Pub Date : 2012-10-04DOI: 10.1109/BIBM.2012.6392680
Nimit Dhulekar, Lauren M. Bange, Abiurami Baskaran, D. Yuan, Basak Oztan, B. Yener, Shayoni Ray, M. Larsen
In this paper, we introduce a biologically motivated dynamic graph-based growth model to describe and predict the stages of cleft formation during the process of branching morphogenesis in the submandibular mouse gland (SMG) from 3 hrs after embryonic day E12 to 8 hrs after embryonic day E12, which can be considered as E12.5. Branching morphogenesis is the process by which many mammalian exocrine and endocrine glands undergo significant morphological transformations, from a primary bud to an adult organ. Although many studies have investigated the cellular and molecular mechanisms driving branching morphogenesis, it is not clear how the shape changes that are inherent to establishing organ structure are produced. Using morphological features extracted from sequential images of SMG organ cultures we were able to develop a dynamic graph-based predictive model that is able to mimic the process of cleft formation and predict the final state. In addition, we compare our model to a state-of-the-art Glazier-Graner-Hogeweg (GGH) simulative tool, and demonstrate that the dynamic graph-based predictive model has comparable accuracy in modeling growth of clefts across SMG developmental stages, as well as faster convergence to the target SMG morphology.
{"title":"A novel dynamic graph-based computational model for predicting salivary gland branching morphogenesis","authors":"Nimit Dhulekar, Lauren M. Bange, Abiurami Baskaran, D. Yuan, Basak Oztan, B. Yener, Shayoni Ray, M. Larsen","doi":"10.1109/BIBM.2012.6392680","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392680","url":null,"abstract":"In this paper, we introduce a biologically motivated dynamic graph-based growth model to describe and predict the stages of cleft formation during the process of branching morphogenesis in the submandibular mouse gland (SMG) from 3 hrs after embryonic day E12 to 8 hrs after embryonic day E12, which can be considered as E12.5. Branching morphogenesis is the process by which many mammalian exocrine and endocrine glands undergo significant morphological transformations, from a primary bud to an adult organ. Although many studies have investigated the cellular and molecular mechanisms driving branching morphogenesis, it is not clear how the shape changes that are inherent to establishing organ structure are produced. Using morphological features extracted from sequential images of SMG organ cultures we were able to develop a dynamic graph-based predictive model that is able to mimic the process of cleft formation and predict the final state. In addition, we compare our model to a state-of-the-art Glazier-Graner-Hogeweg (GGH) simulative tool, and demonstrate that the dynamic graph-based predictive model has comparable accuracy in modeling growth of clefts across SMG developmental stages, as well as faster convergence to the target SMG morphology.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85686844","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.2012.6392724
Bobby McKnight, I. Arpinar
The association of experimental data with domain knowledge expressed in ontologies facilitates information aggregation, meaningful querying and knowledge discovery to aid in the process of analyzing the extensive amount of interconnected data available for genome projects. TcruziKB is an ontology-driven problem solving system to describe and provide access to the data available for a traditional genome database for the parasite Trypanosoma Cruzi. The problem solving environment enables many advanced search and information presentation features that enable complex queries that would be difficult, if not impossible, to execute without semantic enhancements. However the problem solving features do not only improve the quality of the information retrieved but also reduces the strain on the user by improving usability over the standard system.
{"title":"Linking and querying genomic datasets using natural language","authors":"Bobby McKnight, I. Arpinar","doi":"10.1109/BIBM.2012.6392724","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392724","url":null,"abstract":"The association of experimental data with domain knowledge expressed in ontologies facilitates information aggregation, meaningful querying and knowledge discovery to aid in the process of analyzing the extensive amount of interconnected data available for genome projects. TcruziKB is an ontology-driven problem solving system to describe and provide access to the data available for a traditional genome database for the parasite Trypanosoma Cruzi. The problem solving environment enables many advanced search and information presentation features that enable complex queries that would be difficult, if not impossible, to execute without semantic enhancements. However the problem solving features do not only improve the quality of the information retrieved but also reduces the strain on the user by improving usability over the standard system.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84069187","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.2012.6470268
Sameh Saleh, Brian S. Olson, Amarda Shehu
Structural characterization of the protein native state is an important problem in computational biology. Thermodynamically, the native state is that of lowest free energy in the protein conformational space. Predicting it ab initio from the amino-acid sequence can be posed as an optimization problem that has proven to be NP-hard. Due to imperfect modeling of interatomic interactions, the native state often does not correspond to the global minimum. As a result, the goal in ab-initio protein structure prediction is to first arrive at a diverse ensemble of low-energy (decoy) conformations potentially relevant for the native state. Decoys are often computed using a coarse-grained energy function that expedites sampling of low-energy conformations. Select decoys are then refined with heavy-duty protocols using fine-grained energy functions to allow prediction of the native state.
{"title":"An evolutionary framework to sample near-native protein conformations","authors":"Sameh Saleh, Brian S. Olson, Amarda Shehu","doi":"10.1109/BIBMW.2012.6470268","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470268","url":null,"abstract":"Structural characterization of the protein native state is an important problem in computational biology. Thermodynamically, the native state is that of lowest free energy in the protein conformational space. Predicting it ab initio from the amino-acid sequence can be posed as an optimization problem that has proven to be NP-hard. Due to imperfect modeling of interatomic interactions, the native state often does not correspond to the global minimum. As a result, the goal in ab-initio protein structure prediction is to first arrive at a diverse ensemble of low-energy (decoy) conformations potentially relevant for the native state. Decoys are often computed using a coarse-grained energy function that expedites sampling of low-energy conformations. Select decoys are then refined with heavy-duty protocols using fine-grained energy functions to allow prediction of the native state.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84107677","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.2012.6470306
Francisco I. Pena, Young-Rae Cho
The generation of protein-protein interactions (PPIs) has created the need for efficient computational approaches that can discover highly modular clusters of good quality. These clusters represent protein complexes or functional modules. There are a number of seed-growth style algorithms that exist to identify protein complexes from the genome-wide PPI networks. However, these methods lose accuracy when the networks are comparatively large and have complex connectivity. To combat the noise that exists in these large PPI networks, we propose an improvement to the graph entropy approach which is one of the seed-growth style algorithms. As a novel information-theoretic definition, Graph Entropy is a measure of the structural complexity of a graph. For example, the loss of entropy represents an increase in modularity of the graph. The original algorithm only considers the interconnected nature of vertices, but the new modified definition now considers edge weights. These edge weights are achieved by measuring the semantic similarity of PPIs. The weighted graph entropy approach is applied to the S. cerevisiae PPI data set from BioGRID. The output clusters are compared with known protein complexes so that we can calculate /-scores and use them to evaluate the clusters accuracy. The proposed improvement to the graph entropy approach proves to enhance the quality of clusters as potential protein complexes when compared to the other seed-growth style algorithms.
{"title":"Improvements of graph entropy approach to detect protein complexes by ontological analysis of PPIs","authors":"Francisco I. Pena, Young-Rae Cho","doi":"10.1109/BIBMW.2012.6470306","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470306","url":null,"abstract":"The generation of protein-protein interactions (PPIs) has created the need for efficient computational approaches that can discover highly modular clusters of good quality. These clusters represent protein complexes or functional modules. There are a number of seed-growth style algorithms that exist to identify protein complexes from the genome-wide PPI networks. However, these methods lose accuracy when the networks are comparatively large and have complex connectivity. To combat the noise that exists in these large PPI networks, we propose an improvement to the graph entropy approach which is one of the seed-growth style algorithms. As a novel information-theoretic definition, Graph Entropy is a measure of the structural complexity of a graph. For example, the loss of entropy represents an increase in modularity of the graph. The original algorithm only considers the interconnected nature of vertices, but the new modified definition now considers edge weights. These edge weights are achieved by measuring the semantic similarity of PPIs. The weighted graph entropy approach is applied to the S. cerevisiae PPI data set from BioGRID. The output clusters are compared with known protein complexes so that we can calculate /-scores and use them to evaluate the clusters accuracy. The proposed improvement to the graph entropy approach proves to enhance the quality of clusters as potential protein complexes when compared to the other seed-growth style algorithms.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78068143","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.2012.6470297
Q. Tran, V. Andreev, Ariel Fernández
As the heterogeneous nature of cancer starts to emerge, the focus of molecular therapy is shifting progressively towards multi-target drugs. For example, drug-based interference with several signaling pathways controlling different aspects of cell fate provides a multi-pronged attack that is proving more effective than magic bullets in hampering development and progression of malignancy. Such therapeutic agents typically target kinases, the basic signal transducers of the cell. Because kinases share common evolutionary backgrounds, they also share structural attributes, making it difficult for drugs to tell apart paralogs of clinical importance from off-target kinases. Thus, multi-target kinase inhibitors (KIs) tend to have undesired cross-reactivities with potentially lethal or debilitating side effects. As multi-target therapies are favored, a pressing issue takes the stakes: which type of clinical impact can only be achieved with a promiscuous drug, and conversely, which clinical effect lends itself to drug specificity? Combining statistical analysis with data mining and machine learning, we determine extremely small inferential bases with 3-5 targets that enable a kinomewide assessment of promiscuity and specificity with over 97% accuracy. Thus, the likelihood of side effects in molecular therapy arising from undesired cross-activities is pivotally dependent on the intended clinical impact restricted to checking a few relevant targets.
{"title":"Likelihood of side effects depends on desired clinical impact: Affinities within a very small set of targets enables inference of promiscuity or specificity of kinase inhibitors","authors":"Q. Tran, V. Andreev, Ariel Fernández","doi":"10.1109/BIBMW.2012.6470297","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470297","url":null,"abstract":"As the heterogeneous nature of cancer starts to emerge, the focus of molecular therapy is shifting progressively towards multi-target drugs. For example, drug-based interference with several signaling pathways controlling different aspects of cell fate provides a multi-pronged attack that is proving more effective than magic bullets in hampering development and progression of malignancy. Such therapeutic agents typically target kinases, the basic signal transducers of the cell. Because kinases share common evolutionary backgrounds, they also share structural attributes, making it difficult for drugs to tell apart paralogs of clinical importance from off-target kinases. Thus, multi-target kinase inhibitors (KIs) tend to have undesired cross-reactivities with potentially lethal or debilitating side effects. As multi-target therapies are favored, a pressing issue takes the stakes: which type of clinical impact can only be achieved with a promiscuous drug, and conversely, which clinical effect lends itself to drug specificity? Combining statistical analysis with data mining and machine learning, we determine extremely small inferential bases with 3-5 targets that enable a kinomewide assessment of promiscuity and specificity with over 97% accuracy. Thus, the likelihood of side effects in molecular therapy arising from undesired cross-activities is pivotally dependent on the intended clinical impact restricted to checking a few relevant targets.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73282547","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.2012.6392692
N. Balov
Using gene expression profiles for predicting phenotypic differences that result from cell specializations or diseases poses an important statistical problem. Graphical statistical models such as Bayesian networks may improve the prediction accuracy by identifying alternations in gene regulations due to the experimental conditions. We consider a discrete Bayesian network model that represents pairs of experimental classes by networks that share a common graph structure but have distinct probability tables. We apply a score-based network estimation procedure that maximizes the KL-divergence between class probabilities. The proposed method performs an implicit model selection and does not involve additional complexity penalization parameters. Classification of gene profiles is performed by comparing the likelihood of the estimated class networks. We evaluate the performance of the new model against support vector machine, penalized linear regression and linear Gaussian networks. The classifiers are compared by prediction accuracy across 9 independent data sets from breast and lung cancer studies. The proposed method demonstrates a strong performance against the competitors.
{"title":"A discrete Bayesian network framework for discrimination of gene expression profiles","authors":"N. Balov","doi":"10.1109/BIBM.2012.6392692","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392692","url":null,"abstract":"Using gene expression profiles for predicting phenotypic differences that result from cell specializations or diseases poses an important statistical problem. Graphical statistical models such as Bayesian networks may improve the prediction accuracy by identifying alternations in gene regulations due to the experimental conditions. We consider a discrete Bayesian network model that represents pairs of experimental classes by networks that share a common graph structure but have distinct probability tables. We apply a score-based network estimation procedure that maximizes the KL-divergence between class probabilities. The proposed method performs an implicit model selection and does not involve additional complexity penalization parameters. Classification of gene profiles is performed by comparing the likelihood of the estimated class networks. We evaluate the performance of the new model against support vector machine, penalized linear regression and linear Gaussian networks. The classifiers are compared by prediction accuracy across 9 independent data sets from breast and lung cancer studies. The proposed method demonstrates a strong performance against the competitors.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73324624","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.2012.6392610
Okko Lohmann, T. Luhmann, A. Hein
This paper presents a novel approach to fully automate the Timed Up and Go Assessment Test (TUG) in professional environments. The approach, called Skeleton Timed Up and Go (sTUG), is based on the usage of two Kinect for Xbox 360 sensors. sTUG supports the execution and documentation of the traditional TUG assessment test and furthermore calculates nine events, which demarcate the five main components during a run. On two days we conducted an experiment with five elderly aged 70-84 and four males aged 29-31 in the activity laboratory of the OFFIS Institute, Oldenburg to proof the reliability and feasibility of the system. Results demonstrate that sTUG can precisely measure the total duration of traditional TUG and is capable of detecting accurately nine motion events which demarcate the components during a run.
本文提出了一种在专业环境中实现完全自动化的time Up and Go评估测试(TUG)的新方法。这种方法被称为Skeleton Timed Up and Go (sTUG)，是基于Xbox 360的两个Kinect传感器的使用。sTUG支持传统TUG评估测试的执行和文档编制，并进一步计算9个事件，这些事件在运行期间划分了5个主要组件。我们用两天的时间在Oldenburg OFFIS Institute的活动实验室对5名70-84岁的老年人和4名29-31岁的男性进行了实验，以证明系统的可靠性和可行性。结果表明，sTUG可以精确地测量传统TUG的总持续时间，并能够准确地检测出在一次运行中划分组件的9个运动事件。
{"title":"Skeleton Timed Up and Go","authors":"Okko Lohmann, T. Luhmann, A. Hein","doi":"10.1109/BIBM.2012.6392610","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392610","url":null,"abstract":"This paper presents a novel approach to fully automate the Timed Up and Go Assessment Test (TUG) in professional environments. The approach, called Skeleton Timed Up and Go (sTUG), is based on the usage of two Kinect for Xbox 360 sensors. sTUG supports the execution and documentation of the traditional TUG assessment test and furthermore calculates nine events, which demarcate the five main components during a run. On two days we conducted an experiment with five elderly aged 70-84 and four males aged 29-31 in the activity laboratory of the OFFIS Institute, Oldenburg to proof the reliability and feasibility of the system. Results demonstrate that sTUG can precisely measure the total duration of traditional TUG and is capable of detecting accurately nine motion events which demarcate the components during a run.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77067605","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.2012.6470380
Kevin Molloy, Amarda Shehu
Characterization of transition trajectories that take a protein between different functional states is an important yet challenging problem in computational biology. Approaches based on Molecular Dynamics can obtain the most detailed and accurate information but at considerable computational cost. To address the cost, sampling-based path planning methods adapted from robotics forego protein dynamics and seek instead conformational paths, operating under the assumption that dynamics can be incorporated later to transform paths to transition trajectories. Existing methods focus either on short peptides or large proteins; on the latter, coarse representations simplify the search space. Here we present a robotics-inspired tree-based method to sample conformational paths that connect known structural states of small- to medium- size proteins. We address the dimensionality of the search space using molecular fragment replacement to efficiently obtain physically-realistic conformations. The method grows a tree in conformational space rooted at a given conformation and biases the growth of the tree to steer it to a given goal conformation. Different bias schemes are investigated for their efficacy. Experiments on proteins up to 214 amino acids long with known functionally-relevant states more than 13ÅA apart show that the method effectively obtains conformational paths connecting significantly different structural states.
{"title":"A robotics-inspired method to sample conformational paths connecting known functionally-relevant structures in protein systems","authors":"Kevin Molloy, Amarda Shehu","doi":"10.1109/BIBMW.2012.6470380","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470380","url":null,"abstract":"Characterization of transition trajectories that take a protein between different functional states is an important yet challenging problem in computational biology. Approaches based on Molecular Dynamics can obtain the most detailed and accurate information but at considerable computational cost. To address the cost, sampling-based path planning methods adapted from robotics forego protein dynamics and seek instead conformational paths, operating under the assumption that dynamics can be incorporated later to transform paths to transition trajectories. Existing methods focus either on short peptides or large proteins; on the latter, coarse representations simplify the search space. Here we present a robotics-inspired tree-based method to sample conformational paths that connect known structural states of small- to medium- size proteins. We address the dimensionality of the search space using molecular fragment replacement to efficiently obtain physically-realistic conformations. The method grows a tree in conformational space rooted at a given conformation and biases the growth of the tree to steer it to a given goal conformation. Different bias schemes are investigated for their efficacy. Experiments on proteins up to 214 amino acids long with known functionally-relevant states more than 13ÅA apart show that the method effectively obtains conformational paths connecting significantly different structural states.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82457081","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.2012.6392690
S. Li, James O. Nyagilo, D. Dave, Jean X. Gao
Quantitative analysis of Raman spectra using Surface Enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Square Regression (PLSR) methods are the state-of-the-art methods. But they rely on the whole intensities of Raman spectra and can not avoid the instable background. In this paper we design a new CWT-PLSR algorithm that uses mixing concentrations and the average continuous wavelet transform (CWT) coefficients of Raman spectra to do PLSR. The average CWT coefficients with a Mexican hat mother wavelet are robust representations of the Raman peaks, and the method can reduce the influences of the instable baseline and random noises during the prediction process. In the end, the algorithm is tested on three Raman spectrum data sets with three cross-validation methods, and the results show its robustness and effectiveness.
{"title":"CWT-PLSR for quantitative analysis of Raman spectrum","authors":"S. Li, James O. Nyagilo, D. Dave, Jean X. Gao","doi":"10.1109/BIBM.2012.6392690","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392690","url":null,"abstract":"Quantitative analysis of Raman spectra using Surface Enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Square Regression (PLSR) methods are the state-of-the-art methods. But they rely on the whole intensities of Raman spectra and can not avoid the instable background. In this paper we design a new CWT-PLSR algorithm that uses mixing concentrations and the average continuous wavelet transform (CWT) coefficients of Raman spectra to do PLSR. The average CWT coefficients with a Mexican hat mother wavelet are robust representations of the Raman peaks, and the method can reduce the influences of the instable baseline and random noises during the prediction process. In the end, the algorithm is tested on three Raman spectrum data sets with three cross-validation methods, and the results show its robustness and effectiveness.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82086240","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.2012.6470291
B. Godshall, B. Chen
Conformational changes make the comparison of protein structures difficult. Algorithms that identify small differences in protein structures to identify influences on specificity are particularly affected by molecular flexibility. However, such algorithms typically compare proteins with identical function and varying specificity, causing them to focus on closely related proteins rather than the remote evolutionary homologs sought by most comparison algorithms. This focus inspired us to ask if structure prediction algorithms, which more accurately predict the structures of close evolutionary neighbors, can be used to "remodel" existing structures with the same template, to make the comparison of their binding sites more accurate. Our results, on the enolase superfamily and the tyrosine kinases, reveal that this approach to error reduction is indeed possible, enabling our methods to identify influences on specificity in protein structures that originally could not be compared.
{"title":"Improving accuracy in binding site comparison with homology modeling","authors":"B. Godshall, B. Chen","doi":"10.1109/BIBMW.2012.6470291","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470291","url":null,"abstract":"Conformational changes make the comparison of protein structures difficult. Algorithms that identify small differences in protein structures to identify influences on specificity are particularly affected by molecular flexibility. However, such algorithms typically compare proteins with identical function and varying specificity, causing them to focus on closely related proteins rather than the remote evolutionary homologs sought by most comparison algorithms. This focus inspired us to ask if structure prediction algorithms, which more accurately predict the structures of close evolutionary neighbors, can be used to \"remodel\" existing structures with the same template, to make the comparison of their binding sites more accurate. Our results, on the enolase superfamily and the tyrosine kinases, reveal that this approach to error reduction is indeed possible, enabling our methods to identify influences on specificity in protein structures that originally could not be compared.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78943518","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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