The Experts below are selected from a list of 145722 Experts worldwide ranked by ideXlab platform
Sudhir Kumar - One of the best experts on this subject based on the ideXlab platform.
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image level and group level models for drosophila Gene Expression Pattern annotation
BMC Bioinformatics, 2013Co-Authors: Qian Sun, Sudhir Kumar, Lei Yuan, Sherin Muckatira, Stuart J NewfeldAbstract:Background Drosophila melanogaster has been established as a model organism for investigating the developmental Gene interactions. The spatio-temporal Gene Expression Patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these Expression images will provide biological insights into the Gene functions, interactions, and networks. To facilitate Pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
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learning sparse representations for fruit fly Gene Expression Pattern image annotation and retrieval
BMC Bioinformatics, 2012Co-Authors: Lei Yuan, Zhihua Zhou, Alexander Woodard, Yuan Jiang, Sudhir KumarAbstract:Fruit fly embryoGenesis is one of the best understood animal development systems, and the spatiotemporal Gene Expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal Genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal Gene Expression Patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the Expression Patterns may be made more accurate using keywords. In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly Gene Expression Pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes. We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
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Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2012Co-Authors: Yingxin Li, Shuiwang Ji, Jieping Ye, Sudhir Kumar, Zhihua ZhouAbstract:In the studies of Drosophila embryoGenesis, a large number of two-dimensional digital images of Gene Expression Patterns have been produced to build an atlas of spatio-temporal Gene Expression dynamics across developmental time. Gene Expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding Gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local Expression Patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila Gene Expression Pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.
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drosophila Gene Expression Pattern annotation through multi instance multi label learning
International Joint Conference on Artificial Intelligence, 2009Co-Authors: Yingxin Li, Shuiwang Ji, Jieping Ye, Sudhir Kumar, Zhihua ZhouAbstract:The Berkeley Drosophila Genome Project (BDGP) has produced a large number of Gene Expression Patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of Expression Patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with a new machine learning framework, Multi-Instance Multi-Label learning (MIML). We propose a new MIML support vector machine to solve the problems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila Gene Expression Pattern annotation methods.
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drosophila Gene Expression Pattern annotation using sparse features and term term interactions
Knowledge Discovery and Data Mining, 2009Co-Authors: Lei Yuan, Zhihua Zhou, Sudhir KumarAbstract:The Drosophila Gene Expression Pattern images document the spatial and temporal dynamics of Gene Expression and they are valuable tools for explicating the Gene functions, interaction, and networks during Drosophila embryoGenesis. To provide text-based Pattern searching, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with ontology terms manually by human curators. We present a systematic approach for automating this task, because the number of images needing text descriptions is now rapidly increasing. We consider both improved feature representation and novel learning formulation to boost the annotation performance. For feature representation, we adapt the bag-of-words scheme commonly used in visual recognition problems so that the image group information in the BDGP study is retained. Moreover, images from multiple views can be integrated naturally in this representation. To reduce the quantization error caused by the bag-of-words representation, we propose an improved feature representation scheme based on the sparse learning technique. In the design of learning formulation, we propose a local regularization framework that can incorporate the correlations among terms explicitly. We further show that the resulting optimization problem admits an analytical solution. Experimental results show that the representation based on sparse learning outperforms the bag-of-words representation significantly. Results also show that incorporation of the term-term correlations improves the annotation performance consistently.
Zhihua Zhou - One of the best experts on this subject based on the ideXlab platform.
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learning sparse representations for fruit fly Gene Expression Pattern image annotation and retrieval
BMC Bioinformatics, 2012Co-Authors: Lei Yuan, Zhihua Zhou, Alexander Woodard, Yuan Jiang, Sudhir KumarAbstract:Fruit fly embryoGenesis is one of the best understood animal development systems, and the spatiotemporal Gene Expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal Genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal Gene Expression Patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the Expression Patterns may be made more accurate using keywords. In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly Gene Expression Pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes. We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
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Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2012Co-Authors: Yingxin Li, Shuiwang Ji, Jieping Ye, Sudhir Kumar, Zhihua ZhouAbstract:In the studies of Drosophila embryoGenesis, a large number of two-dimensional digital images of Gene Expression Patterns have been produced to build an atlas of spatio-temporal Gene Expression dynamics across developmental time. Gene Expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding Gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local Expression Patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila Gene Expression Pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.
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drosophila Gene Expression Pattern annotation through multi instance multi label learning
International Joint Conference on Artificial Intelligence, 2009Co-Authors: Yingxin Li, Shuiwang Ji, Jieping Ye, Sudhir Kumar, Zhihua ZhouAbstract:The Berkeley Drosophila Genome Project (BDGP) has produced a large number of Gene Expression Patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of Expression Patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with a new machine learning framework, Multi-Instance Multi-Label learning (MIML). We propose a new MIML support vector machine to solve the problems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila Gene Expression Pattern annotation methods.
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drosophila Gene Expression Pattern annotation using sparse features and term term interactions
Knowledge Discovery and Data Mining, 2009Co-Authors: Lei Yuan, Zhihua Zhou, Sudhir KumarAbstract:The Drosophila Gene Expression Pattern images document the spatial and temporal dynamics of Gene Expression and they are valuable tools for explicating the Gene functions, interaction, and networks during Drosophila embryoGenesis. To provide text-based Pattern searching, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with ontology terms manually by human curators. We present a systematic approach for automating this task, because the number of images needing text descriptions is now rapidly increasing. We consider both improved feature representation and novel learning formulation to boost the annotation performance. For feature representation, we adapt the bag-of-words scheme commonly used in visual recognition problems so that the image group information in the BDGP study is retained. Moreover, images from multiple views can be integrated naturally in this representation. To reduce the quantization error caused by the bag-of-words representation, we propose an improved feature representation scheme based on the sparse learning technique. In the design of learning formulation, we propose a local regularization framework that can incorporate the correlations among terms explicitly. We further show that the resulting optimization problem admits an analytical solution. Experimental results show that the representation based on sparse learning outperforms the bag-of-words representation significantly. Results also show that incorporation of the term-term correlations improves the annotation performance consistently.
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a bag of words approach for drosophila Gene Expression Pattern annotation
BMC Bioinformatics, 2009Co-Authors: Zhihua Zhou, Sudhir KumarAbstract:Drosophila Gene Expression Pattern images document the spatiotemporal dynamics of Gene Expression during embryoGenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate Pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task. We present a computational method to annotate Gene Expression Pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on Pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic Patterns from BDGP data demonstrate that our method significantly outperforms other methods. The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
Pernilla Eliasson - One of the best experts on this subject based on the ideXlab platform.
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Simvastatin and atorvastatin reduce the mechanical properties of tendon constructs in vitro and introduce catabolic changes in the Gene Expression Pattern
2017Co-Authors: Pernilla Eliasson, Rene B. Svensson, Antonis Giannopoulos, Christian Eismark, Michael Kjær, Peter Schjerling, Katja M. HeinemeierAbstract:Treatment with lipid-lowering drugs, statins, is common all over the world. Lately, the occurrence of spontaneous tendon ruptures or tendinosis have suggested a negative influence of statins upon tendon tissue. But how statins might influence tendons is not clear. In the present study, we investigated the effect of statin treatment on mechanical strength, cell proliferation, collagen content and Gene Expression Pattern in a tendon-like tissue made from human tenocytes in vitro. Human tendon fibroblasts were grown in a 3D tissue culture model (tendon constructs), and treated with either simvastatin or atorvastatin, low or high dose, respectively, for up to seven days. After seven days of treatment, mechanical testing of the constructs was performed. Collagen content and cell proliferation were also determined. mRNA levels of several target Genes were measured after one or seven days. The maximum force and stiffness were reduced by both statins after 7 days (p
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microtrauma stimulates rat achilles tendon healing via an early Gene Expression Pattern similar to mechanical loading
Journal of Applied Physiology, 2014Co-Authors: Malin Hammerman, Pernilla Eliasson, Per AspenbergAbstract:Mechanical loading increases the strength of healing tendons, but also induces small localized bleedings. Therefore, it is unclear if increased strength after loading is a response to mechanotransd...
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microtrauma stimulates rat achilles tendon healing via an early Gene Expression Pattern similar to mechanical loading
Journal of Applied Physiology, 2014Co-Authors: Malin Hammerman, Pernilla Eliasson, Per AspenbergAbstract:Mechanical loading increases the strength of healing tendons, but also induces small localized bleedings. Therefore, it is unclear if increased strength after loading is a response to mechanotransduction or microtrauma. We have previously found only five Genes to be up-regulated 15 min after a single loading episode, of them four were transcription factors. These Genes are followed by hundreds of Genes after 3 h, many of them involved in inflammation. We now compared healing in mechanically unloaded tendons with or without added microtrauma induced by needling of the healing tissue. Nineteen rats received Botox into the calf muscle to reduce loading, and the Achilles tendon was transected. Ten rats were randomized to needling days 2-5. Mechanical testing on day 8 showed increased strength by 45% in the needling group. Next, another 24 rats were similarly unloaded, and 16 randomized to needling on day 5 after transection. Nineteen characteristic Genes, known to be regulated by loading in this model, were analyzed by qRT-PCR. Four of these Genes were regulated 15 min after needling. Three of them (Egr1, c-Fos, Rgs1) were among the five regulated Genes after loading in a previous study. Sixteen of the 19 Genes were regulated after 3 h, in the same way as after loading. In conclusion, needling increased strength, and there was a striking similarity between the Gene Expression response to needling and mechanical loading. This suggests that the response to loading in early tendon healing can, at least in part, be a response to microtrauma.
Norbert Frey - One of the best experts on this subject based on the ideXlab platform.
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Gene Expression Pattern in biomechanically stretched cardiomyocytes evidence for a stretch specific Gene program
Hypertension, 2008Co-Authors: Derk Frank, Christian Kuhn, Benedikt Brors, Christiane Hanselmann, Mark Ludde, Hugo A Katus, Norbert FreyAbstract:Biomechanical stress ie, attributable to pressure overload, leads to cardiac hypertrophy and may ultimately cause heart failure. Yet, it is still unclear how mechanical stress is sensed and transduced on the molecular level. To systematically elucidate the underlying signal transduction pathways, we analyzed the Gene Expression profile of stretched cardiomyocytes on a genome-wide scale in comparison with other inducers of hypertrophy such as pharmacological stimulation. Neonatal rat ventricular cardiomyocytes were either stretched biaxially or stimulated with phenylephrine (PE), both resulting in a similar degree of hypertrophy. Microarray analyses revealed 164 Genes >2.0-fold up- and 21 Genes P 1 receptor blocker irbesartan markedly blunted stretch-mediated GDF15 and Hmox1 upregulation, suggesting that the angiotensin receptor tranduces the biomechanical induction of these Genes. In conclusion, we report a comprehensive Gene Expression profile of cardiomyocytes subjected to biomechanical stress in comparison with pharmacologically induced hypertrophy. Our data imply that a stretch-specific Gene program exists, which is mediated, at least in part, by angiotensin II–dependent signaling.
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Gene Expression Pattern in biomechanically stretched cardiomyocytes evidence for a stretch specific Gene program
Hypertension, 2008Co-Authors: Derk Frank, Christian Kuhn, Benedikt Brors, Christiane Hanselmann, Mark Ludde, Hugo A Katus, Norbert FreyAbstract:Biomechanical stress ie, attributable to pressure overload, leads to cardiac hypertrophy and may ultimately cause heart failure. Yet, it is still unclear how mechanical stress is sensed and transduced on the molecular level. To systematically elucidate the underlying signal transduction pathways, we analyzed the Gene Expression profile of stretched cardiomyocytes on a genome-wide scale in comparison with other inducers of hypertrophy such as pharmacological stimulation. Neonatal rat ventricular cardiomyocytes were either stretched biaxially or stimulated with phenylephrine (PE), both resulting in a similar degree of hypertrophy. Microarray analyses revealed 164 Genes >2.0-fold up- and 21 Genes <0.5-fold downregulated (P<0.01). Differential Expression was confirmed by real-time polymerase chain reaction. Genes of the "fetal Gene program" such as BNP were induced by both stretch (4.2x) and PE (2.9x). We also verified upregulation of known stretch-responsive Genes, including HSP70 (20.9x) and c-myc (3.0x). Moreover, several Genes were found to be preferentially induced by stretch, such as the cardioprotective cytokine GDF15 (24.8x) and heme oxygenase 1 (Hmox1, 10.8x; both confirmed on protein level). Neither PE nor endothelin-1 upregulated GDF15 and Hmox1, whereas angiotensin II significantly induced both Genes. Conversely, the AT(1) receptor blocker irbesartan markedly blunted stretch-mediated GDF15 and Hmox1 upregulation, suggesting that the angiotensin receptor transduces the biomechanical induction of these Genes. In conclusion, we report a comprehensive Gene Expression profile of cardiomyocytes subjected to biomechanical stress in comparison with pharmacologically induced hypertrophy. Our data imply that a stretch-specific Gene program exists, which is mediated, at least in part, by angiotensin II-dependent signaling.
Derk Frank - One of the best experts on this subject based on the ideXlab platform.
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Gene Expression Pattern in biomechanically stretched cardiomyocytes evidence for a stretch specific Gene program
Hypertension, 2008Co-Authors: Derk Frank, Christian Kuhn, Benedikt Brors, Christiane Hanselmann, Mark Ludde, Hugo A Katus, Norbert FreyAbstract:Biomechanical stress ie, attributable to pressure overload, leads to cardiac hypertrophy and may ultimately cause heart failure. Yet, it is still unclear how mechanical stress is sensed and transduced on the molecular level. To systematically elucidate the underlying signal transduction pathways, we analyzed the Gene Expression profile of stretched cardiomyocytes on a genome-wide scale in comparison with other inducers of hypertrophy such as pharmacological stimulation. Neonatal rat ventricular cardiomyocytes were either stretched biaxially or stimulated with phenylephrine (PE), both resulting in a similar degree of hypertrophy. Microarray analyses revealed 164 Genes >2.0-fold up- and 21 Genes P 1 receptor blocker irbesartan markedly blunted stretch-mediated GDF15 and Hmox1 upregulation, suggesting that the angiotensin receptor tranduces the biomechanical induction of these Genes. In conclusion, we report a comprehensive Gene Expression profile of cardiomyocytes subjected to biomechanical stress in comparison with pharmacologically induced hypertrophy. Our data imply that a stretch-specific Gene program exists, which is mediated, at least in part, by angiotensin II–dependent signaling.
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Gene Expression Pattern in biomechanically stretched cardiomyocytes evidence for a stretch specific Gene program
Hypertension, 2008Co-Authors: Derk Frank, Christian Kuhn, Benedikt Brors, Christiane Hanselmann, Mark Ludde, Hugo A Katus, Norbert FreyAbstract:Biomechanical stress ie, attributable to pressure overload, leads to cardiac hypertrophy and may ultimately cause heart failure. Yet, it is still unclear how mechanical stress is sensed and transduced on the molecular level. To systematically elucidate the underlying signal transduction pathways, we analyzed the Gene Expression profile of stretched cardiomyocytes on a genome-wide scale in comparison with other inducers of hypertrophy such as pharmacological stimulation. Neonatal rat ventricular cardiomyocytes were either stretched biaxially or stimulated with phenylephrine (PE), both resulting in a similar degree of hypertrophy. Microarray analyses revealed 164 Genes >2.0-fold up- and 21 Genes <0.5-fold downregulated (P<0.01). Differential Expression was confirmed by real-time polymerase chain reaction. Genes of the "fetal Gene program" such as BNP were induced by both stretch (4.2x) and PE (2.9x). We also verified upregulation of known stretch-responsive Genes, including HSP70 (20.9x) and c-myc (3.0x). Moreover, several Genes were found to be preferentially induced by stretch, such as the cardioprotective cytokine GDF15 (24.8x) and heme oxygenase 1 (Hmox1, 10.8x; both confirmed on protein level). Neither PE nor endothelin-1 upregulated GDF15 and Hmox1, whereas angiotensin II significantly induced both Genes. Conversely, the AT(1) receptor blocker irbesartan markedly blunted stretch-mediated GDF15 and Hmox1 upregulation, suggesting that the angiotensin receptor transduces the biomechanical induction of these Genes. In conclusion, we report a comprehensive Gene Expression profile of cardiomyocytes subjected to biomechanical stress in comparison with pharmacologically induced hypertrophy. Our data imply that a stretch-specific Gene program exists, which is mediated, at least in part, by angiotensin II-dependent signaling.