Pub Date : 2012-10-04DOI: 10.1109/BIBM.2012.6392643
Vinod Kumar
Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes. Here we will describe two distinct approaches that utilize these data types to systematically evaluate and suggest new disease indications for new or existing drugs. The first approach dubbed the Connectivity Map (CMap) is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules that enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene-expression changes.The second approach uses genetic associations from Genome Wide Association Studies (GWAS) to find alternative indications for existing drugs. Other approaches which take advantage of the availability of clinical data will also be discussed briefly.
{"title":"Systematic drug repositioning: A new paradigm in drug discovery","authors":"Vinod Kumar","doi":"10.1109/BIBM.2012.6392643","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392643","url":null,"abstract":"Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes. Here we will describe two distinct approaches that utilize these data types to systematically evaluate and suggest new disease indications for new or existing drugs. The first approach dubbed the Connectivity Map (CMap) is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules that enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene-expression changes.The second approach uses genetic associations from Genome Wide Association Studies (GWAS) to find alternative indications for existing drugs. Other approaches which take advantage of the availability of clinical data will also be discussed briefly.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"15 1","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90264287","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.2012.6470265
Sagar Patel, H. Panchal, K. Anjaria
Bioinformatics is rapidly growing field of applied science, here we have done DNA sequence analyses of few legume tree species by bioinformatics tools. In this paper bioinformatics data of some leguminous trees have been explored and brought to one platform. Various analytical bioinformatics tools were used to generate the information for particular species or group of species. DNA sequence analyses have been done by ORF FINDER & GENOMATIX. The results were discussed in context with all available data generated through above methods for leguminous trees. We have done analysis of 23 legume species of Leguminosae family, and it is further classified in three subfamily, 1. Fabaceae (Papilionaceae) 2. Caesalpiniaceae 3. Mimosaeae and made a database which contains all legume species results, but here we have taken only two legume species in two tools to demonstrate our work. We have used DNA sequence from EMBL database.
{"title":"DNA sequence analysis by ORF FINDER & GENOMATIX tool: Bioinformatics analysis of some tree species of Leguminosae family","authors":"Sagar Patel, H. Panchal, K. Anjaria","doi":"10.1109/BIBMW.2012.6470265","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470265","url":null,"abstract":"Bioinformatics is rapidly growing field of applied science, here we have done DNA sequence analyses of few legume tree species by bioinformatics tools. In this paper bioinformatics data of some leguminous trees have been explored and brought to one platform. Various analytical bioinformatics tools were used to generate the information for particular species or group of species. DNA sequence analyses have been done by ORF FINDER & GENOMATIX. The results were discussed in context with all available data generated through above methods for leguminous trees. We have done analysis of 23 legume species of Leguminosae family, and it is further classified in three subfamily, 1. Fabaceae (Papilionaceae) 2. Caesalpiniaceae 3. Mimosaeae and made a database which contains all legume species results, but here we have taken only two legume species in two tools to demonstrate our work. We have used DNA sequence from EMBL database.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"4613 3 1","pages":"922-926"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78078251","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.2012.6392647
Hong Cai, Changjin Hong, Jianying Gu, T. Lilburn, R. Kuang, Yufeng Wang
With 300-500 clinical cases and 1-2 million deaths yearly, malaria contributes to enormous health care and economic burden worldwide. The advent of high throughput -omics technologies is driving new approaches to the identification of potential antimalarial targets. In this paper, we propose a neighborhood subnetwork alignment approach to uncover the network components involved in cell cycle regulation of the malaria parasite Plasmodium falciparum and to assign function to previously unannotated proteins.
{"title":"Prediction of novel systems components in cell cycle regulation in malaria parasite by subnetwork alignments","authors":"Hong Cai, Changjin Hong, Jianying Gu, T. Lilburn, R. Kuang, Yufeng Wang","doi":"10.1109/BIBM.2012.6392647","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392647","url":null,"abstract":"With 300-500 clinical cases and 1-2 million deaths yearly, malaria contributes to enormous health care and economic burden worldwide. The advent of high throughput -omics technologies is driving new approaches to the identification of potential antimalarial targets. In this paper, we propose a neighborhood subnetwork alignment approach to uncover the network components involved in cell cycle regulation of the malaria parasite Plasmodium falciparum and to assign function to previously unannotated proteins.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"28 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75602935","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.2012.6392721
Shijiazhu, Yadong Wang
Dynamic Bayesian Network (DBN) has been widely used to infer gene regulatory network from time series gene expression dataset. The standard assumption underlying DBN is based on stationarity, however, in many cases, the gene regulatory network topology might evolve over time. In this paper, we propose a novel non-stationary DBN based network inference approach. In this model, for each variable, a specific HMM implicitly well handles the transition of the stationary DBNs along timesteps. Furthermore, we present a criterion, named as BWBIC score. This criterion is an approximation to the EM objective term, which can reasonably and easily evaluate hmDBN Towards BWBIC score, a greedy hill climbing based structural EM algorithm is proposed to efficiently infer the hmDBN model. We respectively apply our method on synthetic and real biological data. Compared to the recent proposed methods, we obtained better prediction accuracy on both datasets.
{"title":"Modelling non-stationary gene regulatory process with hidden Markov Dynamic Bayesian Network","authors":"Shijiazhu, Yadong Wang","doi":"10.1109/BIBM.2012.6392721","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392721","url":null,"abstract":"Dynamic Bayesian Network (DBN) has been widely used to infer gene regulatory network from time series gene expression dataset. The standard assumption underlying DBN is based on stationarity, however, in many cases, the gene regulatory network topology might evolve over time. In this paper, we propose a novel non-stationary DBN based network inference approach. In this model, for each variable, a specific HMM implicitly well handles the transition of the stationary DBNs along timesteps. Furthermore, we present a criterion, named as BWBIC score. This criterion is an approximation to the EM objective term, which can reasonably and easily evaluate hmDBN Towards BWBIC score, a greedy hill climbing based structural EM algorithm is proposed to efficiently infer the hmDBN model. We respectively apply our method on synthetic and real biological data. Compared to the recent proposed methods, we obtained better prediction accuracy on both datasets.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"1 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":"75613314","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.2012.6470337
Weiwei Han, Lei Xie
Metformin is the first-line drug of choice for the treatment of type 2 diabetes. Recently, it was found that clinically achievable concentrations of metformin cause significant death of cancer cells in culture. Existing evidences connect its anti-cancer effects to the inhibition of the mTOR signaling pathway, but the actual molecular targets remain unknown. In this study, proteome-wide ligand binding site analysis, reverse protein-ligand docking, and quantum mechanics are used to search for the potential molecular targets of metformin. Our results suggest that metformin may bind to β-subunit of AMP-Activated Protein Kinase (AMPK), and active AMPK through allosteric regulation. Several off-targets that are directly or indirectly involved in mTOR pathways are identified. These results generate a tractable set of anti-cancer protein targets for experimental validations.
{"title":"Structural basis of polypharmacological effects of metformin","authors":"Weiwei Han, Lei Xie","doi":"10.1109/BIBMW.2012.6470337","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470337","url":null,"abstract":"Metformin is the first-line drug of choice for the treatment of type 2 diabetes. Recently, it was found that clinically achievable concentrations of metformin cause significant death of cancer cells in culture. Existing evidences connect its anti-cancer effects to the inhibition of the mTOR signaling pathway, but the actual molecular targets remain unknown. In this study, proteome-wide ligand binding site analysis, reverse protein-ligand docking, and quantum mechanics are used to search for the potential molecular targets of metformin. Our results suggest that metformin may bind to β-subunit of AMP-Activated Protein Kinase (AMPK), and active AMPK through allosteric regulation. Several off-targets that are directly or indirectly involved in mTOR pathways are identified. These results generate a tractable set of anti-cancer protein targets for experimental validations.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"448 1","pages":"28-31"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74403813","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.2012.6392713
Randall Wald, T. Khoshgoftaar, A. A. Shanab
Many biological datasets exhibit high dimensionality, a large abundance of attributes (genes) per instance (sample). This problem is often solved using feature selection, which works by selecting the most relevant attributes and removing irrelevant and redundant attributes. Although feature selection techniques are often evaluated based on the performance of classification models (e.g., algorithms designed to distinguish between multiple classes of instances, such as cancerous vs. noncancerous) built using the selected features, another important criterion which is often neglected is stability, the degree of agreement among a feature selection technique's outputs when there are changes to the dataset. More stable feature selection techniques will give the same features even if aspects of the data change. In this study we consider two different approaches for evaluating the stability of feature selection techniques, with each approach consisting of noise injection followed by feature ranking. The two approaches differ in that the first approach compares the features selected from the noisy datasets with the features selected from the original (clean) dataset, while the second approach performs pairwise comparisons among the results from the noisy datasets. To evaluate these two approaches, we use four biological datasets and employ six commonly-used feature rankers. We draw two primary conclusions from our experiments: First, the rankers show different levels of stability in the face of noise. In particular, the ReliefF ranker has significantly greater stability than the other rankers. Also, we found that both approaches gave the same results in terms of stability patterns, although the first approach had greater stability overall. Additionally, because the first approach is significantly less computationally expensive, future studies may employ a faster technique to gain the same results.
{"title":"The effect of measurement approach and noise level on gene selection stability","authors":"Randall Wald, T. Khoshgoftaar, A. A. Shanab","doi":"10.1109/BIBM.2012.6392713","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392713","url":null,"abstract":"Many biological datasets exhibit high dimensionality, a large abundance of attributes (genes) per instance (sample). This problem is often solved using feature selection, which works by selecting the most relevant attributes and removing irrelevant and redundant attributes. Although feature selection techniques are often evaluated based on the performance of classification models (e.g., algorithms designed to distinguish between multiple classes of instances, such as cancerous vs. noncancerous) built using the selected features, another important criterion which is often neglected is stability, the degree of agreement among a feature selection technique's outputs when there are changes to the dataset. More stable feature selection techniques will give the same features even if aspects of the data change. In this study we consider two different approaches for evaluating the stability of feature selection techniques, with each approach consisting of noise injection followed by feature ranking. The two approaches differ in that the first approach compares the features selected from the noisy datasets with the features selected from the original (clean) dataset, while the second approach performs pairwise comparisons among the results from the noisy datasets. To evaluate these two approaches, we use four biological datasets and employ six commonly-used feature rankers. We draw two primary conclusions from our experiments: First, the rankers show different levels of stability in the face of noise. In particular, the ReliefF ranker has significantly greater stability than the other rankers. Also, we found that both approaches gave the same results in terms of stability patterns, although the first approach had greater stability overall. Additionally, because the first approach is significantly less computationally expensive, future studies may employ a faster technique to gain the same results.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"187 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":"72725479","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.2012.6470220
Ali Alatabbi, Carl Barton, C. Iliopoulos
In large data sets such as genomes from a single species, large sets of reads, and version control data it is often noted that each entry only differs from another by a very small number of variations. This leads to a large set of data with a great deal of redundancy and repetitiveness. Rapid development in DNA sequencing technologies has caused a drastic growth in the size of publicly available sequence databases with such data. DNA sequencing has become so fast and cost-effective that sequencing individual genomes will soon become a common task [9] making querying and storing such sets of data an important task. In this paper, we propose an indexing structure for highly repetitive collections of sequence data based on a multilevel g-gram model. In particular, the proposed algorithm accommodates variations that may occur in the target sequence with respect to the reference sequence. The paper is organized as follows. Section [1] and [2] introduce the basic concepts and go through the related literature. In Section [3] we present notions and facts. Details of the proposed data structure/algorithm will be given in Section [5] and [4], Section [6] discusses complexity analysis and Section [7] gives conclusions of future work.
{"title":"On the repetitive collection indexing problem","authors":"Ali Alatabbi, Carl Barton, C. Iliopoulos","doi":"10.1109/BIBMW.2012.6470220","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470220","url":null,"abstract":"In large data sets such as genomes from a single species, large sets of reads, and version control data it is often noted that each entry only differs from another by a very small number of variations. This leads to a large set of data with a great deal of redundancy and repetitiveness. Rapid development in DNA sequencing technologies has caused a drastic growth in the size of publicly available sequence databases with such data. DNA sequencing has become so fast and cost-effective that sequencing individual genomes will soon become a common task [9] making querying and storing such sets of data an important task. In this paper, we propose an indexing structure for highly repetitive collections of sequence data based on a multilevel g-gram model. In particular, the proposed algorithm accommodates variations that may occur in the target sequence with respect to the reference sequence. The paper is organized as follows. Section [1] and [2] introduce the basic concepts and go through the related literature. In Section [3] we present notions and facts. Details of the proposed data structure/algorithm will be given in Section [5] and [4], Section [6] discusses complexity analysis and Section [7] gives conclusions of future work.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"14 2","pages":"682-687"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72597788","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.2012.6470261
S. Moffatt, R. Cristiano, R. Boyle
In this formulation, Doxorubicin (Dox) was conjugated to Poly (lactic-co-glycolic acid) (PLGA), and formulated via the solvent-diffusion techniques into nanoparticles. The surface of the nanoparticles was subsequently linked with Poly (ethylene glycol) (PEG) and Arg-Gly-Asp (RGD) peptide to achieve both passive and active targeting moieties. The nanoparticles were then tested against several malignant tumor cell lines. The conjugation increased loading efficiency of Dox to PLGA nanoparticles (the encapsulation efficiency was over 85%) and alleviated the drug burst release effect substantially. The drug was released from the polymeric matrix in a sustained release manner over a period of 12 days. The resultant nanoparticles were spherically uniform and well-dispersed. The nanoparticle targeting ability was proven through strong affinity to various integrin-expressing cancer cells, and much less affinity to the low integrin expression cancer cells. The nanoparticles also showed high efficacy in inducing apoptosis in specific malignant cancer cells. Taken together, these multifunctional nanoparticles hold potential to treat malignant integrin-expressing cancers.
在该配方中，阿霉素(Dox)与聚乳酸-羟基乙酸(PLGA)偶联，并通过溶剂扩散技术配制成纳米颗粒。纳米颗粒的表面随后与聚乙二醇(PEG)和精氨酸-甘氨酸- asp (RGD)肽连接，以实现被动和主动靶向部分。然后对纳米颗粒对几种恶性肿瘤细胞系进行了测试。偶联提高了Dox对PLGA纳米颗粒的负载效率(包封效率超过85%)，并显著减轻了药物的爆发释放效应。药物在12天内以缓释方式从聚合物基质中释放出来。合成的纳米颗粒呈球形均匀且分散良好。纳米颗粒对多种表达整合素的癌细胞具有较强的亲和力，而对低表达整合素的癌细胞具有较弱的亲和力。纳米颗粒在诱导特定恶性肿瘤细胞凋亡方面也表现出较高的效果。综上所述，这些多功能纳米颗粒具有治疗表达整合素的恶性癌症的潜力。
{"title":"Combined formulation of Doxorubicin-Arg-Gly-Asp (RGD) and modified PEGylated PLGA-encapsulated nanocarrier improves anti-tumor activity","authors":"S. Moffatt, R. Cristiano, R. Boyle","doi":"10.1109/BIBMW.2012.6470261","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470261","url":null,"abstract":"In this formulation, Doxorubicin (Dox) was conjugated to Poly (lactic-co-glycolic acid) (PLGA), and formulated via the solvent-diffusion techniques into nanoparticles. The surface of the nanoparticles was subsequently linked with Poly (ethylene glycol) (PEG) and Arg-Gly-Asp (RGD) peptide to achieve both passive and active targeting moieties. The nanoparticles were then tested against several malignant tumor cell lines. The conjugation increased loading efficiency of Dox to PLGA nanoparticles (the encapsulation efficiency was over 85%) and alleviated the drug burst release effect substantially. The drug was released from the polymeric matrix in a sustained release manner over a period of 12 days. The resultant nanoparticles were spherically uniform and well-dispersed. The nanoparticle targeting ability was proven through strong affinity to various integrin-expressing cancer cells, and much less affinity to the low integrin expression cancer cells. The nanoparticles also showed high efficacy in inducing apoptosis in specific malignant cancer cells. Taken together, these multifunctional nanoparticles hold potential to treat malignant integrin-expressing cancers.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"1 1","pages":"903-909"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74017150","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.2012.6470331
Feng Yuan, Lizhi Chen, Xiaohong Xu, Qian Wang, W. Fu
This article is to discuss the mechanism of acupuncture for neck type cervical spondylosis (NTCS) based on Heart-Gallbladder theory. NTCS is a clinical frequently-occurring disease, which has a trend of younger. We will explore the fundamental causes of NTCS from the cervical spine biomechanics, and the mechanism of acupuncture for it. The mechanism will be discussed from: the overall adjustment of Spirit and the local adjustment of tendons.
{"title":"Acupuncture for neck type cervical spondylosis on Heart-Gallbladder theory","authors":"Feng Yuan, Lizhi Chen, Xiaohong Xu, Qian Wang, W. Fu","doi":"10.1109/BIBMW.2012.6470331","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470331","url":null,"abstract":"This article is to discuss the mechanism of acupuncture for neck type cervical spondylosis (NTCS) based on Heart-Gallbladder theory. NTCS is a clinical frequently-occurring disease, which has a trend of younger. We will explore the fundamental causes of NTCS from the cervical spine biomechanics, and the mechanism of acupuncture for it. The mechanism will be discussed from: the overall adjustment of Spirit and the local adjustment of tendons.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"82 1","pages":"346-348"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74087754","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.2012.6392633
Bo Song, A. Sacan
A system that can automatically and accurately identify the region of a chronic wound could largely improve conventional clinical practice for the wound diagnosis and treatment. We designed a system that uses color wound photographs taken from the patients, and is capable of automatic image segmentation and wound region identification. Several commonly used segmentation methods are utilized with their parameters fine-tuned automatically to obtain a collection of candidate wound regions. Two different types of Artificial Neural Networks (ANNs), the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) with parameters determined by a cross-validation approach, are then applied with supervised learning in the prediction procedure for the wound identification, and their results are compared. The satisfactory results obtained by this system make it a promising tool to assist in the field of clinical wound evaluation.
{"title":"Automated wound identification system based on image segmentation and Artificial Neural Networks","authors":"Bo Song, A. Sacan","doi":"10.1109/BIBM.2012.6392633","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392633","url":null,"abstract":"A system that can automatically and accurately identify the region of a chronic wound could largely improve conventional clinical practice for the wound diagnosis and treatment. We designed a system that uses color wound photographs taken from the patients, and is capable of automatic image segmentation and wound region identification. Several commonly used segmentation methods are utilized with their parameters fine-tuned automatically to obtain a collection of candidate wound regions. Two different types of Artificial Neural Networks (ANNs), the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) with parameters determined by a cross-validation approach, are then applied with supervised learning in the prediction procedure for the wound identification, and their results are compared. The satisfactory results obtained by this system make it a promising tool to assist in the field of clinical wound evaluation.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"13 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":"84301792","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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