Pub Date : 2023-01-01DOI: 10.1504/ijdmb.2023.10058133
Ping Yu, Fahuan Xie, Yunxin Long, Duo Long, Hui Yan
{"title":"Fast retrieval method of biomedical literature based on feature mining","authors":"Ping Yu, Fahuan Xie, Yunxin Long, Duo Long, Hui Yan","doi":"10.1504/ijdmb.2023.10058133","DOIUrl":"https://doi.org/10.1504/ijdmb.2023.10058133","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66730887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmb.2023.134297
Ahmet Toprak
{"title":"Identification of disease-related miRNAs based on weighted k-nearest known neighbours and inductive matrix completion","authors":"Ahmet Toprak","doi":"10.1504/ijdmb.2023.134297","DOIUrl":"https://doi.org/10.1504/ijdmb.2023.134297","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135006906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmb.2023.134292
Taiping Jiang, Zhibing Wang, Lei Huang
{"title":"Research on human health status recognition based on association algorithm","authors":"Taiping Jiang, Zhibing Wang, Lei Huang","doi":"10.1504/ijdmb.2023.134292","DOIUrl":"https://doi.org/10.1504/ijdmb.2023.134292","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135006907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmb.2023.134301
Priya Arora, Ashutosh Mishra, Avleen Malhi
{"title":"Diagnosis of Parkinson's disease genes using LSTM and MLP-based multi-feature extraction methods","authors":"Priya Arora, Ashutosh Mishra, Avleen Malhi","doi":"10.1504/ijdmb.2023.134301","DOIUrl":"https://doi.org/10.1504/ijdmb.2023.134301","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135006919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmb.2023.134293
Bin Fang
{"title":"Low resolution face recognition algorithm based on MB-LBP","authors":"Bin Fang","doi":"10.1504/ijdmb.2023.134293","DOIUrl":"https://doi.org/10.1504/ijdmb.2023.134293","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135007560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1504/ijdmb.2022.10052684
Vishan Kumar Gupta N.A.
{"title":"Toxicity Detection of Small Drug Molecules of the Mitochondrial Membrane Potential Signalling Pathway using Bagging-based Ensemble Learning","authors":"Vishan Kumar Gupta N.A.","doi":"10.1504/ijdmb.2022.10052684","DOIUrl":"https://doi.org/10.1504/ijdmb.2022.10052684","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66731203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1504/ijdmb.2021.10048132
Xiaolan Guo, Lei Xu, Xiaoping Zhou, Yuting Bai, Can Luo, Xin Chen, Qing Wu, Xiaowu Zhong
{"title":"Diagnostic and prognostic value of HSPD1 in esophageal cancer","authors":"Xiaolan Guo, Lei Xu, Xiaoping Zhou, Yuting Bai, Can Luo, Xin Chen, Qing Wu, Xiaowu Zhong","doi":"10.1504/ijdmb.2021.10048132","DOIUrl":"https://doi.org/10.1504/ijdmb.2021.10048132","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66731184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-09DOI: 10.1504/ijdmb.2020.10031424
Nansheng Chen, Xiaolong Zhang, Haomin Gan, Jing Hu
Revealing the protein hot regions is the key point for understanding the protein-protein interaction, while due to the long period and labour-consuming of experimental methods, it is very helpful to use computational method to improve the efficiency to predict hot regions. In previous methods, some methods are based on a single side, such as structure, energy, and sequence, every side has its limitations. In this paper, we proposed a new method that combines structure-based classification, energy-based clustering and sequence-based conservation. This method makes full use of three sides of protein features and minimise the limitations of using one single side. Experimental results show that the proposed method increases the prediction accuracy of protein hot regions.
{"title":"Identification of protein hot regions by combing structure-based classification, energy-based clustering and sequence-based conservation in evolution","authors":"Nansheng Chen, Xiaolong Zhang, Haomin Gan, Jing Hu","doi":"10.1504/ijdmb.2020.10031424","DOIUrl":"https://doi.org/10.1504/ijdmb.2020.10031424","url":null,"abstract":"Revealing the protein hot regions is the key point for understanding the protein-protein interaction, while due to the long period and labour-consuming of experimental methods, it is very helpful to use computational method to improve the efficiency to predict hot regions. In previous methods, some methods are based on a single side, such as structure, energy, and sequence, every side has its limitations. In this paper, we proposed a new method that combines structure-based classification, energy-based clustering and sequence-based conservation. This method makes full use of three sides of protein features and minimise the limitations of using one single side. Experimental results show that the proposed method increases the prediction accuracy of protein hot regions.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"24 1","pages":"74-95"},"PeriodicalIF":0.3,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42385830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fetal Weight Prediction Based on Improved PSO-GRNN Model","authors":"Fangxiong Chen, Guoheng Huang, Hui-Shi Wu, Ke Hu, Weiwen Zhang, Cheng Lianglun","doi":"10.1504/ijdmb.2020.10031299","DOIUrl":"https://doi.org/10.1504/ijdmb.2020.10031299","url":null,"abstract":"","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"24 1","pages":"177-200"},"PeriodicalIF":0.3,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66731008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-18DOI: 10.1504/IJDMB.2019.10021458
Cong Sun, Zhihao Yang, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang, Liang Yang, Kan Xu, Yijia Zhang
Mining chemical-protein interactions between chemicals and proteins plays vital roles in biomedical tasks, such as knowledge graph, pharmacology, and clinical research. Although chemical-protein interactions can be manually curated from the biomedical literature, the process is difficult and time-consuming. Hence, it is of great value to automatically obtain the chemical-protein interactions from biomedical literature. Recently, the most popular methods are based on the neural network to avoid complex manual processing. However, the performance is usually limited because of the lengthy and complicated sentences. To address this limitation, we propose a novel model, Hierarchical Recurrent Convolutional Neural Network (HRCNN), to learn hidden semantic and syntactic features from sentence sub-sequences effectively. Our approach achieves an F-score of 65.56% on the CHEMPROT corpus and outperforms the state-of-the-art systems. The experimental results demonstrate that our approach can greatly alleviate the defect of existing methods due to the existence of long sentences.
{"title":"Chemical-protein interaction extraction from biomedical literature: a hierarchical recurrent convolutional neural network method","authors":"Cong Sun, Zhihao Yang, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang, Liang Yang, Kan Xu, Yijia Zhang","doi":"10.1504/IJDMB.2019.10021458","DOIUrl":"https://doi.org/10.1504/IJDMB.2019.10021458","url":null,"abstract":"Mining chemical-protein interactions between chemicals and proteins plays vital roles in biomedical tasks, such as knowledge graph, pharmacology, and clinical research. Although chemical-protein interactions can be manually curated from the biomedical literature, the process is difficult and time-consuming. Hence, it is of great value to automatically obtain the chemical-protein interactions from biomedical literature. Recently, the most popular methods are based on the neural network to avoid complex manual processing. However, the performance is usually limited because of the lengthy and complicated sentences. To address this limitation, we propose a novel model, Hierarchical Recurrent Convolutional Neural Network (HRCNN), to learn hidden semantic and syntactic features from sentence sub-sequences effectively. Our approach achieves an F-score of 65.56% on the CHEMPROT corpus and outperforms the state-of-the-art systems. The experimental results demonstrate that our approach can greatly alleviate the defect of existing methods due to the existence of long sentences.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2019-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44720726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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