Pub Date : 2012-10-04DOI: 10.1109/BIBM.2012.6392725
I. Hashmi, Amarda Shehu
We present a novel probabilistic search algorithm to efficiently search the structure space of protein dimers. The algorithm is based on the basin hopping framework that repeatedly follows up structural perturbation with energy minimization to obtain a coarse-grained view of the dimeric energy surface in terms of its local minima. A Metropolis criterion biases the search towards lower-energy minima over time. Extensive analysis highlights efficient and effective implementations for the perturbation and minimization components. Testing on a broad list of dimers shows the algorithm recovers the native dimeric configuration with great accuracy and produces many minima near the native configuration. The algorithm can be employed to efficiently produce relevant decoys that can be further refined at greater detail to predict the native configuration.
{"title":"A basin hopping algorithm for protein-protein docking","authors":"I. Hashmi, Amarda Shehu","doi":"10.1109/BIBM.2012.6392725","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392725","url":null,"abstract":"We present a novel probabilistic search algorithm to efficiently search the structure space of protein dimers. The algorithm is based on the basin hopping framework that repeatedly follows up structural perturbation with energy minimization to obtain a coarse-grained view of the dimeric energy surface in terms of its local minima. A Metropolis criterion biases the search towards lower-energy minima over time. Extensive analysis highlights efficient and effective implementations for the perturbation and minimization components. Testing on a broad list of dimers shows the algorithm recovers the native dimeric configuration with great accuracy and produces many minima near the native configuration. The algorithm can be employed to efficiently produce relevant decoys that can be further refined at greater detail to predict the native configuration.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"102 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80556549","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470346
Zhaohui Liang, Gang Zhang, Ziping Li, Jian Yin, W. Fu
The inheritance of clinical experience of veteran doctors of Chinese medicine (CM) plays a key role in the development and effectiveness enhancement of Chinese medicine in the history. The clinical experience are classified as the patterns of disease diagnosis and Chinese medical Zheng diagnosis, the identification of core elements of Zheng, the treatment experience and relation of herbal medicine formulae, Zheng and disease, and the common law of diagnosis and treatment in real practice. The source of the experience mainly originates from literature and manuscripts of CM masters, which are being electronically recorded during the last two decades. As a result, it makes feasible to apply data mining to the knowledge discovery through the experience of veteran CM doctors. However, the current focus on this field is limited to the published literature such as journal papers, conference proceedings and textbooks, but the paper based manuscripts personally written by the veteran doctors are usually neglected. In this paper, we established a database for Dr Situ Ling, who is a deceased famous CM acupuncture master in southern China. The study objective is to discover the acupuncture point selection patterns which require profession knowledge and experience from senior CM doctors. It is believed these patterns are deposited as underlying knowledge with various middle level concepts that can be analyzed and discover by a serial of algorithms. Thus in this work, we formularized the patterns of acupuncture point selection as a learning task with deep architecture, which attempts to capture either existent or underlying concepts so as to simulate the planning process of the combined diagnosis of western medicine and Chinese medicine. The Restricted Boltzmann Machines (RBM) was used as the main model for deep learning to process to medical record data with international standard diagnosis (ICD-10) previously made by trained doctors. Then the ICD-10 based diagnosis dataset was introduced into our framework to enhance the concepts diversity. After applying this model, the learning accuracy based on the medical record database of Dr Situ Ling was raised up to 75%. Thus this model can serve as a solution to discover the acupuncture point selection patterns of CM acupuncture veteran doctors. Furthermore, the data mining study model linked by international diagnosis standard (i.e. ICD-10), point selection patterns, and clinical symptoms will provide useful cues to reveal the essence of Zheng diagnosis through experience of CM veteran doctors.
{"title":"Deep learning for acupuncture point selection patterns based on veteran doctor experience of Chinese medicine","authors":"Zhaohui Liang, Gang Zhang, Ziping Li, Jian Yin, W. Fu","doi":"10.1109/BIBMW.2012.6470346","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470346","url":null,"abstract":"The inheritance of clinical experience of veteran doctors of Chinese medicine (CM) plays a key role in the development and effectiveness enhancement of Chinese medicine in the history. The clinical experience are classified as the patterns of disease diagnosis and Chinese medical Zheng diagnosis, the identification of core elements of Zheng, the treatment experience and relation of herbal medicine formulae, Zheng and disease, and the common law of diagnosis and treatment in real practice. The source of the experience mainly originates from literature and manuscripts of CM masters, which are being electronically recorded during the last two decades. As a result, it makes feasible to apply data mining to the knowledge discovery through the experience of veteran CM doctors. However, the current focus on this field is limited to the published literature such as journal papers, conference proceedings and textbooks, but the paper based manuscripts personally written by the veteran doctors are usually neglected. In this paper, we established a database for Dr Situ Ling, who is a deceased famous CM acupuncture master in southern China. The study objective is to discover the acupuncture point selection patterns which require profession knowledge and experience from senior CM doctors. It is believed these patterns are deposited as underlying knowledge with various middle level concepts that can be analyzed and discover by a serial of algorithms. Thus in this work, we formularized the patterns of acupuncture point selection as a learning task with deep architecture, which attempts to capture either existent or underlying concepts so as to simulate the planning process of the combined diagnosis of western medicine and Chinese medicine. The Restricted Boltzmann Machines (RBM) was used as the main model for deep learning to process to medical record data with international standard diagnosis (ICD-10) previously made by trained doctors. Then the ICD-10 based diagnosis dataset was introduced into our framework to enhance the concepts diversity. After applying this model, the learning accuracy based on the medical record database of Dr Situ Ling was raised up to 75%. Thus this model can serve as a solution to discover the acupuncture point selection patterns of CM acupuncture veteran doctors. Furthermore, the data mining study model linked by international diagnosis standard (i.e. ICD-10), point selection patterns, and clinical symptoms will provide useful cues to reveal the essence of Zheng diagnosis through experience of CM veteran doctors.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"19 1","pages":"396-401"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88724394","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470207
Sameh Saleh, Brian S. Olson, Amarda Shehu
Obtaining a structural characterization of the biologically active (native) state of a protein is a long standing problem in computational biology. The high dimensionality of the conformational space and ruggedness of the associated energy surface are key challenges to algorithms in search of an ensemble of low-energy decoy conformations relevant for the native state. As the native structure does not often correspond to the global minimum energy, diversity is key. We present a memetic evolutionary algorithm to sample a diverse ensemble of conformations that represent low-energy local minima in the protein energy surface. Conformations in the algorithm are members of an evolving population. The molecular fragment replacement technique is employed to obtain children from parent conformations. A greedy search maps a child conformation to its nearest local minimum. Resulting minima and parent conformations are merged and truncated back to the initial population size based on potential energies. Results show that the additional minimization is key to obtaining a diverse ensemble of decoys, circumvent premature convergence to sub-optimal regions in the conformational space, and approach the native structure with IRMSDs comparable to state-of-the-art decoy sampling methods.
{"title":"A population-based evolutionary algorithm for sampling minima in the protein energy surface","authors":"Sameh Saleh, Brian S. Olson, Amarda Shehu","doi":"10.1109/BIBMW.2012.6470207","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470207","url":null,"abstract":"Obtaining a structural characterization of the biologically active (native) state of a protein is a long standing problem in computational biology. The high dimensionality of the conformational space and ruggedness of the associated energy surface are key challenges to algorithms in search of an ensemble of low-energy decoy conformations relevant for the native state. As the native structure does not often correspond to the global minimum energy, diversity is key. We present a memetic evolutionary algorithm to sample a diverse ensemble of conformations that represent low-energy local minima in the protein energy surface. Conformations in the algorithm are members of an evolving population. The molecular fragment replacement technique is employed to obtain children from parent conformations. A greedy search maps a child conformation to its nearest local minimum. Resulting minima and parent conformations are merged and truncated back to the initial population size based on potential energies. Results show that the additional minimization is key to obtaining a diverse ensemble of decoys, circumvent premature convergence to sub-optimal regions in the conformational space, and approach the native structure with IRMSDs comparable to state-of-the-art decoy sampling methods.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"7 1","pages":"64-71"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89416358","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470274
Daniel Veltri, Amarda Shehu
Concerns over antibacterial resistance have antimicrobial peptides (AMPs) garnering attention as potential targets for new antibacterial drugs [1]. Wet-lab development of AMP-based drugs hinge on understanding the relationship between AMP sequence and activity [1]. In support of such efforts, we devise a method to highlight position-based physico-chemical features related to activity. We do so in a focused analysis of the mature peptide fragments of cathelicidins; a populous sequence-diverse family of well-studied a-helical AMPs [1]. We employ features based on the AAIndex [2], an extensive collection of documented physico-chemical amino acid properties, and Support Vector Machine (SVM) to recognize cathelicidins from a set of carefully designed decoy sequences. Our results demonstrate that these features are very useful in elucidating specific residue positions and properties related to AMP activity.
{"title":"Physico-chemical features for recognition of antimicrobial peptides","authors":"Daniel Veltri, Amarda Shehu","doi":"10.1109/BIBMW.2012.6470274","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470274","url":null,"abstract":"Concerns over antibacterial resistance have antimicrobial peptides (AMPs) garnering attention as potential targets for new antibacterial drugs [1]. Wet-lab development of AMP-based drugs hinge on understanding the relationship between AMP sequence and activity [1]. In support of such efforts, we devise a method to highlight position-based physico-chemical features related to activity. We do so in a focused analysis of the mature peptide fragments of cathelicidins; a populous sequence-diverse family of well-studied a-helical AMPs [1]. We employ features based on the AAIndex [2], an extensive collection of documented physico-chemical amino acid properties, and Support Vector Machine (SVM) to recognize cathelicidins from a set of carefully designed decoy sequences. Our results demonstrate that these features are very useful in elucidating specific residue positions and properties related to AMP activity.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"11 1","pages":"942-942"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84664837","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470254
Xiong Liu, Kaizhi Tang, S. Harper, B. Harper, J. Steevens, R. Xu
Nanomaterial environmental impact (NEI) modeling is critical for industry and policymakers to assess the unintended biological effects (e.g. mortality, malformation, growth inhibition) resulting from the application of engineered nanomaterials. The scope of NEI modeling covers nanomaterial physical, chemical and manufacturing properties, exposure and study scenarios, environmental and ecosystem responses, biological responses, and their interactions. In this paper, we introduce a data mining approach to modeling the biological effects of nanomaterials. Data mining techniques can assist analysts in developing risk assessment models for nanomaterials. Using an experimental dataset on the toxicity of nanomaterials to embryonic zebrafish, we conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic end-points such as mortality, delayed development, and morpholigcal malformations and behavioral abnormalities. The results show that different biological effects have different modeling accuracy given the same set of algorithms and data. The results also show that the weighting scheme for different biological effects has a significant influence on modeling the overall biological effect. These results provide insights into the understanding and modeling of nanomaterial biological effects.
{"title":"Predictive modeling of nanomaterial biological effects","authors":"Xiong Liu, Kaizhi Tang, S. Harper, B. Harper, J. Steevens, R. Xu","doi":"10.1109/BIBMW.2012.6470254","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470254","url":null,"abstract":"Nanomaterial environmental impact (NEI) modeling is critical for industry and policymakers to assess the unintended biological effects (e.g. mortality, malformation, growth inhibition) resulting from the application of engineered nanomaterials. The scope of NEI modeling covers nanomaterial physical, chemical and manufacturing properties, exposure and study scenarios, environmental and ecosystem responses, biological responses, and their interactions. In this paper, we introduce a data mining approach to modeling the biological effects of nanomaterials. Data mining techniques can assist analysts in developing risk assessment models for nanomaterials. Using an experimental dataset on the toxicity of nanomaterials to embryonic zebrafish, we conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic end-points such as mortality, delayed development, and morpholigcal malformations and behavioral abnormalities. The results show that different biological effects have different modeling accuracy given the same set of algorithms and data. The results also show that the weighting scheme for different biological effects has a significant influence on modeling the overall biological effect. These results provide insights into the understanding and modeling of nanomaterial biological effects.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"210 1","pages":"859-863"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84102087","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470243
Timoteus B. Ziminski, A. D. L. R. Algarin, R. Saripalle, S. Demurjian, E. Jackson
Interactions between prescription medications, over-the-counter drugs, and nutritional supplements can have negative consequences for patients. There is a need for the reconciliation across this spectrum spurred on by the adoption of electronic medical records by healthcare providers and the usage of personal health records by patients. In such a setting, unifying information from multiple sources through automated reconciliation can address adverse medication interactions, track adverse medication reactions, and avoid overmedication. This requires mitigating the integration issues of multiple data sources and systems. In this paper, we leverage Harvard University's SMART framework to perform medication reconciliation across different data sources, with the long-term goal of providing robust decision support for overmedication and adverse interactions. Our prototype application SMARTSync provides ontology-backed recognition of interactions, decision support, and is able to warn a patient (or notify a provider) of potential medication problems.
{"title":"SMARTSync: Towards patient-driven medication reconciliation","authors":"Timoteus B. Ziminski, A. D. L. R. Algarin, R. Saripalle, S. Demurjian, E. Jackson","doi":"10.1109/BIBMW.2012.6470243","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470243","url":null,"abstract":"Interactions between prescription medications, over-the-counter drugs, and nutritional supplements can have negative consequences for patients. There is a need for the reconciliation across this spectrum spurred on by the adoption of electronic medical records by healthcare providers and the usage of personal health records by patients. In such a setting, unifying information from multiple sources through automated reconciliation can address adverse medication interactions, track adverse medication reactions, and avoid overmedication. This requires mitigating the integration issues of multiple data sources and systems. In this paper, we leverage Harvard University's SMART framework to perform medication reconciliation across different data sources, with the long-term goal of providing robust decision support for overmedication and adverse interactions. Our prototype application SMARTSync provides ontology-backed recognition of interactions, decision support, and is able to warn a patient (or notify a provider) of potential medication problems.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"44 1","pages":"806-813"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86853865","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470229
L. C. Galvao, L. Nunes, H. S. Lopes, P. Moscato
In spite of the fact that many models of protein structure prediction have been proposed and have also been widely studied in the last years, little attention has been given to the discrete models with side chains. Few papers present algorithms that try to predict the 3 dimensional structures of protein from their amino acid sequences represented by a backbone and the side chains (hydrophobic or hydrophilic). In this paper, we propose a new greedy heuristic with a pull-move set for finding these structures to the 3DHP-SC model, i.e. for a three-dimensional model on a cubic lattice, with side chains. To demonstrate the performance of our method, we have used 25 benchmark instances from the literature. For the instances tested, the proposed technique matched the best known results for 12 instances and obtained better results for the other 13. The computational resources that we have used have been relatively limited in comparison with other studies in the literature, and the quality of our results shows the potential of the approach both in terms of quality and total computation time.
{"title":"A new greedy heuristic for 3DHP protein struture prediction with side chain","authors":"L. C. Galvao, L. Nunes, H. S. Lopes, P. Moscato","doi":"10.1109/BIBMW.2012.6470229","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470229","url":null,"abstract":"In spite of the fact that many models of protein structure prediction have been proposed and have also been widely studied in the last years, little attention has been given to the discrete models with side chains. Few papers present algorithms that try to predict the 3 dimensional structures of protein from their amino acid sequences represented by a backbone and the side chains (hydrophobic or hydrophilic). In this paper, we propose a new greedy heuristic with a pull-move set for finding these structures to the 3DHP-SC model, i.e. for a three-dimensional model on a cubic lattice, with side chains. To demonstrate the performance of our method, we have used 25 benchmark instances from the literature. For the instances tested, the proposed technique matched the best known results for 12 instances and obtained better results for the other 13. The computational resources that we have used have been relatively limited in comparison with other studies in the literature, and the quality of our results shows the potential of the approach both in terms of quality and total computation time.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"87 1","pages":"77-81"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81053994","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 : 2012-10-04DOI: 10.1109/BIBM.2012.6392718
Bingjing Cai, Haiying Wang, Huiru Zheng, Hui Wang
This paper presents a framework for incorporating semantic similarities in the detection of protein complexes from Affinity Purification/Mass Spectrometry (AP-MS) data. AP-MS data is modeled as a bipartite network, where one set of nodes consist of bait proteins and the other set are prey proteins. Pair-wise similarities of bait proteins are computed by combining similarities based on topological features and functional semantic similarities. A hierarchical clustering algorithm is then applied to obtain `seed clusters' consisting of bait proteins. Starting from these `seed' clusters, an expansion process is developed to recruit prey proteins which are significantly associated with bait proteins, to produce final sets of identified protein complexes. In the application to real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes. Six statistical metrics have been applied. Results show that by integrating semantic similarities into the clustering process, the accuracy of identifying complexes has been greatly improved. Meanwhile, clustering results obtained by the proposed framework are better than those from several existent clustering methods.
{"title":"Incorporating semantic similarity into clustering process for identifying protein complexes from Affinity Purification/Mass Spectrometry data","authors":"Bingjing Cai, Haiying Wang, Huiru Zheng, Hui Wang","doi":"10.1109/BIBM.2012.6392718","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392718","url":null,"abstract":"This paper presents a framework for incorporating semantic similarities in the detection of protein complexes from Affinity Purification/Mass Spectrometry (AP-MS) data. AP-MS data is modeled as a bipartite network, where one set of nodes consist of bait proteins and the other set are prey proteins. Pair-wise similarities of bait proteins are computed by combining similarities based on topological features and functional semantic similarities. A hierarchical clustering algorithm is then applied to obtain `seed clusters' consisting of bait proteins. Starting from these `seed' clusters, an expansion process is developed to recruit prey proteins which are significantly associated with bait proteins, to produce final sets of identified protein complexes. In the application to real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes. Six statistical metrics have been applied. Results show that by integrating semantic similarities into the clustering process, the accuracy of identifying complexes has been greatly improved. Meanwhile, clustering results obtained by the proposed framework are better than those from several existent clustering methods.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"190 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79509611","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 : 2012-10-04DOI: 10.1109/BIBMW.2012.6470349
D. Guo, Jian Li, Gang Zhang, Weixiang Lu, Shaojian Xu, Jun Liu
At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.
{"title":"Research on optimal Traditional Chinese Medicine treatment of knee ostarthritis with data mining algorithms","authors":"D. Guo, Jian Li, Gang Zhang, Weixiang Lu, Shaojian Xu, Jun Liu","doi":"10.1109/BIBMW.2012.6470349","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470349","url":null,"abstract":"At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"12 1","pages":"406-409"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81291222","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 : 2012-10-04DOI: 10.1109/BIBM.2012.6392624
Yongguo Mei, R. Hontecillas, Xiaoying Zhang, K. Bisset, S. Eubank, S. Hoops, M. Marathe, J. Bassaganya-Riera
This paper presents ENISI Visual, an agent-based simulator for modeling gut immunity to enteric pathogens. Gastrointestinal systems are important for in-taking food and other nutritions and gut immunity is an important part of human immune system. ENISI Visual provides quality visualizations and users can control initial cell concentrations and the simulation speed, take snapshots, and record videos. The cells are represented with different icons and the icons change colors as their states change. Users can observe real-time immune responses, including cell recruitment, cytokine and chemokine secretion and dissipation, random or chemotactic movement, cell-cell interactions, and state changes. The case study clearly shows that users can use ENISI Visual to develop models and run novel and insightful in silico experiments.
{"title":"ENISI Visual, an agent-based simulator for modeling gut immunity","authors":"Yongguo Mei, R. Hontecillas, Xiaoying Zhang, K. Bisset, S. Eubank, S. Hoops, M. Marathe, J. Bassaganya-Riera","doi":"10.1109/BIBM.2012.6392624","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392624","url":null,"abstract":"This paper presents ENISI Visual, an agent-based simulator for modeling gut immunity to enteric pathogens. Gastrointestinal systems are important for in-taking food and other nutritions and gut immunity is an important part of human immune system. ENISI Visual provides quality visualizations and users can control initial cell concentrations and the simulation speed, take snapshots, and record videos. The cells are represented with different icons and the icons change colors as their states change. Users can observe real-time immune responses, including cell recruitment, cytokine and chemokine secretion and dissipation, random or chemotactic movement, cell-cell interactions, and state changes. The case study clearly shows that users can use ENISI Visual to develop models and run novel and insightful in silico experiments.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"16 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90960172","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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