- Email: [email protected]
Advanced computing solutions for health care and medicine
Journal of Computational Science 3 (2012) 250–253
Contents lists available at SciVerse ScienceDirect
Journal of Computational Science journal homepage: www.elsevier.com/locate/jocs
Editorial
Advanced computing solutions for health care and medicine a r t i c l e
i n f o
Keywords: Health informatics Bioinformatics Clinical decision support systems High performance simulation
a b s t r a c t This guest editorial introduces the special issue on “Advanced Computing Solutions for Health Care and Medicine”. The goal of this special issue was to collect high quality papers describing the application of computer science methods and techniques to main health care and clinical problems, resulting in high performance applications or prototypes for medical and clinical environments. The special issue touched different health informatics hot topics and is organized in four sections: (i) clinical decision support systems; (ii) biomedical imaging; (iii) high performance computing and biomedical simulations; (iv) bioinformatics data analysis. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Computer science and Health care are two large science areas that are sharing common interests and research topics. This is always more relevant since communication, algorithms and data management applications have been using for practical clinical applications and clinical process management. This is also demonstrated by the sharing of common interest topics and multidisciplinary scientific collaborations and projects. Recently, many interdisciplinary conferences and workshops have been organized where both computer scientists and physicians discussed about research problems and results of common interests. Among those, we recall the recent International Symposium on Computer-Based Medical Systems (CBMS) and its Special Track on Computational Proteomics and Genomics, and the International Conference on Computational Science (ICCS) and its Workshop on Biomedical and Bioinformatics Challenges to Computer Science [1–4]. These conferences resulted in an increasing interdisciplinary scientific production, as demonstrated by different journal special issues and books, such as [5–8]. Recently, also scientific communities such as the ones referencing to ACM and Euro-Par, are favouring communication among computer science, database, and parallel computing scientists with researchers in biology and clinical applications, as demonstrated by some emerging interdisciplinary conferences and journals, such as the ACM SIGHIT1 International Health Informatics Symposium, the ACM SIGHIT Special Interest Group Newsletter, and the Euro-Par Workshop on High Performance Bioinformatics and Biomedicine [9,10], to cite a few. Similarly, measurements, communication and electronic engineering communities are presenting their results to medical doctors and biologists communities for health care and life quality
1
http://www.sighit.org/
1877-7503/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jocs.2012.07.002
applications (see for instance the IEEE International Workshop series on Medical Measurement and Applications). There are research problems in computer science that are critical while addressing issues related to health care, and there are research topics in biomedicine and in health care that cannot be addressed without ad-hoc computer science solutions or applications. For instance, in the first direction (computer science supporting health care), high performance computing (HPC) and communications, (large) database management techniques, data quality and real time communications pose research problems that find application and validation in health care management systems where performance cannot accept any compromise. In other words, the health care domain can be considered as a realistic validation platform for many new applied computer science techniques. On the other hand, information systems and healthcare management systems are currently considered principal solutions for managing data and processes in health care management, both in clinical services and in financial optimization solutions (i.e., to maximize services while minimizing costs). Moreover, considering information and communication technology (ICT) solutions, significant improvements both in terms of efficiency and effectiveness of health care strategies, have been obtained by using new frontiers and applications of ICT solutions, such as broadband communication solutions and data streaming. Finally, high-performance and large-scale health care applications pose different challenges to computer science such as in distributed management of medical images, high-performance multi-scale simulation of organs or biological systems, data mining and warehousing of large health care data stores, just to name a few. The goal of this special issue is to collect high quality papers containing results in computer science applications for health care and medical environments. In particular, we selected papers describing solutions and software prototypes that have already demonstrated their usefulness in clinical houses or in biomedical laboratories, either in patient front solutions (such as surgery room, advanced
Editorial / Journal of Computational Science 3 (2012) 250–253
electronic patient records, medical decision support systems), or in back office solutions (such as in biological laboratory, off line clinical data analysis, health care administration). We distributed the call for paper through main computer science, biomedical and clinical communities, by following mailing lists, newsgroup and by involving the organizers of the most important interdisciplinary computer science symposiums and workshops. We received 38 submissions, all of good quality. Each manuscript was reviewed by at least 3 international reviewers. Finally, 10 out of 38 papers have been accepted with an acceptance rate of 26.31%. The accepted papers have been revised two or three times before final acceptance, to improve quality. The accepted papers can be mostly considered as proposals and results of computer scientists that have been working in cooperation with physicians or biologists. In the rest of this Guest Editorial, we summarize the accepted contributions that have been grouped according to the following health informatics hot topics: (i) clinical decision support systems; (ii) biomedical imaging; (iii) high performance computing and biomedical simulations; (iv) bioinformatics data analysis. 2. Clinical decision support systems The area of clinical decision support systems (CDSS) includes methodologies and computer-based systems for supporting medical decision. A broad classification includes computer-aided diagnosis (CAD) and computer-aided surgery (CAS) systems. CDSSs are mainly based on decisional trees, where tree nodes contain information about a described environment. Each node contains variables and, depending on the current instances values, by visiting the tree from root to leaves, it is possible to map possible cases. Thus, mapping clinical and biological conditions into a tree based database allows to guide ad support analysis and diagnostic processes. The open problems are still so many in this area, mostly due to reliability of knowledge base. Nevertheless the contributions in medical areas reported in this special issue are very interesting for decision and for practical purposes. Still many efforts should be done to create general purpose CDSSs. In this special issue we reported two CDSS proposals. In the first one, titled “Clinical Decision Support System for Dental Treatment”, Vijay Kumar Mago et al. present a decision making system based on fuzzy inference mechanism. An expert system has been designed to accept vague values of dental signs and symptoms associated with the broken tooth. A knowledge base and an inference algorithm to decide the possible treatment(s) are presented. Simulations are performed and results are compared with the dentists’ suggestions with good results. The interest is on the possibility of using fuzzy-based mechanisms as particularly efficient systems. In the second paper, titled “A Two-phase Decision Support Framework for the Automatic Screening of Digital Fundus Images”, Bálint Antal et al. present an enhanced detection procedure for the screening of diabetic retinopathy: input digital fundus images2 are first classified and then regions of interest with possible lesions are identified on the images. The proposed procedure can increase the computational performance of a screening system. 3. Biomedical imaging The analysis and management of biomedical images for supporting clinical diagnosis and surgical room operation is another
2 The fundus of the eye is the interior surface of the eye and can be examined by ophthalmoscopy or fundus photography.
251
challenging hot topic of biomedical research. Thanks to highthroughput image acquisition devices, the storage of clinical images is increasing rapidly. Thus automatic systems are required for performing data management and analysis, e.g. by automatically supporting clinical diagnosis by classifying and proposing the (part of) images containing relevant information. We received several contributions on such a topic, nevertheless, we selected the most interesting one. The work titled “An Efficient Computational Framework for the Analysis of Whole Slide Images: Application to Follicular Lymphoma Immunohistochemistry”, authored by Siddharth Samsi et al., presents a new computer-aided image analysis tool that can improve the accuracy of diagnosis and grading of Follicular Lymphoma (FL), which is one of the most common non-Hodgkin Lymphoma in the United States. The paper discusses the challenges involved in the processing of high-definition images acquired from histopathological tissue sections, in particular, those associated to the identification of follicles in whole slide immunohistochemical images that are relatively large (on the order of 100 K × 100 K pixels). To face these challenges, Samsi et al. developed an efficient parallel implementation of the automated algorithm for detecting follicles in whole slide images. The parallel implementation was able to overcome the memory challenges, i.e. to process near 40GB of data, and to reduce the total time to process one image, from near half an hour to around 3 min, when using a cluster of 12 processors.
4. High performance computing and biomedical simulations High performance computing (HPC) can help scientists to improve execution time and to face huge volumes of data available in clinical settings. We gathered mature contributions on the application of HPC solutions for solving high demanding problems in clinical areas. In the paper titled “Solutions for Biomedical Grid Computing – Case Studies from the D-Grid Project Services@MediGRID”, Frank Dickmann et al. give an overview of the results of the Services@MediGRID project. The Services@MediGRID consortium established a tool set of grid-based biomedical services since 2008. The system-biological complexity of genomes and genomic data obviously remains a huge challenge for research, diagnostics and treatment. To face this challenge, Dickmann et al. describe different services that support: (a) haplotype estimation; (b) diagnostic image analysis of molecular markers in the DNA; (c) visualization, annotation and management of genome data; d) phenotype – transcriptome analysis; and (e) pharmacokinetic tools. After an outline of technical and organizational elements, each service is presented as a case study. Since the grid services of Services@MediGRID were developed under the premise to be applied for commercial application, a business concept for each service is also described. Therefore, the authors claim that the MediGRID infrastructure has further evolved in order to properly handle both academical and commercial application scenarios. In the paper titled “Effect of Expiratory Flow Rate on the Acoustic Characteristics of Sibilant /s/”, Kazunori Nozaki et al. defined a model and a simulation tool to analyze the connection between oral cavity, expiratory flow and sibilant /s/. This paper is an example of how computational models may be useful for simulating and studying clinical problems (in this case identification of oral cavity characteristics as well as expiratory flow turbulences). The study can be considered as a contribution of the mathematical modeling and simulation approaches to clinical problems. In the paper “Accelerating the Simulation of Brain Tumor Proliferation with Many-core GPUs”, Konstantinos I. Karantasis et al. address the important issue of reducing costs and computation
252
Editorial / Journal of Computational Science 3 (2012) 250–253
time of clinically relevant simulations. Such reductions are essential for transforming biomedical simulations from a research tool to being widely applied in clinical work. The specific application addressed by the authors is the modelling of brain tumour growth, in which the main computational load stems from solving a non-homogeneous, time-dependent diffusion equation with a source term. The authors show how the discontinuous Galerkin (DG) method can be applied to this problem, and discusses some of the advantages of the DG method compared with similar schemes such as the continuous Galerkin finite element method and the finite volume method. The DG scheme is combined with an explicit Runge–Kutta time integrator, resulting in a numerical method that is well suited for implementation on GPUs. The algorithm is tested on a multicore Nvidia Tesla GPU, and yielded a maximum speed-up of around 60 compared with a serial implementation. In the paper titled “IMENSE: An e-Infrastructure Environment for Patient Specific Multiscale Data Integration, Modelling and Clinical Treatment”, Stefan J. Zasadaa et al. present the open source software called Individualized MEdiciNe Simulation Environment (IMENSE), which provides a simulation tool for patient information treatment. IMENSE can be considered as an efficient clinical decision support system mainly oriented to securely manage clinical data with a special focus on secure access to patient data. The applicability to a wide range scenario of cooperation among clinical structures is another interesting aspect of the system. Finally, in the paper titled “Combinatorial Selection of Short Triplex Forming Oligonucleotides for Fluorescence in situ Hybridisation COMBO-FISH”, Eberhard Schmitt et al. present the implementation of algorithms to design oligonucleotide sets. The algorithms can be trivially parallelised and run on clusters, grids, and clouds. The system is proposed for the selection of short triplex forming oligonucleotides for COMBO-FISH hybridisations and for 3Dstructure investigations of the nucleus.
5. Bioinformatics data analysis A further interesting topic that has been captured by this special issue regards tools for clinical and biological data analysis. This area can be often considered as the pre-processing phase for clinical data analysis. Bioinformatics and data analysis tools are mainly used in biological laboratories connected to clinical operation units producing data from hosted patients. In the paper titled “The Voronoi Diagram for Graphs and Its Application in the Sickle Cell Disease Research”, Marko Zivanic et al. apply a generalization of the classical Voronoi diagram, named the Voronoi diagram for graphs (VDG), to the analysis of protein–protein interaction (PPI) networks. This novel methodology is applicable both to weighted and un-weighted PPI networks, and is particularly well suited to analyze protein clusters that are centered around a well-defined subset of input nodes. Proteins that interact with these nodes are clustered on the basis of their relative distance in the PPI network, which is computed efficiently using the VDG. Compared with commonly applied methods for PPI clustering analysis, the proposed VDG-based method offers benefits both in terms of computational efficiency and offered information details. The VDG analysis is applied to the study of Sickle cell disease (SCD), which is a genetic disorder affecting the human red blood cell. Although the genetic mutation responsible for SCD is well known, there are many unresolved questions related to variations in the severity of the disorder. VDG analysis is successfully applied to shed light on the modified protein expression in the SCD affected cells. In paper titled “A Time Series Approach for Clustering Mass Spectrometry Data”, Andrea Tagarelli et al. propose a time series approach for biological data clustering. They use a data mining approach for the analysis of mass spectrometry data, where spectra
are modelled as time series. Time series representation allows to reduce dimensionality while preserving the relevant features and alleviates the critical task of preprocessing the raw spectra in the whole process of MS data analysis. The use of mass spectrometry to support clinical data analysis is now mature, thus that improving and simplifying the clinical information extraction from spectra data is highly interesting. The proposed approach can be used to aid clinicians in studying and formulating diagnosis of different pathologies. 6. Conclusions The experience of serving as Guest Editors for this special issue on “Advanced Computing Solutions for Health Care and Medicine”, helped us in enriching our knowledge on health informatics and on requirements of clinicians on using and being supported by computer based-solutions. We claim that still many efforts should be done both in designing new architectural solutions for ICT and HPC applied to medicine, and for new algorithms allowing to support physicians in diagnosis and interventional phases. Among those, the integration of collaborative tools resulting in the so called “collaboratories” [11] and the use of ontologies to model application domains [12], are two key issues for the effective application of HPC to medicine and biology. We really enjoyed in preparing such issue, and we hope readers will find interesting papers and useful topics for their research. Acknowledgements This special issue originates as a follow-up of the last editions of the workshop on Biomedical and Bioinformatics Challenges to Computer Science, held in conjunction with the ICCS conference. We wish to thank the organizers of ICCS and especially Peter Sloot and Dick van Albada for helping us in making this special issue. Finally, we wish to thank all the authors who submitted their manuscripts to this special issue and especially the Reviewers that helped us in selecting high quality manuscripts. References [1] M. Cannataro, M. Romberg, J. Sundnes, R.W. dos Santos, Bioinformatics’ challenges to computer science, in: M. Bubak, G.D. van Albada, J. Dongarra, P.M.A. Sloot (Eds.), ICCS (3), Vol. 5103 of Lecture Notes in Computer Science, Springer, 2008, pp. 67–69. [2] M. Cannataro, R.W. dos Santos, J. Sundnes, Bioinformatics’ challenges to computer science: Bioinformatics tools and biomedical modeling, in: G. Allen, J. Nabrzyski, E. Seidel, G.D. van Albada, J. Dongarra, P.M.A. Sloot (Eds.), ICCS (1) Vol. 5544 of Lecture Notes in Computer Science, Springer, 2009, pp. 807–809. [3] M. Cannataro, R.W. dos Santos, J. Sundnes, Biomedical and bioinformatics challenges to computer science, Procedia CS 1 (1) (2010) 931–933. [4] M. Cannataro, R.W. dos Santos, J. Sundnes, Biomedical and bioinformatics challenges to computer science: bioinformatics, modeling of biomedical systems and clinical applications, Procedia CS 4 (2011) 1058–1061. [5] G. Aloisio, V. Breton, M. Mirto, A. Murli, T. Solomonides, Special section: life science grids for biomedicine and bioinformatics, Future Generation Computer Systems 23 (2007) 367–370. [6] M. Cannataro, M. Romberg, J. Sundnes, R.W. dos Santos, Special section: biomedical and bioinformatics challenges to computer science, Future Generation Computer System 26 (3) (2010) 421–423. [7] E.-G. Talbi, A.Y. Zomaya (Eds.), Grid Computing for Bioinformatics and Computational Biology, Wiley, 2008. [8] M. Cannataro (Ed.), Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, Medical Information Science Reference, IGI Global, Hershey, 2009. [9] M. Cannataro, Hibb 2010: Workshop on high performance bioinformatics and biomedicine, in: M.R. Guarracino, F. Vivien, J.L. Träff, M. Cannataro, M. Danelutto, A. Hast, F. Perla, A. Knüpfer, B.D. Martino, M. Alexander (Eds.), Euro-Par Workshops, Vol. 6586 of Lecture Notes in Computer Science, Springer, 2010, pp. 165–166. [10] M. Cannataro, Hibb 2011: 2nd workshop on high performance bioinformatics and biomedicine, in: M. Alexander, P. D’Ambra, A. Belloum, G. Bosilca, M. Cannataro, M. Danelutto, B.D. Martino, M. Gerndt, E. Jeannot, R. Namyst, J. Roman, S.L. Scott, J.L. Träff, G. Vallée, J. Weidendorfer (Eds.), Euro-Par Workshops (2) Vol. 7156 of Lecture Notes in Computer Science, Springer, 2011, pp. 1–2.
Editorial / Journal of Computational Science 3 (2012) 250–253 [11] M. Cannataro, D. Talia, G. Tradigo, P. Trunfio, P. Veltri, SIGMCC: A system for sharing meta patient records in a Peer-to-Peer environment, Future Generation Computer Systems 24 (3) (2008) 222–234. [12] M. Cannataro, P.H. Guzzi, T. Mazza, G. Tradigo, P. Veltri, Using ontologies for preprocessing and mining spectra data on the Grid, Future Generation Computer Systems 23 (1) (2007) 55–60.
Mario Cannataro ∗ Department of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, Italy, and ICAR-CNR, Rende, Italy Rodrigo Weber dos Santos Department of Computer Science, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
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Joakim Sundnes Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway Pierangelo Veltri Department of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, Italy ∗ Corresponding author. E-mail address: [email protected] (M. Cannataro)
Available online 27 July 2012
Contents lists available at SciVerse ScienceDirect
Journal of Computational Science journal homepage: www.elsevier.com/locate/jocs
Editorial
Advanced computing solutions for health care and medicine a r t i c l e
i n f o
Keywords: Health informatics Bioinformatics Clinical decision support systems High performance simulation
a b s t r a c t This guest editorial introduces the special issue on “Advanced Computing Solutions for Health Care and Medicine”. The goal of this special issue was to collect high quality papers describing the application of computer science methods and techniques to main health care and clinical problems, resulting in high performance applications or prototypes for medical and clinical environments. The special issue touched different health informatics hot topics and is organized in four sections: (i) clinical decision support systems; (ii) biomedical imaging; (iii) high performance computing and biomedical simulations; (iv) bioinformatics data analysis. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Computer science and Health care are two large science areas that are sharing common interests and research topics. This is always more relevant since communication, algorithms and data management applications have been using for practical clinical applications and clinical process management. This is also demonstrated by the sharing of common interest topics and multidisciplinary scientific collaborations and projects. Recently, many interdisciplinary conferences and workshops have been organized where both computer scientists and physicians discussed about research problems and results of common interests. Among those, we recall the recent International Symposium on Computer-Based Medical Systems (CBMS) and its Special Track on Computational Proteomics and Genomics, and the International Conference on Computational Science (ICCS) and its Workshop on Biomedical and Bioinformatics Challenges to Computer Science [1–4]. These conferences resulted in an increasing interdisciplinary scientific production, as demonstrated by different journal special issues and books, such as [5–8]. Recently, also scientific communities such as the ones referencing to ACM and Euro-Par, are favouring communication among computer science, database, and parallel computing scientists with researchers in biology and clinical applications, as demonstrated by some emerging interdisciplinary conferences and journals, such as the ACM SIGHIT1 International Health Informatics Symposium, the ACM SIGHIT Special Interest Group Newsletter, and the Euro-Par Workshop on High Performance Bioinformatics and Biomedicine [9,10], to cite a few. Similarly, measurements, communication and electronic engineering communities are presenting their results to medical doctors and biologists communities for health care and life quality
1
http://www.sighit.org/
1877-7503/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jocs.2012.07.002
applications (see for instance the IEEE International Workshop series on Medical Measurement and Applications). There are research problems in computer science that are critical while addressing issues related to health care, and there are research topics in biomedicine and in health care that cannot be addressed without ad-hoc computer science solutions or applications. For instance, in the first direction (computer science supporting health care), high performance computing (HPC) and communications, (large) database management techniques, data quality and real time communications pose research problems that find application and validation in health care management systems where performance cannot accept any compromise. In other words, the health care domain can be considered as a realistic validation platform for many new applied computer science techniques. On the other hand, information systems and healthcare management systems are currently considered principal solutions for managing data and processes in health care management, both in clinical services and in financial optimization solutions (i.e., to maximize services while minimizing costs). Moreover, considering information and communication technology (ICT) solutions, significant improvements both in terms of efficiency and effectiveness of health care strategies, have been obtained by using new frontiers and applications of ICT solutions, such as broadband communication solutions and data streaming. Finally, high-performance and large-scale health care applications pose different challenges to computer science such as in distributed management of medical images, high-performance multi-scale simulation of organs or biological systems, data mining and warehousing of large health care data stores, just to name a few. The goal of this special issue is to collect high quality papers containing results in computer science applications for health care and medical environments. In particular, we selected papers describing solutions and software prototypes that have already demonstrated their usefulness in clinical houses or in biomedical laboratories, either in patient front solutions (such as surgery room, advanced
Editorial / Journal of Computational Science 3 (2012) 250–253
electronic patient records, medical decision support systems), or in back office solutions (such as in biological laboratory, off line clinical data analysis, health care administration). We distributed the call for paper through main computer science, biomedical and clinical communities, by following mailing lists, newsgroup and by involving the organizers of the most important interdisciplinary computer science symposiums and workshops. We received 38 submissions, all of good quality. Each manuscript was reviewed by at least 3 international reviewers. Finally, 10 out of 38 papers have been accepted with an acceptance rate of 26.31%. The accepted papers have been revised two or three times before final acceptance, to improve quality. The accepted papers can be mostly considered as proposals and results of computer scientists that have been working in cooperation with physicians or biologists. In the rest of this Guest Editorial, we summarize the accepted contributions that have been grouped according to the following health informatics hot topics: (i) clinical decision support systems; (ii) biomedical imaging; (iii) high performance computing and biomedical simulations; (iv) bioinformatics data analysis. 2. Clinical decision support systems The area of clinical decision support systems (CDSS) includes methodologies and computer-based systems for supporting medical decision. A broad classification includes computer-aided diagnosis (CAD) and computer-aided surgery (CAS) systems. CDSSs are mainly based on decisional trees, where tree nodes contain information about a described environment. Each node contains variables and, depending on the current instances values, by visiting the tree from root to leaves, it is possible to map possible cases. Thus, mapping clinical and biological conditions into a tree based database allows to guide ad support analysis and diagnostic processes. The open problems are still so many in this area, mostly due to reliability of knowledge base. Nevertheless the contributions in medical areas reported in this special issue are very interesting for decision and for practical purposes. Still many efforts should be done to create general purpose CDSSs. In this special issue we reported two CDSS proposals. In the first one, titled “Clinical Decision Support System for Dental Treatment”, Vijay Kumar Mago et al. present a decision making system based on fuzzy inference mechanism. An expert system has been designed to accept vague values of dental signs and symptoms associated with the broken tooth. A knowledge base and an inference algorithm to decide the possible treatment(s) are presented. Simulations are performed and results are compared with the dentists’ suggestions with good results. The interest is on the possibility of using fuzzy-based mechanisms as particularly efficient systems. In the second paper, titled “A Two-phase Decision Support Framework for the Automatic Screening of Digital Fundus Images”, Bálint Antal et al. present an enhanced detection procedure for the screening of diabetic retinopathy: input digital fundus images2 are first classified and then regions of interest with possible lesions are identified on the images. The proposed procedure can increase the computational performance of a screening system. 3. Biomedical imaging The analysis and management of biomedical images for supporting clinical diagnosis and surgical room operation is another
2 The fundus of the eye is the interior surface of the eye and can be examined by ophthalmoscopy or fundus photography.
251
challenging hot topic of biomedical research. Thanks to highthroughput image acquisition devices, the storage of clinical images is increasing rapidly. Thus automatic systems are required for performing data management and analysis, e.g. by automatically supporting clinical diagnosis by classifying and proposing the (part of) images containing relevant information. We received several contributions on such a topic, nevertheless, we selected the most interesting one. The work titled “An Efficient Computational Framework for the Analysis of Whole Slide Images: Application to Follicular Lymphoma Immunohistochemistry”, authored by Siddharth Samsi et al., presents a new computer-aided image analysis tool that can improve the accuracy of diagnosis and grading of Follicular Lymphoma (FL), which is one of the most common non-Hodgkin Lymphoma in the United States. The paper discusses the challenges involved in the processing of high-definition images acquired from histopathological tissue sections, in particular, those associated to the identification of follicles in whole slide immunohistochemical images that are relatively large (on the order of 100 K × 100 K pixels). To face these challenges, Samsi et al. developed an efficient parallel implementation of the automated algorithm for detecting follicles in whole slide images. The parallel implementation was able to overcome the memory challenges, i.e. to process near 40GB of data, and to reduce the total time to process one image, from near half an hour to around 3 min, when using a cluster of 12 processors.
4. High performance computing and biomedical simulations High performance computing (HPC) can help scientists to improve execution time and to face huge volumes of data available in clinical settings. We gathered mature contributions on the application of HPC solutions for solving high demanding problems in clinical areas. In the paper titled “Solutions for Biomedical Grid Computing – Case Studies from the D-Grid Project Services@MediGRID”, Frank Dickmann et al. give an overview of the results of the Services@MediGRID project. The Services@MediGRID consortium established a tool set of grid-based biomedical services since 2008. The system-biological complexity of genomes and genomic data obviously remains a huge challenge for research, diagnostics and treatment. To face this challenge, Dickmann et al. describe different services that support: (a) haplotype estimation; (b) diagnostic image analysis of molecular markers in the DNA; (c) visualization, annotation and management of genome data; d) phenotype – transcriptome analysis; and (e) pharmacokinetic tools. After an outline of technical and organizational elements, each service is presented as a case study. Since the grid services of Services@MediGRID were developed under the premise to be applied for commercial application, a business concept for each service is also described. Therefore, the authors claim that the MediGRID infrastructure has further evolved in order to properly handle both academical and commercial application scenarios. In the paper titled “Effect of Expiratory Flow Rate on the Acoustic Characteristics of Sibilant /s/”, Kazunori Nozaki et al. defined a model and a simulation tool to analyze the connection between oral cavity, expiratory flow and sibilant /s/. This paper is an example of how computational models may be useful for simulating and studying clinical problems (in this case identification of oral cavity characteristics as well as expiratory flow turbulences). The study can be considered as a contribution of the mathematical modeling and simulation approaches to clinical problems. In the paper “Accelerating the Simulation of Brain Tumor Proliferation with Many-core GPUs”, Konstantinos I. Karantasis et al. address the important issue of reducing costs and computation
252
Editorial / Journal of Computational Science 3 (2012) 250–253
time of clinically relevant simulations. Such reductions are essential for transforming biomedical simulations from a research tool to being widely applied in clinical work. The specific application addressed by the authors is the modelling of brain tumour growth, in which the main computational load stems from solving a non-homogeneous, time-dependent diffusion equation with a source term. The authors show how the discontinuous Galerkin (DG) method can be applied to this problem, and discusses some of the advantages of the DG method compared with similar schemes such as the continuous Galerkin finite element method and the finite volume method. The DG scheme is combined with an explicit Runge–Kutta time integrator, resulting in a numerical method that is well suited for implementation on GPUs. The algorithm is tested on a multicore Nvidia Tesla GPU, and yielded a maximum speed-up of around 60 compared with a serial implementation. In the paper titled “IMENSE: An e-Infrastructure Environment for Patient Specific Multiscale Data Integration, Modelling and Clinical Treatment”, Stefan J. Zasadaa et al. present the open source software called Individualized MEdiciNe Simulation Environment (IMENSE), which provides a simulation tool for patient information treatment. IMENSE can be considered as an efficient clinical decision support system mainly oriented to securely manage clinical data with a special focus on secure access to patient data. The applicability to a wide range scenario of cooperation among clinical structures is another interesting aspect of the system. Finally, in the paper titled “Combinatorial Selection of Short Triplex Forming Oligonucleotides for Fluorescence in situ Hybridisation COMBO-FISH”, Eberhard Schmitt et al. present the implementation of algorithms to design oligonucleotide sets. The algorithms can be trivially parallelised and run on clusters, grids, and clouds. The system is proposed for the selection of short triplex forming oligonucleotides for COMBO-FISH hybridisations and for 3Dstructure investigations of the nucleus.
5. Bioinformatics data analysis A further interesting topic that has been captured by this special issue regards tools for clinical and biological data analysis. This area can be often considered as the pre-processing phase for clinical data analysis. Bioinformatics and data analysis tools are mainly used in biological laboratories connected to clinical operation units producing data from hosted patients. In the paper titled “The Voronoi Diagram for Graphs and Its Application in the Sickle Cell Disease Research”, Marko Zivanic et al. apply a generalization of the classical Voronoi diagram, named the Voronoi diagram for graphs (VDG), to the analysis of protein–protein interaction (PPI) networks. This novel methodology is applicable both to weighted and un-weighted PPI networks, and is particularly well suited to analyze protein clusters that are centered around a well-defined subset of input nodes. Proteins that interact with these nodes are clustered on the basis of their relative distance in the PPI network, which is computed efficiently using the VDG. Compared with commonly applied methods for PPI clustering analysis, the proposed VDG-based method offers benefits both in terms of computational efficiency and offered information details. The VDG analysis is applied to the study of Sickle cell disease (SCD), which is a genetic disorder affecting the human red blood cell. Although the genetic mutation responsible for SCD is well known, there are many unresolved questions related to variations in the severity of the disorder. VDG analysis is successfully applied to shed light on the modified protein expression in the SCD affected cells. In paper titled “A Time Series Approach for Clustering Mass Spectrometry Data”, Andrea Tagarelli et al. propose a time series approach for biological data clustering. They use a data mining approach for the analysis of mass spectrometry data, where spectra
are modelled as time series. Time series representation allows to reduce dimensionality while preserving the relevant features and alleviates the critical task of preprocessing the raw spectra in the whole process of MS data analysis. The use of mass spectrometry to support clinical data analysis is now mature, thus that improving and simplifying the clinical information extraction from spectra data is highly interesting. The proposed approach can be used to aid clinicians in studying and formulating diagnosis of different pathologies. 6. Conclusions The experience of serving as Guest Editors for this special issue on “Advanced Computing Solutions for Health Care and Medicine”, helped us in enriching our knowledge on health informatics and on requirements of clinicians on using and being supported by computer based-solutions. We claim that still many efforts should be done both in designing new architectural solutions for ICT and HPC applied to medicine, and for new algorithms allowing to support physicians in diagnosis and interventional phases. Among those, the integration of collaborative tools resulting in the so called “collaboratories” [11] and the use of ontologies to model application domains [12], are two key issues for the effective application of HPC to medicine and biology. We really enjoyed in preparing such issue, and we hope readers will find interesting papers and useful topics for their research. Acknowledgements This special issue originates as a follow-up of the last editions of the workshop on Biomedical and Bioinformatics Challenges to Computer Science, held in conjunction with the ICCS conference. We wish to thank the organizers of ICCS and especially Peter Sloot and Dick van Albada for helping us in making this special issue. Finally, we wish to thank all the authors who submitted their manuscripts to this special issue and especially the Reviewers that helped us in selecting high quality manuscripts. References [1] M. Cannataro, M. Romberg, J. Sundnes, R.W. dos Santos, Bioinformatics’ challenges to computer science, in: M. Bubak, G.D. van Albada, J. Dongarra, P.M.A. Sloot (Eds.), ICCS (3), Vol. 5103 of Lecture Notes in Computer Science, Springer, 2008, pp. 67–69. [2] M. Cannataro, R.W. dos Santos, J. Sundnes, Bioinformatics’ challenges to computer science: Bioinformatics tools and biomedical modeling, in: G. Allen, J. Nabrzyski, E. Seidel, G.D. van Albada, J. Dongarra, P.M.A. Sloot (Eds.), ICCS (1) Vol. 5544 of Lecture Notes in Computer Science, Springer, 2009, pp. 807–809. [3] M. Cannataro, R.W. dos Santos, J. Sundnes, Biomedical and bioinformatics challenges to computer science, Procedia CS 1 (1) (2010) 931–933. [4] M. Cannataro, R.W. dos Santos, J. Sundnes, Biomedical and bioinformatics challenges to computer science: bioinformatics, modeling of biomedical systems and clinical applications, Procedia CS 4 (2011) 1058–1061. [5] G. Aloisio, V. Breton, M. Mirto, A. Murli, T. Solomonides, Special section: life science grids for biomedicine and bioinformatics, Future Generation Computer Systems 23 (2007) 367–370. [6] M. Cannataro, M. Romberg, J. Sundnes, R.W. dos Santos, Special section: biomedical and bioinformatics challenges to computer science, Future Generation Computer System 26 (3) (2010) 421–423. [7] E.-G. Talbi, A.Y. Zomaya (Eds.), Grid Computing for Bioinformatics and Computational Biology, Wiley, 2008. [8] M. Cannataro (Ed.), Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, Medical Information Science Reference, IGI Global, Hershey, 2009. [9] M. Cannataro, Hibb 2010: Workshop on high performance bioinformatics and biomedicine, in: M.R. Guarracino, F. Vivien, J.L. Träff, M. Cannataro, M. Danelutto, A. Hast, F. Perla, A. Knüpfer, B.D. Martino, M. Alexander (Eds.), Euro-Par Workshops, Vol. 6586 of Lecture Notes in Computer Science, Springer, 2010, pp. 165–166. [10] M. Cannataro, Hibb 2011: 2nd workshop on high performance bioinformatics and biomedicine, in: M. Alexander, P. D’Ambra, A. Belloum, G. Bosilca, M. Cannataro, M. Danelutto, B.D. Martino, M. Gerndt, E. Jeannot, R. Namyst, J. Roman, S.L. Scott, J.L. Träff, G. Vallée, J. Weidendorfer (Eds.), Euro-Par Workshops (2) Vol. 7156 of Lecture Notes in Computer Science, Springer, 2011, pp. 1–2.
Editorial / Journal of Computational Science 3 (2012) 250–253 [11] M. Cannataro, D. Talia, G. Tradigo, P. Trunfio, P. Veltri, SIGMCC: A system for sharing meta patient records in a Peer-to-Peer environment, Future Generation Computer Systems 24 (3) (2008) 222–234. [12] M. Cannataro, P.H. Guzzi, T. Mazza, G. Tradigo, P. Veltri, Using ontologies for preprocessing and mining spectra data on the Grid, Future Generation Computer Systems 23 (1) (2007) 55–60.
Mario Cannataro ∗ Department of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, Italy, and ICAR-CNR, Rende, Italy Rodrigo Weber dos Santos Department of Computer Science, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
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Joakim Sundnes Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway Pierangelo Veltri Department of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, Italy ∗ Corresponding author. E-mail address: [email protected] (M. Cannataro)
Available online 27 July 2012