Pub Date : 2007-04-30DOI: 10.1002/9780470191637.ch16
A. Konagaya, R. Azuma, Ryo Umetsu, Shingo Ohki, Fumikazu Konishi, Kazumi Matsumura, S. Yoshikawa
{"title":"Seeing Is Knowing: Visualization of Parameter-Parameter Dependencies in Biomedical Network Models","authors":"A. Konagaya, R. Azuma, Ryo Umetsu, Shingo Ohki, Fumikazu Konishi, Kazumi Matsumura, S. Yoshikawa","doi":"10.1002/9780470191637.ch16","DOIUrl":"https://doi.org/10.1002/9780470191637.ch16","url":null,"abstract":"","PeriodicalId":164785,"journal":{"name":"Grid Computing for Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130215841","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 : 2007-04-30DOI: 10.1002/9780470191637.CH1
M. Romberg, E. Benfenati, W. Dubitzky
The number of chemicals in society is largely increasing, and therewith the risk of being exposed to chemicals increases. Knowledge of possible toxic effects of these chemicals is vital, as are the measurement and assessment of the effects and related risks. Within the European Union, the Registration, Evaluation, and Authorisation of Chemicals (REACH) legislation [1] places responsibility on the chemical industries to properly assess the risks associated with their products. It has been estimated that about 30,000 new chemicals will be put on the European market in the coming years. The assessment of these chemicals would cost billions of euros and involve the use of millions of animals. REACH also aims to ensure that risks from substances of very high concern (SVHC) are properly controlled or that the substances are substituted. To match REACH requirements, fast and reliable methods with reproducible results are crucial, and regulatory bodies would be able to approve results. Property prediction and modeling will play an important role in this case [2]. Toxicology, the study of harmful interactions between chemicals and biological systems [3], uses more and more computer models. These models are based on already available data and help to reduce in vivo testing. Toxicity modeling and its data have many applications such as characterizing hazards, assessing environmental risks, and identifying potential lead components in drug discovery. A well-established method for toxicity modeling is quantitative structure–activity relationship (QSAR) or quantitative structure–property relationship (QSPR) [4,5]. On the basis of the available measured and calculated properties or activities and descriptors of compounds, predictive models for a certain property are built, which are then used to predict that
{"title":"Open Computing Grid for Molecular Sciences","authors":"M. Romberg, E. Benfenati, W. Dubitzky","doi":"10.1002/9780470191637.CH1","DOIUrl":"https://doi.org/10.1002/9780470191637.CH1","url":null,"abstract":"The number of chemicals in society is largely increasing, and therewith the risk of being exposed to chemicals increases. Knowledge of possible toxic effects of these chemicals is vital, as are the measurement and assessment of the effects and related risks. Within the European Union, the Registration, Evaluation, and Authorisation of Chemicals (REACH) legislation [1] places responsibility on the chemical industries to properly assess the risks associated with their products. It has been estimated that about 30,000 new chemicals will be put on the European market in the coming years. The assessment of these chemicals would cost billions of euros and involve the use of millions of animals. REACH also aims to ensure that risks from substances of very high concern (SVHC) are properly controlled or that the substances are substituted. To match REACH requirements, fast and reliable methods with reproducible results are crucial, and regulatory bodies would be able to approve results. Property prediction and modeling will play an important role in this case [2]. Toxicology, the study of harmful interactions between chemicals and biological systems [3], uses more and more computer models. These models are based on already available data and help to reduce in vivo testing. Toxicity modeling and its data have many applications such as characterizing hazards, assessing environmental risks, and identifying potential lead components in drug discovery. A well-established method for toxicity modeling is quantitative structure–activity relationship (QSAR) or quantitative structure–property relationship (QSPR) [4,5]. On the basis of the available measured and calculated properties or activities and descriptors of compounds, predictive models for a certain property are built, which are then used to predict that","PeriodicalId":164785,"journal":{"name":"Grid Computing for Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129218911","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 : 2007-04-30DOI: 10.1002/9780470191637.CH8
Alexandru-Adrian Tantar, N. Melab, E. Talbi
{"title":"Molecular Docking Using Grid Computing","authors":"Alexandru-Adrian Tantar, N. Melab, E. Talbi","doi":"10.1002/9780470191637.CH8","DOIUrl":"https://doi.org/10.1002/9780470191637.CH8","url":null,"abstract":"","PeriodicalId":164785,"journal":{"name":"Grid Computing for Bioinformatics and Computational Biology","volume":"6 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113932386","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 : 1900-01-01DOI: 10.1002/9780470191637.CH10
Alfredo Tirado-Ramos, P. Sloot, M. Bubak
A huge gap exists between what we know is possible with today's machines and what we have so far been able to finish. —Donald Knuth 1.1 INTRODUCTION The challenges discovered when studying humans as complex systems, from a biomedical viewpoint (from cells to interacting individuals), cover the whole spectrum from genome to health and cross temporal and spatial scales [1]. This includes studying biomedical issues using multiscale and multiscience models and techniques all the way from genomics to the macroscopic medical scale. This is also aggravated by the continuous increase in the amount of digital data produced by modern high-throughput biomedical detection and analysis systems. As reported by Hey et al., it is expected that larger amounts of digital data will be generated by next generations of large scale, collaborative e-Science experiments [2]. New experiments in science and engineering will cover the whole spectrum, from the simulation of complete biological systems, to cutting-edge research in bioinformatics. At the macroscopic scale, for instance, there are research efforts in biomedical informatics that are gradually pushing the boundaries of the state of the art, moving from monolitic software architectures to building more generic components. Such efforts normally leverage object-oriented and distributed component architectures to encapsulate or wrap legacy data in order to improve application interoperability and scalability [3, 4]. This allows for enhanced data and process flow at the macroscopic level, where models such as DICOM provide support for data acces from work stations to archiving and communications systems and back to hospitals' information systems.
{"title":"Grid-Based Interactive Decision Support in Biomedicine","authors":"Alfredo Tirado-Ramos, P. Sloot, M. Bubak","doi":"10.1002/9780470191637.CH10","DOIUrl":"https://doi.org/10.1002/9780470191637.CH10","url":null,"abstract":"A huge gap exists between what we know is possible with today's machines and what we have so far been able to finish. —Donald Knuth 1.1 INTRODUCTION The challenges discovered when studying humans as complex systems, from a biomedical viewpoint (from cells to interacting individuals), cover the whole spectrum from genome to health and cross temporal and spatial scales [1]. This includes studying biomedical issues using multiscale and multiscience models and techniques all the way from genomics to the macroscopic medical scale. This is also aggravated by the continuous increase in the amount of digital data produced by modern high-throughput biomedical detection and analysis systems. As reported by Hey et al., it is expected that larger amounts of digital data will be generated by next generations of large scale, collaborative e-Science experiments [2]. New experiments in science and engineering will cover the whole spectrum, from the simulation of complete biological systems, to cutting-edge research in bioinformatics. At the macroscopic scale, for instance, there are research efforts in biomedical informatics that are gradually pushing the boundaries of the state of the art, moving from monolitic software architectures to building more generic components. Such efforts normally leverage object-oriented and distributed component architectures to encapsulate or wrap legacy data in order to improve application interoperability and scalability [3, 4]. This allows for enhanced data and process flow at the macroscopic level, where models such as DICOM provide support for data acces from work stations to archiving and communications systems and back to hospitals' information systems.","PeriodicalId":164785,"journal":{"name":"Grid Computing for Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358891","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：