Pub Date : 2016-12-01DOI: 10.1109/BIBM.2016.7822619
Xianjun Shen, Yao Chen, Xingpeng Jiang, Xiaohua Hu, Tingting He, Jincai Yang
The microbiota living in the human body plays a very important role in our health and disease, so the identification of microbes associated with diseases will contribute to improving medical care and to better understanding of microbe functions, interactions. However, the known associations between the diseases and microbes are very less. We proposed a new method for prioritization of candidate microbes to predict disease-microbe relationships that based on the random walking on the heterogeneous network. Here, we first constructed a heterogeneous network by connecting the disease network and microbe network using the disease-microbe relationship information, then extended the random walk to the heterogeneous network, finally we used leave-one-out cross-validation to evaluate the method and ranked the candidate disease-causing microbes. We used the algorithm to disclose some potential association between disease and microbe that cannot be found by microbe network or disease network alone. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and presented the potential microbes associated with these diseases, respectively. We confirmed that the discovery of the associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.
{"title":"Predicting disease-microbe association by random walking on the heterogeneous network","authors":"Xianjun Shen, Yao Chen, Xingpeng Jiang, Xiaohua Hu, Tingting He, Jincai Yang","doi":"10.1109/BIBM.2016.7822619","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822619","url":null,"abstract":"The microbiota living in the human body plays a very important role in our health and disease, so the identification of microbes associated with diseases will contribute to improving medical care and to better understanding of microbe functions, interactions. However, the known associations between the diseases and microbes are very less. We proposed a new method for prioritization of candidate microbes to predict disease-microbe relationships that based on the random walking on the heterogeneous network. Here, we first constructed a heterogeneous network by connecting the disease network and microbe network using the disease-microbe relationship information, then extended the random walk to the heterogeneous network, finally we used leave-one-out cross-validation to evaluate the method and ranked the candidate disease-causing microbes. We used the algorithm to disclose some potential association between disease and microbe that cannot be found by microbe network or disease network alone. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and presented the potential microbes associated with these diseases, respectively. We confirmed that the discovery of the associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124340208","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822745
Pedro Ferreira, I. Dutra, R. Salvini, E. Burnside
Several works in the literature use propositional (“black box”) approaches to generate prediction models. In this work we employ the Inductive Logic Programming technique, whose prediction model is based on first order rules, to the domain of breast cancer. These rules have the advantage of being interpretable and convenient to be used as a common language between the computer scientists and the medical experts. We also explore the relevance of some of variables usually collected to predict breast cancer. We compare our results with a propositional classifier that was considered best for the same dataset studied in this paper.
{"title":"Interpretable models to predict Breast Cancer","authors":"Pedro Ferreira, I. Dutra, R. Salvini, E. Burnside","doi":"10.1109/BIBM.2016.7822745","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822745","url":null,"abstract":"Several works in the literature use propositional (“black box”) approaches to generate prediction models. In this work we employ the Inductive Logic Programming technique, whose prediction model is based on first order rules, to the domain of breast cancer. These rules have the advantage of being interpretable and convenient to be used as a common language between the computer scientists and the medical experts. We also explore the relevance of some of variables usually collected to predict breast cancer. We compare our results with a propositional classifier that was considered best for the same dataset studied in this paper.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124372613","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822495
Xiaodong Zhang, Chong Chu, Yao Zhang, Y. Wu, Jingyang Gao
Accurate calling of structural variations such as deletions with short sequence reads from high-throughput sequencing is an important but challenging problem in the field of genome analysis. There are many existing methods for calling deletions. At present, not a single method clearly outperforms all other methods in precision and sensitivity. A popular strategy used by several authors is combining different signatures left by deletions in order to achieve more accurate deletion calling. However, most existing methods using the combining approach are heuristic and the called deletions by these tools still contain many wrongly called deletions. In this paper, we present Concod, a machine learning based framework for calling deletions with consensus, which is able to more accurately detect and distinguish true deletions from falsely called ones. First, Concod collects candidate deletions by merging the output of multiple existing deletion calling tools. Then, features of each candidate are extracted from aligned reads based on multiple detection theories. Finally, a machine learning model is trained with these features and used to classify the true and false candidates. We test our approach on different coverage of real data and compare with existing tools, including Pindel, SVseq2, BreakDancer, and DELLY. Results show that Concod improves both precision and sensitivity of deletion calling significantly.
{"title":"Concod: Accurate consensus-based approach of calling deletions from high-throughput sequencing data","authors":"Xiaodong Zhang, Chong Chu, Yao Zhang, Y. Wu, Jingyang Gao","doi":"10.1109/BIBM.2016.7822495","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822495","url":null,"abstract":"Accurate calling of structural variations such as deletions with short sequence reads from high-throughput sequencing is an important but challenging problem in the field of genome analysis. There are many existing methods for calling deletions. At present, not a single method clearly outperforms all other methods in precision and sensitivity. A popular strategy used by several authors is combining different signatures left by deletions in order to achieve more accurate deletion calling. However, most existing methods using the combining approach are heuristic and the called deletions by these tools still contain many wrongly called deletions. In this paper, we present Concod, a machine learning based framework for calling deletions with consensus, which is able to more accurately detect and distinguish true deletions from falsely called ones. First, Concod collects candidate deletions by merging the output of multiple existing deletion calling tools. Then, features of each candidate are extracted from aligned reads based on multiple detection theories. Finally, a machine learning model is trained with these features and used to classify the true and false candidates. We test our approach on different coverage of real data and compare with existing tools, including Pindel, SVseq2, BreakDancer, and DELLY. Results show that Concod improves both precision and sensitivity of deletion calling significantly.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124436582","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822627
Shuohong Wang, Xiang Liu, Jingwen Zhao, J. Song, J. Zhang, Y. Chen
This paper proposes a novel cirrhosis diagnosis method using high-frequency ultrasound imaging that is able to not only diagnose cirrhosis, but also determine its stage. We propose combined features extracted from both liver capsule and parenchyma texture to avoid the bias caused by considering only one aspect. The liver capsule is localized using a multi-scale, multi-objective optimization method and indices are proposed to measure the smoothness and continuity of the capsule. The parenchyma texture is modeled with Gaussian mixture model (GMM), and the lesions in the parenchyma are detected by a scale-space defect detection algorithm. The degree of pathological changes of the liver is quantitatively evaluated by 7 features describing morphology of the capsule and lesions in the parenchyma. Then SVM classifiers are trained to classify the samples into different cirrhosis stages. Experiment results demonstrate the effectiveness of the proposed method, which outperforms other 4 state-of-the-art methods and the proposed method that solely uses capsule or parenchyma texture features.
{"title":"Learning to diagnose cirrhosis via combined liver capsule and parenchyma ultrasound image features","authors":"Shuohong Wang, Xiang Liu, Jingwen Zhao, J. Song, J. Zhang, Y. Chen","doi":"10.1109/BIBM.2016.7822627","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822627","url":null,"abstract":"This paper proposes a novel cirrhosis diagnosis method using high-frequency ultrasound imaging that is able to not only diagnose cirrhosis, but also determine its stage. We propose combined features extracted from both liver capsule and parenchyma texture to avoid the bias caused by considering only one aspect. The liver capsule is localized using a multi-scale, multi-objective optimization method and indices are proposed to measure the smoothness and continuity of the capsule. The parenchyma texture is modeled with Gaussian mixture model (GMM), and the lesions in the parenchyma are detected by a scale-space defect detection algorithm. The degree of pathological changes of the liver is quantitatively evaluated by 7 features describing morphology of the capsule and lesions in the parenchyma. Then SVM classifiers are trained to classify the samples into different cirrhosis stages. Experiment results demonstrate the effectiveness of the proposed method, which outperforms other 4 state-of-the-art methods and the proposed method that solely uses capsule or parenchyma texture features.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114565788","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822815
Dan Meng, Guitao Cao, Y. Duan, Minghua Zhu, Liping Tu, Jia-tuo Xu, Dong-Guo Xu
With the improvement of people's living standards, there is no doubt that people are paying more and more attention to their health. However, shortage of medical resources is a critical global problem. As a result, an intelligent prognostics system has a great potential to play important roles in computer aided diagnosis. Numerous papers reported that tongue features have been closely related to a human's state. Among them, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by a deep convolutional neural network (CNN), we propose a deep tongue image feature analysis system to extract unbiased features and reduce human labor for tongue diagnosis. With the unbalanced sample distribution, it is hard to form a balanced classification model based on feature representations obtained by existing low-level and high-level methods. Our proposed deep tongue image feature analysis model learns high-level features and provide more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed system on a set of 267 gastritis patients, and a control group of 48 healthy volunteers (labeled according to Western medical practices). Test results show that the proposed deep tongue image feature analysis model can classify a given tongue image into healthy and diseased state with an average accuracy of 91.49%, which demonstrates the relationship between human body's state and its deep tongue image features.
{"title":"A deep tongue image features analysis model for medical application","authors":"Dan Meng, Guitao Cao, Y. Duan, Minghua Zhu, Liping Tu, Jia-tuo Xu, Dong-Guo Xu","doi":"10.1109/BIBM.2016.7822815","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822815","url":null,"abstract":"With the improvement of people's living standards, there is no doubt that people are paying more and more attention to their health. However, shortage of medical resources is a critical global problem. As a result, an intelligent prognostics system has a great potential to play important roles in computer aided diagnosis. Numerous papers reported that tongue features have been closely related to a human's state. Among them, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by a deep convolutional neural network (CNN), we propose a deep tongue image feature analysis system to extract unbiased features and reduce human labor for tongue diagnosis. With the unbalanced sample distribution, it is hard to form a balanced classification model based on feature representations obtained by existing low-level and high-level methods. Our proposed deep tongue image feature analysis model learns high-level features and provide more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed system on a set of 267 gastritis patients, and a control group of 48 healthy volunteers (labeled according to Western medical practices). Test results show that the proposed deep tongue image feature analysis model can classify a given tongue image into healthy and diseased state with an average accuracy of 91.49%, which demonstrates the relationship between human body's state and its deep tongue image features.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115068263","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822492
Q. Zou, Shixiang Wan, Xiangxiang Zeng
Constructing phylogenetic tree for ultra-large sequences (eg. Files more than 1GB) is quite difficult, especially for the unaligned DNA sequences. It is meaningless and impracticable to do multiple sequence alignment for large diverse DNA sequences. We try to do clustering firstly for the mounts of DNA sequences, and divide them into several clusters. Then each cluster is aligned and phylogenetic analysed in parallel. Hadoop, which is the most popular parallel platform in cloud computing, is employed for this process. Our software tool HPTree can handle the >1GB DNA sequence file or more than 1,000,000 DNA sequences in few hours. Users could try HPTree in the cloud computing platform (eg. Amazon) or their own clusters for the big data phylogenetic tree reconstruction. No super machine or large memory is required. HPTree could benefit the users who focus on population evolution or long common genes (eg. 16s rRNA) evolution. The software tool along with its codes and datasets are accessible at http://lab.malab.cn/soft/HPtree/.
{"title":"HPTree: Reconstructing phylogenetic trees for ultra-large unaligned DNA sequences via NJ model and Hadoop","authors":"Q. Zou, Shixiang Wan, Xiangxiang Zeng","doi":"10.1109/BIBM.2016.7822492","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822492","url":null,"abstract":"Constructing phylogenetic tree for ultra-large sequences (eg. Files more than 1GB) is quite difficult, especially for the unaligned DNA sequences. It is meaningless and impracticable to do multiple sequence alignment for large diverse DNA sequences. We try to do clustering firstly for the mounts of DNA sequences, and divide them into several clusters. Then each cluster is aligned and phylogenetic analysed in parallel. Hadoop, which is the most popular parallel platform in cloud computing, is employed for this process. Our software tool HPTree can handle the >1GB DNA sequence file or more than 1,000,000 DNA sequences in few hours. Users could try HPTree in the cloud computing platform (eg. Amazon) or their own clusters for the big data phylogenetic tree reconstruction. No super machine or large memory is required. HPTree could benefit the users who focus on population evolution or long common genes (eg. 16s rRNA) evolution. The software tool along with its codes and datasets are accessible at http://lab.malab.cn/soft/HPtree/.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123503179","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822671
Shengyu Liu, Kai Chen, Qingcai Chen, Buzhou Tang
Drug-drug interactions (DDIs) are crucial for healthcare. Besides DDIs reported in medical knowledge bases such as DrugBank, a large number of latest DDI findings are also reported in unstructured biomedical literature. Extracting DDIs from unstructured biomedical literature is a worthy addition to the existing knowledge bases. Currently, convolutional neural network (CNN) is a state-of-the-art method for DDI extraction. One limitation of CNN is that it neglects long distance dependencies between words in candidate DDI instances, which may be helpful for DDI extraction. In order to incorporate the long distance dependencies between words in candidate DDI instances, in this work, we propose a dependency-based convolutional neural network (DCNN) for DDI extraction. Experiments conducted on the DDIExtraction 2013 corpus show that DCNN using a public state-of-the-art dependency parser achieves an F-score of 70.19%, outperforming CNN by 0.44%. By analyzing errors of DCNN, we find that errors from dependency parsers are propagated into DCNN and affect the performance of DCNN. To reduce error propagation, we design a simple rule to combine CNN with DCNN, that is, using DCNN to extract DDIs in short sentences and CNN to extract DDIs in long distances as most dependency parsers work well for short sentences but bad for long sentences. Finally, our system that combines CNN and DCNN achieves an F-score of 70.81%, outperforming CNN by 1.06% and DNN by 0.62% on the DDIExtraction 2013 corpus.
{"title":"Dependency-based convolutional neural network for drug-drug interaction extraction","authors":"Shengyu Liu, Kai Chen, Qingcai Chen, Buzhou Tang","doi":"10.1109/BIBM.2016.7822671","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822671","url":null,"abstract":"Drug-drug interactions (DDIs) are crucial for healthcare. Besides DDIs reported in medical knowledge bases such as DrugBank, a large number of latest DDI findings are also reported in unstructured biomedical literature. Extracting DDIs from unstructured biomedical literature is a worthy addition to the existing knowledge bases. Currently, convolutional neural network (CNN) is a state-of-the-art method for DDI extraction. One limitation of CNN is that it neglects long distance dependencies between words in candidate DDI instances, which may be helpful for DDI extraction. In order to incorporate the long distance dependencies between words in candidate DDI instances, in this work, we propose a dependency-based convolutional neural network (DCNN) for DDI extraction. Experiments conducted on the DDIExtraction 2013 corpus show that DCNN using a public state-of-the-art dependency parser achieves an F-score of 70.19%, outperforming CNN by 0.44%. By analyzing errors of DCNN, we find that errors from dependency parsers are propagated into DCNN and affect the performance of DCNN. To reduce error propagation, we design a simple rule to combine CNN with DCNN, that is, using DCNN to extract DDIs in short sentences and CNN to extract DDIs in long distances as most dependency parsers work well for short sentences but bad for long sentences. Finally, our system that combines CNN and DCNN achieves an F-score of 70.81%, outperforming CNN by 1.06% and DNN by 0.62% on the DDIExtraction 2013 corpus.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122104250","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822621
Rongjie Wang, Mingxiang Teng, Yang Bai, Tianyi Zang, Yadong Wang
Genome data increasing exponentially since the last decade, compressing genome with Markov models has been proposed as an effective statistical method. However, existing methods set a static order-k Markov models to compress various genomes. Employing static order-k Markov model could result in a sub-optimal orders on some genomes. In this paper, we propose a compression method that relies on a pre-analysis of the data before compression, with the aim of estimating Markov models order k, yielding improvements over static Markov models. Experimental results on the latest complete bacterial genome data show that our method could effectively compress genome with a better performance than the state-of-the-art method. The codes of DMcompress are available at https://rongjiewang.github.io/DMcompress
{"title":"DMcompress: Dynamic Markov models for bacterial genome compression","authors":"Rongjie Wang, Mingxiang Teng, Yang Bai, Tianyi Zang, Yadong Wang","doi":"10.1109/BIBM.2016.7822621","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822621","url":null,"abstract":"Genome data increasing exponentially since the last decade, compressing genome with Markov models has been proposed as an effective statistical method. However, existing methods set a static order-k Markov models to compress various genomes. Employing static order-k Markov model could result in a sub-optimal orders on some genomes. In this paper, we propose a compression method that relies on a pre-analysis of the data before compression, with the aim of estimating Markov models order k, yielding improvements over static Markov models. Experimental results on the latest complete bacterial genome data show that our method could effectively compress genome with a better performance than the state-of-the-art method. The codes of DMcompress are available at https://rongjiewang.github.io/DMcompress","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"11 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128539204","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822651
Arantza Casillas, Koldo Gojenola, Alicia Pérez, M. Oronoz
This work focuses on the extraction of allergic drug reactions in electronic health records. The goal is to annotate a sub-class of cause-effect events, those in which drugs are causing allergies. Little work has carried out in this field, seldom for Spanish clinical text mining, which is, indeed, the aim of this work. We present two approaches: a rule-based method and another one based on machine learning. Both approaches incorporate semantic knowledge derived from FreeLing-Med, a software explicitly developed to parse texts in the medical domain. Having recognised the medical entities for a given record, the challenge stands on triggering the underlying allergies. To this end, the knowledge is expressed as a set of semantic, syntactic and structural features. Our best system, based on machine learning, obtained a precision of 0.90 with a recall of 0.87, outperforming a rule-based approach.
{"title":"Clinical text mining for efficient extraction of drug-allergy reactions","authors":"Arantza Casillas, Koldo Gojenola, Alicia Pérez, M. Oronoz","doi":"10.1109/BIBM.2016.7822651","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822651","url":null,"abstract":"This work focuses on the extraction of allergic drug reactions in electronic health records. The goal is to annotate a sub-class of cause-effect events, those in which drugs are causing allergies. Little work has carried out in this field, seldom for Spanish clinical text mining, which is, indeed, the aim of this work. We present two approaches: a rule-based method and another one based on machine learning. Both approaches incorporate semantic knowledge derived from FreeLing-Med, a software explicitly developed to parse texts in the medical domain. Having recognised the medical entities for a given record, the challenge stands on triggering the underlying allergies. To this end, the knowledge is expressed as a set of semantic, syntactic and structural features. Our best system, based on machine learning, obtained a precision of 0.90 with a recall of 0.87, outperforming a rule-based approach.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130075288","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822552
Xiaoqin Tang, M. V. Hoff, J. Hoogenboom, Yuanhao Guo, Fuyu Cai, G. Lamers, F. Verbeek
In order to preserve sufficient fluorescence intensity and improve the quality of fluorescence images in optical projection tomography (OPT) imaging, a feasible acquisition solution is to temporally formalize the fluorescence and bright-field imaging procedure as two consecutive phases. To be specific, fluorescence images are acquired first, in a full axial-view revolution, followed by the bright-field images. Due to the mechanical drift, this approach, however, may suffer from a deviation of center of rotation (COR) for the two imaging phases, resulting in irregular 3D image fusion, with which gene or protein activity may be located inaccurately. In this paper, we address this problem and consider it into a framework based on sinogram unification so as to precisely fuse 3D images from different channels for CORs between channels that are not coincident or if COR is not in the center of sinogram. The former case corresponds to the COR deviation above; while the latter one correlates with COR alignment, without which artefacts will be introduced in the reconstructed results. After sinogram unification, inverse radon transform can be implemented on each channel to reconstruct the 3D image. The fusion results are acquired by mapping the 3D images from different channels into a common space. Experimental results indicate that the proposed framework gains excellent performance in 3D image fusion from different channels. For the COR alignment, a new automated method based on interest point detection and included in sinogram unification, is presented. It outperforms traditional COR alignment approaches in combination of effectiveness and computational complexity.
{"title":"Fluorescence and bright-field 3D image fusion based on sinogram unification for optical projection tomography","authors":"Xiaoqin Tang, M. V. Hoff, J. Hoogenboom, Yuanhao Guo, Fuyu Cai, G. Lamers, F. Verbeek","doi":"10.1109/BIBM.2016.7822552","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822552","url":null,"abstract":"In order to preserve sufficient fluorescence intensity and improve the quality of fluorescence images in optical projection tomography (OPT) imaging, a feasible acquisition solution is to temporally formalize the fluorescence and bright-field imaging procedure as two consecutive phases. To be specific, fluorescence images are acquired first, in a full axial-view revolution, followed by the bright-field images. Due to the mechanical drift, this approach, however, may suffer from a deviation of center of rotation (COR) for the two imaging phases, resulting in irregular 3D image fusion, with which gene or protein activity may be located inaccurately. In this paper, we address this problem and consider it into a framework based on sinogram unification so as to precisely fuse 3D images from different channels for CORs between channels that are not coincident or if COR is not in the center of sinogram. The former case corresponds to the COR deviation above; while the latter one correlates with COR alignment, without which artefacts will be introduced in the reconstructed results. After sinogram unification, inverse radon transform can be implemented on each channel to reconstruct the 3D image. The fusion results are acquired by mapping the 3D images from different channels into a common space. Experimental results indicate that the proposed framework gains excellent performance in 3D image fusion from different channels. For the COR alignment, a new automated method based on interest point detection and included in sinogram unification, is presented. It outperforms traditional COR alignment approaches in combination of effectiveness and computational complexity.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130925631","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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