Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995488
Sanbi Luo
Orthodontics transfer is a new, challenging image-to-image transfer task from malpositioned-teeth images to neat-teeth images. More specifically, it belongs to the image-to-image location transfer, which aims to rearrange the chaotic foreground objects into an orderly layout. In this paper, we conducted further research on image-to-image orthodontics transfer task. Firstly, we studied the similarities and differences between malpositioned-teeth images and their corresponding neat-teeth images and found texture feature similarities between them. Then we analyzed the problems of directly applying the LTGAN method to the orthodontics transfer task and proposed an approach based on the boundary label transfer bridge. Finally, our model’s performance is further improved by employing gray level co-occurrence matrix loss. Moreover, we have augmented the OrthoD datasets to support our method and potential attempts to deal with orthodontics transfer task. The added data is available at https://drive.google.com/drive/folders/1bzoxzi_608SzCVgaABlPAjZVqp6pp7L8?usp=sharing.
{"title":"Image-to-Image Orthodontics Transfer Employing Gray Level CO-Occurrence Matrix Loss","authors":"Sanbi Luo","doi":"10.1109/BIBM55620.2022.9995488","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995488","url":null,"abstract":"Orthodontics transfer is a new, challenging image-to-image transfer task from malpositioned-teeth images to neat-teeth images. More specifically, it belongs to the image-to-image location transfer, which aims to rearrange the chaotic foreground objects into an orderly layout. In this paper, we conducted further research on image-to-image orthodontics transfer task. Firstly, we studied the similarities and differences between malpositioned-teeth images and their corresponding neat-teeth images and found texture feature similarities between them. Then we analyzed the problems of directly applying the LTGAN method to the orthodontics transfer task and proposed an approach based on the boundary label transfer bridge. Finally, our model’s performance is further improved by employing gray level co-occurrence matrix loss. Moreover, we have augmented the OrthoD datasets to support our method and potential attempts to deal with orthodontics transfer task. The added data is available at https://drive.google.com/drive/folders/1bzoxzi_608SzCVgaABlPAjZVqp6pp7L8?usp=sharing.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124403017","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995030
Xinyu Li, Jianhong Cheng, Jin Liu, Hulin Kuang, Chen Shen, Pei Yang, Jianxin Wang
Ki67 is a promising molecular biomarker for the diagnosis of lung adenocarcinoma. However, previous methods to determine Ki67 status often require tumor tissue sampling, which is invasive for patients. This study proposes a multi-view signature fusion network (MVSF), combining deep learning encoded (DLE) signatures, handcrafted radiomics (HCR) signatures, and clinical information to noninvasively predict Ki67 status. Multi-view signatures are combined through a tensor fusion network to obtain potentially high-dimensional signatures. Finally, a cooperative game theory-based approach is applied to quantitatively interpret the contribution of signatures to decision-making. The proposed MVSF is evaluated on a retrospectively collected dataset of 661 patients. Experimental results show that the MVSF achieves encouraging performance, with an area under the receiver operating characteristic curve of 0.80 and an accuracy of 0.78, outperforming several state-of-the-art Ki67 status prediction methods, which implies that our proposed method could provide potential support for Ki67 status prediction.
{"title":"MVSF: Multi-View Signature Fusion Network for Noninvasively Predicting Ki67 Status","authors":"Xinyu Li, Jianhong Cheng, Jin Liu, Hulin Kuang, Chen Shen, Pei Yang, Jianxin Wang","doi":"10.1109/BIBM55620.2022.9995030","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995030","url":null,"abstract":"Ki67 is a promising molecular biomarker for the diagnosis of lung adenocarcinoma. However, previous methods to determine Ki67 status often require tumor tissue sampling, which is invasive for patients. This study proposes a multi-view signature fusion network (MVSF), combining deep learning encoded (DLE) signatures, handcrafted radiomics (HCR) signatures, and clinical information to noninvasively predict Ki67 status. Multi-view signatures are combined through a tensor fusion network to obtain potentially high-dimensional signatures. Finally, a cooperative game theory-based approach is applied to quantitatively interpret the contribution of signatures to decision-making. The proposed MVSF is evaluated on a retrospectively collected dataset of 661 patients. Experimental results show that the MVSF achieves encouraging performance, with an area under the receiver operating characteristic curve of 0.80 and an accuracy of 0.78, outperforming several state-of-the-art Ki67 status prediction methods, which implies that our proposed method could provide potential support for Ki67 status prediction.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117204681","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}
The increasing demand for molecular formula image data leads to formidable pressure for researchers. Most existing image segmentation approaches can not be directly utilized for molecules, and how to improve the coverage fineness and generate a large amount of labeled training data is worthy of further exploration. To this end, we establish a deep learning based molecular formula image segmentation model (DL-MFS). Specifically, we design a shape constraint loss (SCL) function to refine the detection frame position and propose a rule-based molecular formula image data augmentation method for solving the bottleneck problem that the lack of training data. Experimental results demonstrate the effectiveness of the proposed segmentation model.
{"title":"Molecular Formula Image Segmentation with Shape Constraint Loss and Data Augmentation","authors":"Ruiqi Jia, Wentao Xie, Baole Wei, Guanren Qiao, Zonglin Yang, Xiaoqing Lyu, Zhi Tang","doi":"10.1109/BIBM55620.2022.9995506","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995506","url":null,"abstract":"The increasing demand for molecular formula image data leads to formidable pressure for researchers. Most existing image segmentation approaches can not be directly utilized for molecules, and how to improve the coverage fineness and generate a large amount of labeled training data is worthy of further exploration. To this end, we establish a deep learning based molecular formula image segmentation model (DL-MFS). Specifically, we design a shape constraint loss (SCL) function to refine the detection frame position and propose a rule-based molecular formula image data augmentation method for solving the bottleneck problem that the lack of training data. Experimental results demonstrate the effectiveness of the proposed segmentation model.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120879655","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995450
Tao Wang, H Zhao, Yifu Xiao, Hanzi Yang, X. Yin, Yongtian Wang, Bing Xiao, Xuequn Shang, Jiajie Peng
The expression quantitative trait loci (eQTL) analysis has become important for understanding the regulatory function of genomic variants on gene expression in a tissuespecific manner and has been widely applied across species from microbes to mammals. Current eQTL studies mainly focus on the simple one-to-one regulation between variant and gene. Recent research have demonstrated there are also more complex regulatory patterns between eQTLs and genes. However, there is a lack of studies and relevant methods to systematically discover the regulatory patterns between multiple eQTLs and multiple genes. In this regard, this study has proposed a novel computational framework, called eQTLMotif, to discover regulation patterns of eQTLs in a many-to-many manner. This framework mainly consists of two steps: (1) construct a novel eQTL regulatory network by integrating bipartite eQTL network, eQTL mediation effects, and gene regulatory network; (2) perform motif mining through exactly enumerating frequently appeared eQTL regulatory structures. Based on this framework, we for the first time systematically investigated the eQTL regulatory patterns in the human frontal cortex based on a large cohort of postmortem human brains. Experiments have demonstrated that our framework can effectively reveal novel eQTL regulatory patterns. And some are in similar structure to the existing gene regulation patterns, such as feed-forward loop (FFL)-like motif, single input module (SIM)-like motif, and dense overlapping regulons (DOR)- like motif. Our method and findings will further enhance the understanding of regulatory mechanisms of eQTLs in multiple tissues and species.
{"title":"Discovering eQTL Regulatory Patterns Through eQTLMotif","authors":"Tao Wang, H Zhao, Yifu Xiao, Hanzi Yang, X. Yin, Yongtian Wang, Bing Xiao, Xuequn Shang, Jiajie Peng","doi":"10.1109/BIBM55620.2022.9995450","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995450","url":null,"abstract":"The expression quantitative trait loci (eQTL) analysis has become important for understanding the regulatory function of genomic variants on gene expression in a tissuespecific manner and has been widely applied across species from microbes to mammals. Current eQTL studies mainly focus on the simple one-to-one regulation between variant and gene. Recent research have demonstrated there are also more complex regulatory patterns between eQTLs and genes. However, there is a lack of studies and relevant methods to systematically discover the regulatory patterns between multiple eQTLs and multiple genes. In this regard, this study has proposed a novel computational framework, called eQTLMotif, to discover regulation patterns of eQTLs in a many-to-many manner. This framework mainly consists of two steps: (1) construct a novel eQTL regulatory network by integrating bipartite eQTL network, eQTL mediation effects, and gene regulatory network; (2) perform motif mining through exactly enumerating frequently appeared eQTL regulatory structures. Based on this framework, we for the first time systematically investigated the eQTL regulatory patterns in the human frontal cortex based on a large cohort of postmortem human brains. Experiments have demonstrated that our framework can effectively reveal novel eQTL regulatory patterns. And some are in similar structure to the existing gene regulation patterns, such as feed-forward loop (FFL)-like motif, single input module (SIM)-like motif, and dense overlapping regulons (DOR)- like motif. Our method and findings will further enhance the understanding of regulatory mechanisms of eQTLs in multiple tissues and species.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127352819","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995308
Jiaqian Yan, Jianing Xi, Zhenhua Yu
Clustering tumor single-cell mutation data has formed an important paradigm for deciphering tumor subclones and evolutionary history. This type of data may often be heavily complicated by incompleteness, false positives and false negatives errors. Despite to the fact that several computational methods have been developed for clustering binary mutation data, their applications still suffer from degraded accuracy on large datasets or datasets with high sparsity. Therefore, more effective methods are sorely required. Here, we propose a novel method called CBM for reliably Clustering Binary Mutation data. CBM formulates the binary mutation data under a probabilistic framework through parameterizing false positive errors, false negative errors, presence probability distribution of subclones and their binary mutation profiles. To cope with the difficulty of optimizing discrete parameters, Gibbs sampling for mixtures is employed to iteratively sample cell-to-cluster assignments and cluster centers from the posterior. Extensive evaluations on simulated and real datasets demonstrate CBM outperforms the state-of-the-art tools in different performance metrics such as ARI for clustering and accuracy for genotyping. CBM can be integrated into the pipeline of reconstructing tumor evolutionary tree, and detecting subclones using CBM can be employed as a pre-text task of tumor subclonal tree inference, which will significantly improve computational efficiency of phylogenetic analysis especially on large datasets. CBM software is freely available at https://github.com/zhyu-lab/cbm.
{"title":"A parametric model for clustering single-cell mutation data","authors":"Jiaqian Yan, Jianing Xi, Zhenhua Yu","doi":"10.1109/BIBM55620.2022.9995308","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995308","url":null,"abstract":"Clustering tumor single-cell mutation data has formed an important paradigm for deciphering tumor subclones and evolutionary history. This type of data may often be heavily complicated by incompleteness, false positives and false negatives errors. Despite to the fact that several computational methods have been developed for clustering binary mutation data, their applications still suffer from degraded accuracy on large datasets or datasets with high sparsity. Therefore, more effective methods are sorely required. Here, we propose a novel method called CBM for reliably Clustering Binary Mutation data. CBM formulates the binary mutation data under a probabilistic framework through parameterizing false positive errors, false negative errors, presence probability distribution of subclones and their binary mutation profiles. To cope with the difficulty of optimizing discrete parameters, Gibbs sampling for mixtures is employed to iteratively sample cell-to-cluster assignments and cluster centers from the posterior. Extensive evaluations on simulated and real datasets demonstrate CBM outperforms the state-of-the-art tools in different performance metrics such as ARI for clustering and accuracy for genotyping. CBM can be integrated into the pipeline of reconstructing tumor evolutionary tree, and detecting subclones using CBM can be employed as a pre-text task of tumor subclonal tree inference, which will significantly improve computational efficiency of phylogenetic analysis especially on large datasets. CBM software is freely available at https://github.com/zhyu-lab/cbm.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127479960","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995652
Bo Wu, Xun Liang, Xiangping Zheng, Jun Wang, Xiaoping Zhou
Graph neural networks (GNNs) have become a practical paradigm for learning graph-structured data, which can generate node representations by recursively aggregating information from neighbor nodes. Recent works utilize self-supervised tasks to learn transferable knowledge from source domain graphs and improve the GNNs performance on target domain graphs. However, there are considerable low-quality and incorrect-labeled graphs in the source domain, which leads to the negative transfer problem in target domain graphs. To tackle this challenge, we propose RSS-GNN, a reinforced sample selection for GNNs transfer learning. The critical insight is that RSS-GNN attempts to use reinforcement learning (RL) to guide transfer learning and narrow the graph divergence between the source and the target domain. We leverage a selection distribution generator (SDG) to produce the probability for each graph and select high-quality graphs to train GNNs. We innovatively designed a reward mechanism to measure the quality of the selection process and employ the policy gradient to update SDG parameters. Extensive experiments demonstrate that our approach can be compatible with various GNNs frameworks and yields superior performance compared to state-of-the-art methods.
{"title":"Reinforced Sample Selection for Graph Neural Networks Transfer Learning","authors":"Bo Wu, Xun Liang, Xiangping Zheng, Jun Wang, Xiaoping Zhou","doi":"10.1109/BIBM55620.2022.9995652","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995652","url":null,"abstract":"Graph neural networks (GNNs) have become a practical paradigm for learning graph-structured data, which can generate node representations by recursively aggregating information from neighbor nodes. Recent works utilize self-supervised tasks to learn transferable knowledge from source domain graphs and improve the GNNs performance on target domain graphs. However, there are considerable low-quality and incorrect-labeled graphs in the source domain, which leads to the negative transfer problem in target domain graphs. To tackle this challenge, we propose RSS-GNN, a reinforced sample selection for GNNs transfer learning. The critical insight is that RSS-GNN attempts to use reinforcement learning (RL) to guide transfer learning and narrow the graph divergence between the source and the target domain. We leverage a selection distribution generator (SDG) to produce the probability for each graph and select high-quality graphs to train GNNs. We innovatively designed a reward mechanism to measure the quality of the selection process and employ the policy gradient to update SDG parameters. Extensive experiments demonstrate that our approach can be compatible with various GNNs frameworks and yields superior performance compared to state-of-the-art methods.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127511885","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995522
Yuting Xing, Hangting Ye, Xiaoyu Zhang, Wei Cao, Shun Zheng, J. Bian, Yike Guo
Continuous glucose monitoring prediction is a crucial yet challenging task in precision medicine. This paper presents a novel neural ODE based approach for predicting continuous glucose monitoring (CGM) levels purely based on sporadic self-monitoring signals. We integrate the expert knowledge from physiological model into our model to improve the accuracy. Experiments on the real-world data demonstrate that our method outperforms other state-of-the-art methods on NRMSE metrics.
{"title":"A continuous glucose monitoring measurements forecasting approach via sporadic blood glucose monitoring","authors":"Yuting Xing, Hangting Ye, Xiaoyu Zhang, Wei Cao, Shun Zheng, J. Bian, Yike Guo","doi":"10.1109/BIBM55620.2022.9995522","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995522","url":null,"abstract":"Continuous glucose monitoring prediction is a crucial yet challenging task in precision medicine. This paper presents a novel neural ODE based approach for predicting continuous glucose monitoring (CGM) levels purely based on sporadic self-monitoring signals. We integrate the expert knowledge from physiological model into our model to improve the accuracy. Experiments on the real-world data demonstrate that our method outperforms other state-of-the-art methods on NRMSE metrics.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125008992","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995014
Peng Jiang, Juan Liu, Lang Wang, Jing Feng, Dehua Cao, Baochuan Pang
The cervical histopathology analysis result is the gold standard for cervical cancer diagnosis. Conventional histopathological examination depends on pathologists’ observation under microscope, which is notoriously labor-intensive and subjective. The popularization of digital pathology technology makes the collection of the cervical histopathological whole slide images (WSIs) more convenient, so it has become possible to develop computer-aided diagnosis methods for cervical cancer. In this work, we first collected the cervical histopathological WSIs from 917 patients with pathological diagnosis through a retrospective study, of which 286 WSIs contained annotations of several lesion areas that were manually outlined by the pathologists. Then we proposed a method for classifying cervical histopathological WSIs by combining deep multi-instance transfer learning (DMITL) and support vector machine (SVM). The DMITL aimed for learning the representations of the WSIs, and the SVM was used for building the classification model of the WSIs. We generated the training and test sets based on our collected WSIs to train and evaluate our method. The validation results have shown that the good performance of our proposed method.
宫颈组织病理学分析结果是宫颈癌诊断的金标准。传统的组织病理学检查依赖于病理学家在显微镜下的观察，这是出了名的劳动密集型和主观性。数字病理技术的普及，使得宫颈组织病理全切片图像(wsi)的采集更加方便，为宫颈癌计算机辅助诊断方法的发展提供了可能。在这项工作中，我们首先通过回顾性研究收集了917例病理诊断的宫颈组织病理学wsi，其中286例wsi包含几个病变区域的注释，这些区域由病理学家手动勾画。然后，我们提出了一种结合深度多实例迁移学习(deep multi-instance transfer learning, DMITL)和支持向量机(support vector machine, SVM)的宫颈组织病理学wsi分类方法。其中，DMITL用于学习wsi的表示，SVM用于构建wsi的分类模型。我们根据收集到的wsi生成训练集和测试集，以训练和评估我们的方法。验证结果表明，该方法具有良好的性能。
{"title":"Classifying Cervical Histopathological Whole Slide Images via Deep Multi-Instance Transfer Learning","authors":"Peng Jiang, Juan Liu, Lang Wang, Jing Feng, Dehua Cao, Baochuan Pang","doi":"10.1109/BIBM55620.2022.9995014","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995014","url":null,"abstract":"The cervical histopathology analysis result is the gold standard for cervical cancer diagnosis. Conventional histopathological examination depends on pathologists’ observation under microscope, which is notoriously labor-intensive and subjective. The popularization of digital pathology technology makes the collection of the cervical histopathological whole slide images (WSIs) more convenient, so it has become possible to develop computer-aided diagnosis methods for cervical cancer. In this work, we first collected the cervical histopathological WSIs from 917 patients with pathological diagnosis through a retrospective study, of which 286 WSIs contained annotations of several lesion areas that were manually outlined by the pathologists. Then we proposed a method for classifying cervical histopathological WSIs by combining deep multi-instance transfer learning (DMITL) and support vector machine (SVM). The DMITL aimed for learning the representations of the WSIs, and the SVM was used for building the classification model of the WSIs. We generated the training and test sets based on our collected WSIs to train and evaluate our method. The validation results have shown that the good performance of our proposed method.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125020880","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}
Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers.
{"title":"Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier","authors":"Supantha Das, A. Hazra, Soumen Kumar Pati, Soumadip Ghosh, Saurav Mallik, Suharta Banerjee, Ayan Mukherji, Aimin Li, Zhongming Zhao","doi":"10.1109/BIBM55620.2022.9994892","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9994892","url":null,"abstract":"Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125814736","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995175
Siyuan Zhao, Jun Meng, Yushi Luan
Long noncoding RNA(lncRNA) has been reported to encode small peptides which play key roles in life activities through their functions by binding to proteins. It is crucial to predict the interactions between the lncRNA-encoded peptide and protein. However, no computational methods have been designed for predicting this type of interactions directly, owing to the few-shot problem causing poor generalization. Prototypical network (ProtoNet) is a classic learner for few-shot learning. However, how to obtain effective embedding and measure the distance between different prototypes accurately are the most important challenges. Although some improved prototypical networks have been proposed, they ignore the role of domain knowledge which is helpful for constructing models conforming to the domain mechanism In this study, we propose a novel method for interactions prediction between plant lncRNA-encoded peptide and protein using domain knowledge-based ProtoNet (IPLncPP-DKPN). Multiple features that imply domain knowledge are extracted, connected, and converted to avoid sparse and enhance information using a dual-routing parallel feature dimensionality reduction algorithm IProtoNet is an improved ProtoNet using capsule network-based embedding and Mahalanobis distance-based prototype. The converted features are fed into IProtoNet to realize the classification task. The experimental results manifest that IPLncPP-DKPN achieves better performance on the independent test set compared with classic machine learning models. To the best of our knowledge, IPLncPP-DKPN is the first computational method for the interactions prediction between lncRNA-encoded peptide and protein.
{"title":"Predicting the interactions between plant lncRNA-encoded peptide and protein using domain knowledge-based prototypical network","authors":"Siyuan Zhao, Jun Meng, Yushi Luan","doi":"10.1109/BIBM55620.2022.9995175","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995175","url":null,"abstract":"Long noncoding RNA(lncRNA) has been reported to encode small peptides which play key roles in life activities through their functions by binding to proteins. It is crucial to predict the interactions between the lncRNA-encoded peptide and protein. However, no computational methods have been designed for predicting this type of interactions directly, owing to the few-shot problem causing poor generalization. Prototypical network (ProtoNet) is a classic learner for few-shot learning. However, how to obtain effective embedding and measure the distance between different prototypes accurately are the most important challenges. Although some improved prototypical networks have been proposed, they ignore the role of domain knowledge which is helpful for constructing models conforming to the domain mechanism In this study, we propose a novel method for interactions prediction between plant lncRNA-encoded peptide and protein using domain knowledge-based ProtoNet (IPLncPP-DKPN). Multiple features that imply domain knowledge are extracted, connected, and converted to avoid sparse and enhance information using a dual-routing parallel feature dimensionality reduction algorithm IProtoNet is an improved ProtoNet using capsule network-based embedding and Mahalanobis distance-based prototype. The converted features are fed into IProtoNet to realize the classification task. The experimental results manifest that IPLncPP-DKPN achieves better performance on the independent test set compared with classic machine learning models. To the best of our knowledge, IPLncPP-DKPN is the first computational method for the interactions prediction between lncRNA-encoded peptide and protein.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125900942","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}
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