Relevant bibliographies by topics / COMPUTERS / Bioinformatics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'COMPUTERS / Bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2024

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Journal articles on the topic "COMPUTERS / Bioinformatics"

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Liang, Xin, Wen Zhu, Zhibin Lv, and Quan Zou. "Molecular Computing and Bioinformatics." Molecules 24, no. 13 (2019): 2358. http://dx.doi.org/10.3390/molecules24132358.

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Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.
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Gauthier, Jeff, Antony T. Vincent, Steve J. Charette, and Nicolas Derome. "A brief history of bioinformatics." Briefings in Bioinformatics 20, no. 6 (2018): 1981–96. http://dx.doi.org/10.1093/bib/bby063.

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AbstractIt is easy for today’s students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of ‘Big Data’ has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.
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Pant, Dhruv Chander, and O. P. Gupta. "Performance Analysis of Parallelized Bioinformatics Applications." Asian Journal of Computer Science and Technology 7, no. 2 (2018): 70–74. http://dx.doi.org/10.51983/ajcst-2018.7.2.1881.

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The main challenges bioinformatics applications facing today are to manage, analyze and process a huge volume of genome data. This type of analysis and processing is very difficult using general purpose computer systems. So the need of distributed computing, cloud computing and high performance computing in bioinformatics applications arises. Now distributed computers, cloud computers and multi-core processors are available at very low cost to deal with bulk amount of genome data. Along with these technological developments in distributed computing, many efforts are being done by the scientists and bioinformaticians to parallelize and implement the algorithms to take the maximum advantage of the additional computational power. In this paper a few bioinformatics algorithms have been discussed. The parallelized implementations of these algorithms have been explained. The performance of these parallelized algorithms has been also analyzed. It has been also observed that in parallel implementations of the various bioinformatics algorithms, impact of communication subsystems with respect to the job sizes should also be analyzed.
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Zhou, Ckeng, and Song-Nian Yu. "Using cluster computers in bioinformatics research." Journal of Shanghai University (English Edition) 7, no. 4 (2003): 370–74. http://dx.doi.org/10.1007/s11741-003-0012-0.

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Chicco, Davide, Umberto Ferraro Petrillo, and Giuseppe Cattaneo. "Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment." PLOS Computational Biology 19, no. 7 (2023): e1011272. http://dx.doi.org/10.1371/journal.pcbi.1011272.

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Some scientific studies involve huge amounts of bioinformatics data that cannot be analyzed on personal computers usually employed by researchers for day-to-day activities but rather necessitate effective computational infrastructures that can work in a distributed way. For this purpose, distributed computing systems have become useful tools to analyze large amounts of bioinformatics data and to generate relevant results on virtual environments, where software can be executed for hours or even days without affecting the personal computer or laptop of a researcher. Even if distributed computing resources have become pivotal in multiple bioinformatics laboratories, often researchers and students use them in the wrong ways, making mistakes that can cause the distributed computers to underperform or that can even generate wrong outcomes. In this context, we present here ten quick tips for the usage of Apache Spark distributed computing systems for bioinformatics analyses: ten simple guidelines that, if taken into account, can help users avoid common mistakes and can help them run their bioinformatics analyses smoothly. Even if we designed our recommendations for beginners and students, they should be followed by experts too. We think our quick tips can help anyone make use of Apache Spark distributed computing systems more efficiently and ultimately help generate better, more reliable scientific results.
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Minton, Allen P., and Arun K. Attri. "Lap computers in the laboratory." Bioinformatics 2, no. 3 (1986): 167–71. http://dx.doi.org/10.1093/bioinformatics/2.3.167.

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Agulleiro, J. I., and J. J. Fernandez. "Fast tomographic reconstruction on multicore computers." Bioinformatics 27, no. 4 (2010): 582–83. http://dx.doi.org/10.1093/bioinformatics/btq692.

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Conrad, Randall. "MacroMolecules, Genes and Computers: Chapter Three." Bioinformatics 9, no. 6 (1993): 767. http://dx.doi.org/10.1093/bioinformatics/9.6.767.

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Hoppe, Carolyn. "Bioinformatics: Computers or clinicians for complex disease risk assessment?" European Journal of Human Genetics 13, no. 8 (2005): 893–94. http://dx.doi.org/10.1038/sj.ejhg.5201441.

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Gilbert, D. G. "Dot plot sequence comparisons on Macintosh computers." Bioinformatics 6, no. 2 (1990): 117. http://dx.doi.org/10.1093/bioinformatics/6.2.117.

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Dissertations / Theses on the topic "COMPUTERS / Bioinformatics"

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Akhurst, Timothy John. "The role of parallel computing in bioinformatics." Thesis, Rhodes University, 2005. http://eprints.ru.ac.za/162/.

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Niu, Yanwei. "Parallelization and performance optimization of bioinformatics and biomedical applications targeted to advanced computer architectures." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 1.05 Mb., 143 p, 2005. http://wwwlib.umi.com/dissertations/fullcit/3181852.

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Pollak, Stephen P. "Computers, telecommunications, and the microbiologist : the online hunt for microbes." Thesis, University of Sussex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259956.

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This study concerns the relationship between social and technological change. It asks whether the introduction of a new technology, computer mediated communication, enables scientists to engage in an altogether new and potentially more effective research activity, the online hunt for microbes. It shows that such technology, in the form of the Microbial Strain Data Network (MSDN), boosts significantly the overall speed, flexibility and extent of possible communication between microbe hunters and microbe suppliers. As a result, the global hunt for microbes in existing research and service oriented culture collections can transcend historiC geographic and institutional barriers that diminish its timeliness and comprehensiveness and, as a result, its potential utility. Therefore, the study hypothesises, microbe hunters will use extensively the MSDN's electronic mail and, especially, its online microbial strain databases. However, contrary to expectations, the mere availability of the MSDN is evidently insufficient to assure its widespread use. The MSDN was, in fact, little used during an extensive six month evaluation period in 1990191. Moreover, despite lowering its prices and increasing its strain database offerings, as well as improving its ease of use, the MSDN remains liltle used today. The study concludes that the MSDN's non-use reflects its general incompatibility with the context in which it was applied. The prevailing sociotechnological structure of microbiology diminishes significantly the accessibility, comparability, and reliability of shared strain data. In doing so, it reduces the potential benefits of CMC technology generally, and the MSDN in particular, in facilitating the online hunt for microbes. Success in the online hunt for microbes therefore requires changing the socio-technological context in which the hunt occurs. The discussion recommends possible changes to the socio-technological structure of microbiology to improve the online hunt's viability. It also points to the need for further research about the viability of the online hunt for microbes, as well as about the effective application of computer mediated communication technology to science generally.
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Al, Assaad Sevin 1984. "Biochemical system simulation on a heterogeneous multicore processor." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115988.

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Biological system simulation is an increasingly popular field of study that provides biologists with the tools necessary to simulate biochemical systems in order to obtain quantitative models. The purpose of this thesis is to describe an accelerated version of GridCell, a stochastic biological system simulator. GridCell tracks each individual particle's location in the system, as well as the time evolution of the concentration of each species involved. It simulates molecular diffusion via Brownian movements, and particle interactions are dependent on their locations. We present here a parallel adaptation of the algorithm, implemented on a heterogeneous multicore processor, i.e. IBM Cell Broadband Engine (CBE). We introduce the CBE architecture and outline its advantages, as well as describe the original algorithm. Subsequently, we detail the parallel implementation and the algorithm modifications. Finally, we perform timing analysis to show that the parallel version provides improved performance over the original serial version.
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Åkerborg, Örjan. "Taking advantage of phylogenetic trees in comparative genomics." Doctoral thesis, KTH, Beräkningsbiologi, CB, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4757.

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Phylogenomics can be regarded as evolution and genomics in co-operation. Various kinds of evolutionary studies, gene family analysis among them, demand access to genome-scale datasets. But it is also clear that many genomics studies, such as assignment of gene function, are much improved by evolutionary analysis. The work leading to this thesis is a contribution to the phylogenomics field. We have used phylogenetic relationships between species in genome-scale searches for two intriguing genomic features, namely and A-to-I RNA editing. In the first case we used pairwise species comparisons, specifically human-mouse and human-chimpanzee, to infer existence of functional mammalian pseudogenes. In the second case we profited upon later years' rapid growth of the number of sequenced genomes, and used 17-species multiple sequence alignments. In both these studies we have used non-genomic data, gene expression data and synteny relations among these, to verify predictions. In the A-to-I editing project we used 454 sequencing for experimental verification. We have further contributed a maximum a posteriori (MAP) method for fast and accurate dating analysis of speciations and other evolutionary events. This work follows recent years' trend of leaving the strict molecular clock when performing phylogenetic inference. We discretised the time interval from the leaves to the root in the tree, and used a dynamic programming (DP) algorithm to optimally factorise branch lengths into substitution rates and divergence times. We analysed two biological datasets and compared our results with recent MCMC-based methodologies. The dating point estimates that our method delivers were found to be of high quality while the gain in speed was dramatic. Finally we applied the DP strategy in a new setting. This time we used a grid laid out on a species tree instead of on an interval. The discretisation gives together with speciation times a common timeframe for a gene tree and the corresponding species tree. This is the key to integration of the sequence evolution process and the gene evolution process. Out of several potential application areas we chose gene tree reconstruction. We performed genome-wide analysis of yeast gene families and found that our methodology performs very well.<br>QC 20100923
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Peterson, Rickard. "Enkel navigering i webbdatabaser inom bioinformatik: En implementation av moduler för ett urval av databaser." Thesis, Linköping University, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-58928.

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<p>Detta examensarbete är framtaget för att utforma moduler till programmet BioSpider som utvecklats vid ADIT avdelningen vid IDA institutionen Linköping Universitet med syfte att förenkla för biologer när de söker information om andra forskares resultat.</p><p>Det finns ett stort antal databaser som innehåller forskningsdata kring proteiner, reaktioner, signalvägar etc. Exempel på databaser är UniProt, Reactome, IntAct, BioModels och KEGG. Det uppstår problem med att dessa databaser är uppbyggda på olika sätt och ej kan användas på ett universellt sätt, utan kräver individuellt utformning och anpassning för att kunna användas tillsammans med andra databaser.  Det är där BioSpider kommer in, BioSpider är ett program som försöker bygga upp ett träd utifrån olika databaser. Varje databas hanteras individuellt av BioSpider men presenteras på ett universellt sätt i form av ett träd för användaren. Examensarbetets del i BioSpider är att utforma moduler som behandlar ytterligare databaser utöver BioModels som redan stöds i BioSpider.</p><p>Behovet av att tillgängliggöra stöd för fler databaser var nödvändigt för att kunna visa upp en användbar version av BioSpider med mer än en databas. Detta för att kunna visa att metoden fungerar i praktiken.</p><p>Examensarbetet har utförts genom att en förstudie av ett antal databaser har gjorts och inom dessa valt ut relevant information som sedan implementerats i BioSpider med olika moduler för de olika databaserna.</p><p>Det som fanns tillgängligt vid examensarbetets start var programmet BioSpider och implementation för en databas (BioModels). Nu stöds BioSpider av ytterligare fyra stycken databaser som är UniProt, Reactome, KEGG och IntAct som bygger ut trädet. BioSpider stöds även utav DIP, Ensembl, EMBL, FlyBase, GO, InterPro, OMIM, PDB, PIR, PROSIT och RefSeq för vidare länkning till webbsida där ytterligare information kan återfås.</p><br><p>The aim of this thesis was to develop modules for the system BioSpider that are developed by ADIT division at IDA institute at Linköping University. The objective is to simplify for biologist when they seek for information about research findings.</p><p>There is a large number of databases that contains research results about proteins, reactions, pathways etc. Examples of these databases are UniProt, Reactome, IntAct, BioModels and KEGG. Problems emerges since the databases are constructed in different ways and cannot be used in a universal way, they must be individually tailored and adjusted to be compatible with other databases. This is where BioSpider comes in, BioSpider is a program that is supposed to build up a tree of the different databases. Each database is managed individually by BioSpider and is presented to the user in a universal way in the form of a tree. This thesis extends the BioSpider system so that more databases are supported than just the database BioModels.</p><p>The need to support more databases was necessary to be able to produce a usable version of BioSpider with more than one database. This is important to show that the method works in practice.</p><p>The work has been performed by a pilot study of a number of databases. Within these we selected appropriate information that was implemented in BioSpider with different modules for different databases.</p><p>At start of this thesis one database was supported by BioSpider, this database is BioModels. Now BioSpider supports by additional four databases UniProt, Reactome, KEGG and IntAct. BioSpider also supports linking to websites where information can be retrieved, the supported databases are DIP, Ensembl, EMBL, FlyBase, GO, InterPro, OMIM, PDB, PIR, PROSIT and RefSeq.</p>
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Zaslavskiy, Mikhail. "Graph matching and its application in computer vision and bioinformatics." Paris, ENMP, 2010. http://www.theses.fr/2010ENMP1659.

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Le problème d'alignement de graphes, qui joue un rôle central dans différents domaines de la reconnaissance de formes, est l'un des plus grands défis dans le traitement de graphes. Nous proposons une méthode approximative pour l'alignement de graphes étiquetés et pondérés, basée sur la programmation convexe concave. Une application importante du problème d'alignement de graphes est l'alignement de réseaux d'interactions de protéines, qui joue un rôle central pour la recherche de voies de signalisation conservées dans l'évolution, de complexes protéiques conservés entre les espèces, et pour l'identification d'orthologues fonctionnels. Nous reformulons le problème d'alignement de réseaux d'interactions comme un problème d'alignement de graphes, et étudions comment les algorithmes existants d'alignement de graphes peuvent être utilisés pour le résoudre. Dans la formulation classique de problème d'alignement de graphes, seules les correspondances bijectives entre les noeuds de deux graphes sont considérées. Dans beaucoup d'applications, cependant, il est plus intéressant de considérer les correspondances entre des ensembles de noeuds. Nous proposons une nouvelle formulation de ce problème comme un problème d'optimisation discret, ainsi qu'un algorithme approximatif basé sur une relaxation continue. Nous présentons également deux résultats indépendents dans les domaines de la traduction automatique statistique et de la bio-informatique. Nous montrons d'une part comment le problème de la traduction statistique basé sur les phrases peut être reformulé comme un problème du voyageur de commerce. Nous proposons d'autre part une nouvelle mesure de similarité entre les sites de fixation de protéines, basée sur la comparaison 3D de nuages atomiques<br>The graph matching problem is among the most important challenges of graph processing, and plays a central role in various fields of pattern recognition. We propose an approximate method for labeled weighted graph matching, based on a convex-concave programming approach which can be applied to the matching of large sized graphs. This method allows to easily integrate information on graph label similarities into the optimization problem, and therefore to perform labeled weighted graph matching. One of the interesting applications of the graph matching problem is the alignment of protein-protein interaction networks. This problem is important when investigating evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. We reformulate PPI alignment as a graph matching problem, and study how state-of-the-art graph matching algorithms can be used for this purpose. In the classical formulation of graph matching, only one-to-one correspondences are considered, which is not always appropriate. In many applications, it is more interesting to consider many-to-many correspondences between graph vertices. We propose a reformulation of the many-to-many graph matching problem as a discrete optimization problem and we propose an approximate algorithm based on a continuous relaxation. In this thesis, we also present two interesting results in statistical machine translation and bioinformatics. We show how the phrase-based statistical machine translation decoding problem can be reformulated as a Traveling Salesman Problem. We also propose a new protein binding pocket similarity measure based on a comparison of 3D atom clouds
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Yuan, Yingqin Kam Moshe Dr. "Decision fusion in distributed detection and bioinformatics /." Philadelphia, Pa. : Drexel University, 2004. http://dspace.library.drexel.edu/handle/1860/336.

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Berry, Eric Zachary 1980. "Bioinformatics and database tools for glycans." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/27085.

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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 75-76).<br>Recent advances in biology have afforded scientists with the knowledge that polysaccharides play an active role in modulating cellular activities. Glycosaminoglycans (GAGs) are one such family of polysaccharides that play a very important role in regulating the functions of numerous important signaling molecules and enzymes in the cell. Developing bioinformatics tools has been integral to advancing genomics and proteomics. While these tools have been well-developed to store and process sequence and structure information for proteins and DNA, they are very poorly developed for polysaccharides. Glycan structures pose special problems because of their tremendous information density per fundamental unit, their often-branched structures, and the complicated nature of their building blocks. The GlycoBank, an online database of known GAG structures and functions, has been developed to overcome many of these difficulties by developing a common notation for researchers to describe GAG sequences, a common repository to view known structure-function relationships, and the complex tools and searches needed to facilitate their work. This thesis focuses on the development of GlycoBank. In addition, a large, NIGMS-funded consortium, the Consortium for Functional Glycomics, is a larger database that also aims to store polysaccharide structure-function information of a broader collection of polysaccharides. The ideas and concepts implemented in developing GlycoBank were instrumental in developing databases and bioinformatics tools for the Consortium for Functional Glycomics.<br>by Eric Zachary Berry.<br>M.Eng.and S.B.
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Alvarez, Vega Marco. "Graph Kernels and Applications in Bioinformatics." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/1185.

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In recent years, machine learning has emerged as an important discipline. However, despite the popularity of machine learning techniques, data in the form of discrete structures are not fully exploited. For example, when data appear as graphs, the common choice is the transformation of such structures into feature vectors. This procedure, though convenient, does not always effectively capture topological relationships inherent to the data; therefore, the power of the learning process may be insufficient. In this context, the use of kernel functions for graphs arises as an attractive way to deal with such structured objects. On the other hand, several entities in computational biology applications, such as gene products or proteins, may be naturally represented by graphs. Hence, the demanding need for algorithms that can deal with structured data poses the question of whether the use of kernels for graphs can outperform existing methods to solve specific computational biology problems. In this dissertation, we address the challenges involved in solving two specific problems in computational biology, in which the data are represented by graphs. First, we propose a novel approach for protein function prediction by modeling proteins as graphs. For each of the vertices in a protein graph, we propose the calculation of evolutionary profiles, which are derived from multiple sequence alignments from the amino acid residues within each vertex. We then use a shortest path graph kernel in conjunction with a support vector machine to predict protein function. We evaluate our approach under two instances of protein function prediction, namely, the discrimination of proteins as enzymes, and the recognition of DNA binding proteins. In both cases, our proposed approach achieves better prediction performance than existing methods. Second, we propose two novel semantic similarity measures for proteins based on the gene ontology. The first measure directly works on the gene ontology by combining the pairwise semantic similarity scores between sets of annotating terms for a pair of input proteins. The second measure estimates protein semantic similarity using a shortest path graph kernel to take advantage of the rich semantic knowledge contained within ontologies. Our comparison with other methods shows that our proposed semantic similarity measures are highly competitive and the latter one outperforms state-of-the-art methods. Furthermore, our two methods are intrinsic to the gene ontology, in the sense that they do not rely on external sources to calculate similarities.
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Books on the topic "COMPUTERS / Bioinformatics"

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Christine, Orengo, Jones David, and Thornton Janet M, eds. Bioinformatics: Genes, proteins, and computers. BIOS Scientific, 2003.

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Bertil, Schmidt, ed. Bioinformatics: High performance parallel computer architectures. CRC Press, 2010.

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Zomaya, Albert Y. Parallel Computing for Bioinformatics and Computational Biology. John Wiley & Sons, Ltd., 2006.

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1966-, Barry Paul, ed. Bioinformatics, biocomputing and Perl: An introduction to bioinformatics computing skills and practice. Wiley, 2004.

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Petukhov, S. V. (Sergeĭ Valentinovich), ed. Mathematics of bioinformatics: Theory, practice, and applications. Wiley, 2011.

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Søren, Brunak, ed. Bioinformatics: The machine learning approach. 2nd ed. MIT Press, 2001.

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Shen, Shiyi. Theory and mathematical methods in bioinformatics. Springer, 2008.

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Zomaya, Albert Y. Parallel computing for bioinformatics and computational biology: Models, enabling technologies, and case studies. John Wiley, 2005.

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library, Wiley online, ed. Knowledge based bioinformatics: From analysis to interpretation. John Wiley & Sons, 2010.

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Martyn, Amos, ed. Cellular computing. Oxford University Press, 2004.

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Book chapters on the topic "COMPUTERS / Bioinformatics"

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Bansod, Tularam M. "Role of Computers in Bioinformatics." In Bioinformatics: Applications in Life and Environmental Sciences. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-8880-3_2.

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Slenter, Denise N., Martina Kutmon, and Egon L. Willighagen. "WikiPathways: Integrating Pathway Knowledge with Clinical Data." In Physician's Guide to the Diagnosis, Treatment, and Follow-Up of Inherited Metabolic Diseases. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67727-5_73.

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SummaryThroughout the chapters in this book, pathways are used to visualize how genetically inheritable metabolic disorders are related. These pathways provide common conceptual models which explain groups of chemical reactions within their biological context. Visual representations of the reactions in biological pathway diagrams provide intuitive ways to study the complex metabolic processes. In order to link (clinical) data to these pathways, they have to be understood by computers. Understanding how to move from a regular pathway drawing to its machine-readable counterpart is pertinent for creating proper models. This chapter outlines the various aspects of the digital counterparts of the pathway diagrams in this book, connecting them to databases and using them in data integration and analysis. This is followed by three examples of bioinformatics applications including a pathway enrichment analysis, a biological network extension, and a final example that integrates pathways with clinical biomarker data.
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Dandekar, Thomas, and Meik Kunz. "When Does the Computer Stop Calculating?" In Bioinformatics. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-65036-3_8.

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Dandekar, Thomas, and Meik Kunz. "Understand Evolution Better Applying the Computer." In Bioinformatics. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-65036-3_10.

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Cohen, William W., and Charles K. Cohen. "Bioinformatics." In A Computer Scientist's Guide to Cell Biology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55907-5_8.

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Dandekar, Thomas, and Meik Kunz. "We Can Think About Ourselves – The Computer Cannot." In Bioinformatics. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-65036-3_14.

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Apweiler, Rolf, Vivien Junker, Alain Gateau, Claire O'Donovan, Fiona Lang, and Amos Bairoch. "New developments in linking of biological databases and computer-generation of annotation: SWISS-PROT and its computer-annotated supplement TREMBL." In Bioinformatics. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033202.

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Cannataro, Mario, Mathilde Romberg, Joakim Sundnes, and Rodrigo Weber dos Santos. "Bioinformatics’ Challenges to Computer Science." In Computational Science – ICCS 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69389-5_9.

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Rajkishan, Thakor, Ailani Rachana, Surani Shruti, Patel Bhumi, and Dhaval Patel. "Computer-Aided Drug Designing." In Advances in Bioinformatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6191-1_9.

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Stamatakos, Georgios S., and Nikolaos Uzunoglu. "Computer Simulation of Tumour Response to Therapy." In Cancer Bioinformatics. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.ch6.

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Conference papers on the topic "COMPUTERS / Bioinformatics"

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"Session MP8a2: Bioinformatics and medical imaging." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094473.

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"Session TP2a: Bioinformatics and DNA computing." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094677.

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"Session MA3b Bioinformatics / Genomic Signal Processing." In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. IEEE, 2004. http://dx.doi.org/10.1109/acssc.2004.1399082.

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Stratton, Jack, Michael Albert, Quentin Jensen, Max Ismailov, Filip Jagodzinski, and Tanzima Islam. "Towards Aggregation Based I/O Optimization for Scaling Bioinformatics Applications." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020. http://dx.doi.org/10.1109/compsac48688.2020.00-85.

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Boanos, Almog, Anitha Sri Mothukuri, Kaitlin A. Goettsch, and Dhundy K. Bastola. "Investigation and utilization of personal food computers for research in drug development and biomedicine." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8218006.

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Gomes, Kaio Pablo, and Simone Matos. "Contributions of Bioinformatics for Computing Education in the Detection of Programming Assignment Plagiarism." In XXX Simpósio Brasileiro de Informática na Educação (Brazilian Symposium on Computers in Education). Brazilian Computer Society (Sociedade Brasileira de Computação - SBC), 2019. http://dx.doi.org/10.5753/cbie.sbie.2019.1351.

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Bile Hassan, Ismail, and Jigang Liu. "A Comparative Study of the Academic Programs between Informatics/BioInformatics and Data Science in the U.S." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020. http://dx.doi.org/10.1109/compsac48688.2020.00030.

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Mallick, Arup, Atanu Kumar Das, and Nabin Ghoshal. "Network Based Analysis for Hiv-1 Significant Protein Through Drug-Target Interaction: Bioinformatics Practices." In 2023 IEEE Fifth International Conference on Advances in Electronics, Computers and Communications (ICAECC). IEEE, 2023. http://dx.doi.org/10.1109/icaecc59324.2023.10560315.

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Kolchanov, N. A. "Systems Computer Biology and Bioinformatics." In IX Congress of society physiologists of plants of Russia "Plant physiology is the basis for creating plants of the future". Kazan University Press, 2019. http://dx.doi.org/10.26907/978-5-00130-204-9-2019-17.

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Ackovska, N., and A. Madevska-Bogdanova. "Teaching Bioinformatics to Computer Science Students." In EUROCON 2005 - The International Conference on "Computer as a Tool". IEEE, 2005. http://dx.doi.org/10.1109/eurcon.2005.1630056.

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Reports on the topic "COMPUTERS / Bioinformatics"

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.
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Chakraborty, Srijani. Promises and Challenges of Systems Biology. Nature Library, 2020. http://dx.doi.org/10.47496/nl.blog.09.

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Modern systems biology is essentially interdisciplinary, tying molecular biology, the omics, bioinformatics and non-biological disciplines like computer science, engineering, physics, and mathematics together.
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