2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献

{"title":"Selecting Reliable Instances from ImageNet for Medical Image Domain Adaptation","authors":"Ying Lv, Xiaodong Yue, Zhikang Xu, Yufei Chen, Zihao Li","doi":"10.1109/BIBM55620.2022.9995146","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995146","url":null,"abstract":"Pre-training deep learning models on ImageNet and transferring the models to medical image applications facilitate to improve the medical image analysis and reduce the need for labeled medical image data. However, some images from ImageNet may be fundamentally different from medical images in feature representation and lead to the negative transfer effects. To deal with this issue, we propose a novel strategy based on evidence theory to select reliable instances from ImageNet for medical image domain adaptation. Specifically, we formulate an evidential mass function to measure the ignorance and reliability of the images from ImageNet with respect to the classification tasks of medical images. Through selecting reliable instances with low ignorance degree from ImageNet, we can enhance the transfer performances of deep neural networks in medical image domain adaptation. Moreover, the proposed data selection strategy is independent of specific learning algorithm and can be viewed as a common preprocessing technique. Numerical experiments on tomography images, X-Ray images, and ultrasound images are given to comprehensively demonstrate the effectiveness of the selection strategy.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116826772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction","authors":"Mei Li, Sihan Xu, Xiangrui Cai, Zhong Zhang, Hua Ji","doi":"10.1109/BIBM55620.2022.9995372","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995372","url":null,"abstract":"Effective drug-target binding affinity (DTA) prediction is essential for drug discovery and development. The development of machine learning techniques considerably advances it. However, the cold-start problems in DTA prediction are still under-explored, which significantly degrades prediction performances on novel drugs and novel targets. In this paper, we propose a contrastive meta-learning (CML) framework to address these issues. We define drug-anchored tasks and target-anchored tasks, which enables the employment of meta-learning to accumulate common knowledge from various tasks so as to adapt to new tasks faster and better. Besides, we utilize a task inequality loss to measure task disparities and enhance model sensitivities to new tasks. We also propose a contrastive learning block (CLB) to explore correlations among drug-target pairs across tasks, which facilitates DTA prediction performance improvements. We compare CML with various baselines on two benchmarks and comparison results show that CML outperforms or achieves competitive results to its competitors.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129574226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Mining and Analysis of The Medical Records of Chinese Medical Master Li Zhenhua in The Treatment of Spleen and Stomach Diseases","authors":"Teng Teng, Xuebo Li","doi":"10.1109/BIBM55620.2022.9995038","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995038","url":null,"abstract":"Objective: To explore and analyze the experience and potential rules of Chinese medical master Li Zhenhua in the treatment of spleen and stomach diseases [1]. Methods: The medical records of 75 patients with spleen and stomach diseases treated by Mr. Li Zhenhua have been collected, and the data of initial diagnosis were entered into the database. After data standardization, IBM SPSS Statistics 26 and IBM SPSS Modeler 18 statistical software were used to process the data and explore the internal diagnosis and treatment rules. Results: Among the 75 medical records of spleen and stomach diseases, the most common syndromes were spleen and stomach deficiency cold syndrome, liver depression and spleen deficiency syndrome, spleen and kidney Yang deficiency syndrome, commonly used herbs are licorice, atractylodes macrocephala, poria, tangerine peel, etc. The commonly used treatment rules were invigorated the spleen, harmonized the stomach and soothed the liver. The prescriptions used more often were Xiangsha Wenzhong Decoction, Xiangsha Liujunzi Decoction, Buzhong Yiqi Decoction, Dingxiang Shidi Decoction and Chaihu Shugan Decoction. The commonly used additional herbs were Lindera aggregata, xiangfu, cassia twig and so on. Among all the used herbs, the herb combinations with the highest support were atractylodes macrocephala → poria and poria → atractylodes macrocephala, and the herb combinations with the highest confidence were poria → atractylodes macrocephala, poria and licorice → atractylodes macrocephala. Among the treatment rules, the combination of treatment rules with the highest support were harmonized the stomach → invigorated the spleen and invigorated the spleen → harmonized the stomach, the combination of treatment rules with the highest confidence was tonified Qi → invigorated the spleen. Among all the additional herbs, the herb combination with the highest support and the highest confidence was malt → hawthorn. Conclusion: The main treatment rules of Mr. Li Zhenhua in treating spleen and stomach diseases were invigorated the spleen, soothed the liver and harmonized the stomach. Xiangsha Wenzhong Decoction is commonly used in the treatment of spleen and stomach diseases, and the syndrome of deficiency and cold is common, therefore, Mr. Li Zhenhua often adds warm herbs combined with tonic herbs to achieve the purpose of treatment.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129587515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting miRNA-disease associations via multi-channel graph convolutional networks","authors":"Haoran Zheng, Qiu Xiao, Jiancheng Zhong","doi":"10.1109/BIBM55620.2022.9994981","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9994981","url":null,"abstract":"Extensive research evidence shows that variation and dysregulation of microRNAs(miRNAs) are important causes of disease, and therefore the study of miRNA-disease associations has important theoretical and applied implications in the field of human disease research and treatment. Based on the time and cost of validating miRNA-disease associations in traditional medicine clinical experiments, using multiple biological datasets to predict potential miRNA-disease associations (MDAs) has become a hot topic in the field of biological research in recent years. This paper develops a novel model of MDA-RGCN based on a multi-channel graph convolutional network and graph attention for MDAs prediction. Based on graph theory, this study treats MDAs prediction as a node classification task. To learn the topology and various interactions between feature graph nodes of various strengths, we employ two independent graph attention networks, which increases training efficiency and accuracy. In order to learn information that is shared by both graphs, we employ a GCN with a shared weight matrix simultaneously. Comprehensive experiments reveal that the prediction performance of MDA-RGCN excels other more sophisticated models for MDAs prediction. Furthermore, we further confirmed the predictive ability of MDA-RGCN to identify potential disease-related miRNAs by selecting two human diseases for case study.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124562163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of virus-receptor interactions based on multi-view learning and link prediction","authors":"Ling-ling Zhu, Kai Zheng, Guihua Duan, Jianxin Wang","doi":"10.1109/BIBM55620.2022.9995553","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995553","url":null,"abstract":"Receptor-binding is the first step of viral infection. Discovering potential virus-receptor interactions may give insight into potential strategies for treating viral infectious diseases. Most of computational methods for the virus-receptor interaction prediction are mainly based on sequence information. They neither makes effective use of structure information nor effectively handles with missing values of multiple similarities. In addition, the Link Prediction via linear optimization (LP) only uses contribution of neighbors of a node and ignores contribution of neighbors of another node on the network link. In this article, we present a virus-receptor interaction prediction method (MVLP) based on Multi-View learning and LP via contributions of all neighbors of two nodes on the network link. First, missing values of the receptor secondary structure similarity, the receptor conserved domain secondary structure similarity, the viral protein secondary structure similarity, the viral protein sequence similarity and the viral genome sequence similarity are updated by the gaussian radial basis function (GRB). To improve these similarities, we fuse updated and initial values of each similarity with multi-view learning, respectively. Next, three virus values and receptor similarities are integrated into the comprehensive virus and receptor similarity by the averaging method, respectively. Finally, LP based on contribution of neighbors of two nodes is presented for the virus-receptor interaction prediction. To evaluate the ability of MVLP, we compare MVLP with four related methods in 10 fold Cross-Validation (10CV). Computational results indicate that an average Area Under Curve (AUC) values of MVLP on viralReceptor sup and viralReceptor are 0.9427 and 0.9444, respectively, which are superior to other related methods. Furthermore, a case study also demonstrates the ability of MVLP in practice.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129902971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CR-GAT: Consistency Regularization Enhanced Graph Attention Network for Semi-supervised EEG Emotion Recognition","authors":"Jiyao Liu, Hao Wu, Li Zhang","doi":"10.1109/BIBM55620.2022.9994941","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9994941","url":null,"abstract":"Electroencephalogram (EEG) emotion recognition has become a research focus in the field of human-computer interaction (HCI). However, the process of EEG signal collection requires lots of expertise, which makes the amount of labeled EEG data very limited. It constrains the performance of supervised methods which require large amounts of annotated data in some sense. Self-supervised learning paradigm, which aims to train models that do not require any labeled samples can make full use of a large amount of unlabeled EEG samples. But a drawback is that they fall short of learning class discriminative sample representations since no labeled information is utilized during training. To solve the above problem, we propose a semi-supervised model, named consistency regularization enhanced graph attention network (CR-GAT) for EEG emotion recognition. The CR-GAT mainly consists of three modules, namely the feature extraction and fusion (FEF) module, the feature graph building and augment (GBA) module as well as the consistency regularization (CR) module. Specifically, t he F EFm odule is to extract task-specific EEG features and highlight the most valuable features from the EEG signals. The GBA module is to build a sample-related graph representation of the EEG feature set. The CR module, which draws support samples from labeled samples and anchor samples from the entire sample set, intends to minimize the difference between the predicted class distributions from different graphs constructed by multi-views of the sample set to push samples that belong to the same class to be grouped together. We conduct our experiment on three real-world datasets, the experimental results show the method surpasses most of competitive models.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130681036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainability-guided Mathematical Model-Based Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy","authors":"Tao Peng, Yiyun Wu, Jing Zhao, Bo Zhang, Jin Wang, Jing Cai","doi":"10.1109/BIBM55620.2022.9995677","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995677","url":null,"abstract":"Accurate segmentation of the prostate is important to image-guided prostate biopsy and brachytherapy treatment planning. However, the incompleteness of prostate boundary increases the challenges in the automatic ultrasound prostate segmentation task. In this work, an automatic coarse-to-fine framework for prostate segmentation was developed and tested. Our framework has four metrics: first, it combines the ability of deep learning model to automatically locate the prostate and integrates the characteristics of principal curve that can automatically fit the data center for refinement. Second, to well balance the accuracy and efficiency of our method, we proposed an intelligent determination of the data radius algorithm-based modified polygon tracking method. Third, we modified the traditional quantum evolution network by adding the numerous-operator scheme and global optimum search scheme for ensuring population diversity and achieving the optimal model parameters. Fourth, we found a suitable mathematical function expressed by the parameters of the machine learning model to smooth the contour of the prostate. Results on the multiple datasets demonstrate that our method has good segmentation performance.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123957870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-task Learning with Consistent Prediction for Efficient Breast Ultrasound Tumor Detection","authors":"Kaiwen Yang, Aiga Suzuki, Jiaxing Ye, H. Nosato, Ayumi Izumori, H. Sakanashi","doi":"10.1109/BIBM55620.2022.9995444","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995444","url":null,"abstract":"Segmentation and classification a re h ighly correlated tasks in tumor detection from breast ultrasound images. Recent studies have successfully applied multi-task learning to breast ultrasound image analysis to explore the correlation between tasks. However, there exists potential inconsistency between individual tasks that critically affect the overall performance of breast ultrasound image analysis. Therefore, this study designs a consistency branch for harmonizing the segmentation and classification t ask 0 ptimization. T he c onsistency b ranch characterizes the outputs of individual task-specific models to maintain consistency during training, thereby generating highly consistent results. Specifically, the consistency branch outputs a consistency probability while determining the inconsistency types predicted by both tasks. Subsequently, the segmentation and classification loss weights are reconciled using consistency probabilities based on the inconsistent prediction behavior for each sample, thus constraining the two tasks to produce consistent predictions close to the ground truth. The evaluation using private and public breast ultrasound image datasets indicates that the proposed method can effectively remedy the inconsistent predictions between tasks for improved computerized breast ultrasound image analysis.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120964173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MFCNet: Multi-Feature Fusion Neural Network for Thoracic Disease Classification","authors":"Kai Chen, Yu Liu, Xuqi Wang, Shanwen Zhang, Chuanghui Zhang","doi":"10.1109/BIBM55620.2022.9995418","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995418","url":null,"abstract":"This paper aims to automatically diagnose thoracic diseases on chest X-ray (CXR) images using convolutional neural networks (CNN). Most existing approaches typically employ a global learning strategy and use CNN with small convolutional kernels for thoracic disease classification. However, irrelevant noisy regions may affect the global learning strategy; small convolutional kernels can only capture fewer discriminant features. To address the above problems, we construct a multi-feature fusion neural network (MFCNet), which can fully use the global and weighted local features. Specifically, the global features are first generated by the global branch. Weighted local features are generated by multiplying the global feature and the heart-lung region mask identified by the Lung-heart Region Generator (LHRG). At last, the fusion branch integrates the global and weighted local features to complement the lost discriminative feature of the global branch and the local branch, thus enabling a better feature presentation for thoracic disease classification. Extensive experiments on the NIH ChestX-ray 14 dataset demonstrate that the MFCNet model achieves superior performance (average AUC=0.844) compared to state-of-the-art methods. Source code is released in https://github.com/Warrior996/MFCNet.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116373556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MoleHD: Efficient Drug Discovery using Brain Inspired Hyperdimensional Computing","authors":"Dongning Ma, Rahul Thapa, Xun Jiao","doi":"10.1109/BIBM55620.2022.9995708","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995708","url":null,"abstract":"In this paper, we propose MoleHD, an efficient learning model based on brain-inspired hyperdimensional computing (HDC) for molecular property prediction. We develop HDC encoders to project SMILES representation of a molecule into high-dimensional vectors that are used for HDC training and inference. We perform an extensive evaluation using 29 classification tasks from 3 widely-used molecule datasets (Clintox, BBBP, SIDER) under three splits methods (random, scaffold, and stratified). By a comprehensive comparison with 8 existing learning models, we show that MoleHD achieves highest ROC-AUC score on random and scaffold splits on average across 3 datasets and achieve second-highest on stratified split. More importantly, MoleHD achieves such performance with significantly reduced computing cost: no back-propagation needed, only around 10 minutes training time using CPU.MoleHD is open-sourced and available at https://github.com/VU-DETAIL/MoleHD.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127771006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}