and task-specific subnetworks. With adversarial training, we guide the task-agnostic subnetwork to learn shared task-invariant features and the task-specific subnetwork to learn task-dependent features. For a particular task, only the shared and its subnetwork are activated, which greatly reduces the number of parameters and avoids interference among tasks. Experimental results on 15 benchmark BioNER datasets show that our proposed method outperforms or is competitive with baseline methods with fewer parameters. Our code is released at: https://***/CSU-NLP-Group/ASNet © 2022 IEEE.

关键词： Natural language processing systems

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ViTRMSE: a three-dimensional RMSE scoring method for protein-ligand docking models based on Vision Transformer

ViTRMSE: a three-dimensional RMSE scoring method for protein...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Guo, Linyuan Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

Protein-ligand interactions (PLIs) play important roles in cellular activities and drug discovery. Due to the technical difficulty and high cost of experimental methods, there is considerable interest in the development of computational approaches, such as protein-ligand docking, to decipher PLI patterns. One of the most important and difficult aspects of protein-ligand docking is recognizing near-native conformations from a set of decoys, but unfortunately, traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. We present a new deep learning-based scoring function for ranking protein-ligand docking models based on Vision Transformer(ViT), named ViTRMSE. To recognize near-native conformations from a set of decoys, ViTRMSE voxelizes the protein-ligand interactional pocket into a 3D grid labeled by the occupancy contribution of atoms in different physicochemical classes. Benefiting from the Vision Transformer architecture, ViTRMSE can effectively capture the subtle differences between spatially and energetically favorable near-native conformations and unfavorable non-native decoys without needing extra information. ViTRMSE is extensively evaluated on diverse test sets including PDBbind2019 and CASF2016, and obtains significant improvements over existing methods in terms of RMSE, R and docking power. © 2022 IEEE.

关键词： Proteins

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Local-Global Spatio-Temporal Encoding with Adaptive Brain Region Signal Segmentation for Mild Cognitive Impairment Detection

Local-Global Spatio-Temporal Encoding with Adaptive Brain Re...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Jie Wu, Luohuang Kuang, Hulin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410000 China

ISBN: (纸本)9798350386226

Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) has been widely used in Mild Cognitive Impairment (MCI) detection. However, most existing methods rely on fixed sliding windows to capture the temporal variations in brain connectivity and fail to model temporal features effectively. To address these limitations, we propose a spatio-temporal graph Transformer framework. First, we segment rs-fMRI data into distinct segments using the multivariate Gaussian distribution of brain region signals and remove redundant segments based on cumulative Jensen-Shannon divergence. Next, we extract features for each brain region and time point within segments, using these temporal features to guide spatial feature aggregation. A position encoder is designed to leverage temporal features across segments to capture both local and global spatio-temporal information. Finally, we employ a population graph framework as the classifier, with spatio-temporal features and demographic data of each subject forming the edge weights, to output the detection results. Our method achieves 92.80%, 81.89%, and 82.80% accuracy in MCI detection, early MCI detection, and late MCI detection tasks on the ADNI3 and ADNI2 datasets, respectively, outperforming existing methods. © 2024 IEEE.

关键词： Magnetic resonance imaging

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LDAGSO: Predicting 1ncRNA-Disease Associations from Graph Sequences and Disease Ontology via Deep Learning techniques

LDAGSO: Predicting 1ncRNA-Disease Associations from Graph Se...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Awn, Norah Saeed Li, Yiming Zhao, Baoying Zeng, Min Li, Min Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

Recent studies have confirmed the significant effects of long non-coding RNAs (1ncRNAs) in understanding the mechanism of diseases. Because of the relatively small number of validated associations between 1ncRNAs and diseases, and previous computational methods have limited performance without capturing important features of sequences and ontology information, we developed LDAGSO, a novel deep learning framework to predict 1ncRNA and disease associations from 1ncRNA sequences and disease ontology. For 1ncRNA sequences, we converted them into graph structure based on k-mer technique and de Bruijn graph, and captured high-level features of the graph using graph convolutional networks. For diseases, we extracted ontology term paths from the disease ontology tree, and treated them as sentences to obtain their feature representation using Bidirectional Encoder Representations from Transformers (BERT) technique. Finally, these two kinds of features were fed into a fully connected layer to perform the task of association prediction between 1ncRNAs and diseases. According to the results, our approach provides state-of-the-art results when evaluated by leave-one-out cross-validation. © 2022 IEEE.

关键词： Forecasting

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Improving Medical Image Denoising via a Lightweight Plug-and-play Module

Improving Medical Image Denoising via a Lightweight Plug-and...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Ma, Lei Kuang, Hulin Liu, Jin Shen, Chengchao Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Medical image denoising, as a part of medical image processing, is significant for the assessment and diagnosis of diseases. To improve the medical image denoising performance of existing deep learning methods, we propose a lightweight plug-and-play module (LP2M) with low complexity, which can be plugged before current image denoising methods. Specifically, the proposed LP2M contains three stacked Convolutional Neural Network (CNN) based blocks: a image receptor block, an adaptive receptive field selection block and a high-low frequency processing block. The image receptor block can perceive color or grayscale images and perform preliminary processing. The adaptive receptive field selection block includes two parallel paths with different receptive fields (i.e., convolution kernel sizes) and the adaptive weighting operation, which can process multi-scale information in the image. The high-low frequency processing block consists of a low frequency pathway using convolutional layers with large kernel sizes, and a high frequency pathway using covolutional layers with small kernel sizes, which can process the low and high frequency components in images. Extensive validation experiments are performed on five state-of-the-art denoising methods on multiple medical image datasets for three different medical image denoising tasks (X-ray image denoising, magnetic resonance image denoising and dermoscopic image denoising). Experimental results show that our proposed LP2M can effectively improve the results of these five state-of-the-art methods for three denoising tasks with only increasing 0.996K parameters and 63.112M FLOPs, and it can provide potential direction for improving image denoising. © 2023 IEEE.

关键词： adaptive receptive field selection high-low frequency processing image receptor medical image denoising plug-and-play

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Predicting of microbe-drug associations via a pre-completion-based label propagation algorithm

Predicting of microbe-drug associations via a pre-completion...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Haochen Duan, Guihua Yang, Botu Li, Suning Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9781665468190

Identifying microbe-drug associations is important to systematically understand a drug's mechanism of action in the therapeutic application. Since identifying microbe-drug associations is expensive and time-consuming via biological experiments, in this study, we propose a Pre-completion-based label Propagation (PLP) method (called PLPMDA) to predict microbe-drug associations based on the multi-type similarities. To obtain richer information of drugs and microbes, we calculate drug chemical structure similarity, drug Anatomical Therapeutic Chemical (ATC) code similarity, microbe functional similarity, microbe sequence similarity and Gaussian Interaction Profile (GIP) kernel similarities of microbes and drugs, and then introduce a non-linear similarity fusion method. Comparing baseline methods, our advantage lies in performing an effective pre-completion step on the initial association matrix from the drug-related and microbe-related information and does not rely on the known drug-microbe associations, which can accelerate the design and discovery of the new drugs. The computational experiment results demonstrate that our proposed approach PLPMDA achieves significantly higher performance than the comparative methods in de novo and cross-validation experiments. © 2022 IEEE.

关键词： Forecasting

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Personalized federated adaptive regularization for heterogeneous medical image classification: COVID-19 CT results

Personalized federated adaptive regularization for heterogen...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yuan Qu, Zhe Wang, Shu Sheng, Tao Yu, Xiaoqing Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China Bank of Sanxiang Hunan Changsha China

ISBN: (纸本)9798350337488

During outbreaks of large infectious diseases like COVID-19, there is a strain on healthcare resources worldwide. To alleviate the burden on healthcare workers during the initial stages of the outbreak, there is an urgent need for the development of automated tools. Federated learning offers a privacypreserving solution to the challenge that limited annotated data within single healthcare facilities. However, existing federated aggregation strategies cannot adapt to the real-world medical image data problem of mixed heterogeneous. This paper introduces Personalized Federated Adaptive Regularization (pFedAR), an adaptive framework for federated learning that effectively utilizes multi-site COVID-19 CT datasets with mixed heterogeneous of distribution discrepancies. To solve the mixed distribution of label skew and feature shift, we propose a two-stage adaptive regularization. In the client training stage, we use the balance loss term to balance the COVID-19 clients with missing label. In the aggregation stage, we correct the client gradient conflict. Our method is developed and evaluated using eight real-world COVID-19 diagnosis datasets composed of CT images. Extensive experiments demonstrate the consistent improvement achieved by our method across the datasets. © 2023 IEEE.

关键词： federated learning heterogeneity medical image personalization

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KEXNet:A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection

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Big Data Mining and Analytics 2024年第4期7卷 1187-1198页

作者： Quan Yan Junwen Duan Jianxin Wang School of Computer Science and Engineering and Hunan Provincial Key Lab on Bioinformatics Central South UniversityChangsha 410083China

Automated diagnosis of chest X-rays is pivotal in radiology,aiming to alleviate the workload of *** methods primarily rely on visual features or label dependence,which is a limitation in detecting nuanced or rare *** address this,we present KEXNet,a pioneering knowledge-enhanced X-ray lesion detection *** employs a unique strategy akin to expert radiologists,integrating a knowledge graph based on expert annotations with an interpretable graph learning *** novel method combines object detection with a graph neural network,facilitating precise local lesion *** global lesion detection,KEXNet synergizes knowledge-enhanced local features with global image features,enhancing diagnostic *** evaluations on three benchmark datasets demonstrate that KEXNet outshines existing models,particularly in identifying small or infrequent ***,on the Chest ImaGenome dataset,KEXNet’s AUC for local lesion detection surpasses 8.9%compared to the state-of-the-art method AnaXNet,showcasing its potential in revolutionizing automated chest X-ray diagnostics.

关键词： multi-label chest X-ray classification object detection knowledge graph learning

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Global Contrastive Learning with High-Quality Data in Large Vision-Language Models for Pathological Question Answering

Global Contrastive Learning with High-Quality Data in Large ...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Kuang, Hulin Liu, Suoni Zhang, Haojie Qu, Zhe Kan, Shichao Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350386226

Pathological question answering (PQA) is vital in computational pathology, as it involves interpreting pathological images and answering questions posed by humans. This interaction offers an effective means of engaging with users and enhancing the understanding of pathology-related information. Recent methods developed using large vision-language models (LVLMs), such as QUILT-LLAVA, have made significant progress in advancing PQA. However, existing models, such as those using the QUILT-1M dataset, neglect the quality of the training set during the fine-tuning stage, leading to sub-optimal performance. We recognize that high-quality training data can significantly enhance model performance. Therefore, we design a model-based data filtering strategy to remove images with obvious impurities from the instruction fine-tuning dataset. The filtered high-quality images are then used to fine-tune the model. Additionally, most existing vision-language alignment strategies focus primarily on aligning local features through next-word prediction, leading to a relatively homogeneous granularity in inter-modal alignment. To address this issue, we propose a global-wise alignment module, which introduces global-level contrastive learning during the pretraining stage to establish multi-granularity alignment between pathological images and language descriptions. Based on the above two processes, we design our method, named Global Contrastive Learning with High-Quality Data (GCL-HQD) for pathological question answering in LVLMs. Extensive experiments on two types of experimental settings demonstrate the effectiveness of the GCL-HQD method. © 2024 IEEE.

关键词： Contrastive Learning

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