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Relevant bibliographies by topics / Bioinformatics applications

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Academic literature on the topic 'Bioinformatics applications'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Bioinformatics applications"

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Nedjah, Nadia, and Luiza de Macedo Mourelle. "Hardware for bioinformatics applications." Integration 46, no. 3 (2013): 219. http://dx.doi.org/10.1016/j.vlsi.2013.03.003.

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Sung, Wing-Kin. "Bioinformatics Applications in Genomics." Computer 45, no. 6 (2012): 57–63. http://dx.doi.org/10.1109/mc.2012.151.

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Parekh, Bhagavati. "Bioinformatics Applications in life Sciences: Concepts and Stance." Paripex - Indian Journal Of Research 3, no. 3 (2012): 72–74. http://dx.doi.org/10.15373/22501991/mar2014/78.

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Baiskhiyar, Divya, and Ravi Kumar. "Quantum Computing-Applications in Bioinformatics." International Journal of Computer Applications 177, no. 12 (2019): 26–28. http://dx.doi.org/10.5120/ijca2019919527.

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Ekanayake, J., T. Gunarathne, and J. Qiu. "Cloud Technologies for Bioinformatics Applications." IEEE Transactions on Parallel and Distributed Systems 22, no. 6 (2011): 998–1011. http://dx.doi.org/10.1109/tpds.2010.178.

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Stromback, Lena, and Juliana Freire. "XML Management for Bioinformatics Applications." Computing in Science & Engineering 13, no. 5 (2011): 12–23. http://dx.doi.org/10.1109/mcse.2010.100.

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Brenner, Chad. "Applications of Bioinformatics in Cancer." Cancers 11, no. 11 (2019): 1630. http://dx.doi.org/10.3390/cancers11111630.

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Baker, P. G., C. A. Goble, S. Bechhofer, N. W. Paton, R. Stevens, and A. Brass. "An ontology for bioinformatics applications." Bioinformatics 15, no. 6 (1999): 510–20. http://dx.doi.org/10.1093/bioinformatics/15.6.510.

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UMAR, ASAD. "Applications of Bioinformatics in Cancer Detection: A Lexicon of Bioinformatics Terms." Annals of the New York Academy of Sciences 1020, no. 1 (2004): 263–76. http://dx.doi.org/10.1196/annals.1310.021.

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Knudsen, Thomas, and Amar Singh. "Comparative bioinformatics—Applications for developmental toxicology." Toxicology Letters 172 (October 2007): S14—S15. http://dx.doi.org/10.1016/j.toxlet.2007.05.059.

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Dissertations / Theses on the topic "Bioinformatics applications"

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Peeters, Justine Kate. "Microarray bioinformatics and applications in oncology." [S.l.] : Rotterdam : [The Author] ; Erasmus University [Host], 2008. http://hdl.handle.net/1765/12618.

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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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Andersson, Claes. "Fusing Domain Knowledge with Data : Applications in Bioinformatics." Doctoral thesis, Uppsala universitet, Centrum för bioinformatik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8477.

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Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.

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Eklund, Martin. "eScience Approaches to Model Selection and Assessment : Applications in Bioinformatics." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-109437.

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Lui, Thomas Wing Hong. "Integrated database mining with applications to bioinformatics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58354.pdf.

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Wu, Man-kit Edward, and 胡文傑. "Improved indexes for next generation bioinformatics applications." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224222.

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Liu, Chi-man, and 廖志敏. "Efficient solutions for bioinformatics applications using GPUs." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2015. http://hdl.handle.net/10722/211138.

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Over the past few years, DNA sequencing technology has been advancing at such a fast pace that computer hardware and software can hardly meet the ever-increasing demand for sequence analysis. A natural approach to boost analysis efficiency is parallelization, which divides the problem into smaller ones that are to be solved simultaneously on multiple execution units. Common architectures such as multi-core CPUs and clusters can increase the throughput to some extent, but the hardware setup and maintenance costs are prohibitive. Fortunately, the newly emerged general-purpose GPU programming paradigm gives us a low-cost alternative for parallelization. This thesis presents GPU-accelerated algorithms for several problems in bioinformatics, along with implementations to demonstrate their power in handling enormous totally different limitations and optimization techniques than the CPU. The first tool presented is SOAP3-dp, which is a DNA short-read aligner highly optimized for speed. Prior to SOAP3-DP, the fastest short-read aligner was its predecessor SOAP2, which was capable of aligning 1 million 100-bp reads in 5 minutes. SOAP3-dp beats this record by aligning the same volume in only 10 seconds. The key to unlocking this unprecedented speed is the revamped BWT engine underlying SOAP3-dp. All data structures and associated operations have been tailor made for the GPU to achieve optimized performance. Experiments show that SOAP3-dp not only excels in speed, but also outperforms other aligners in both alignment sensitivity and accuracy. The next tools are for constructing data structures, namely Burrows-Wheeler transform (BWT) and de Bruijn graphs (DBGs), to facilitate genome assembly of short reads, especially large metagenomics data. The BWT index for a set of short reads has recently found its use in string-graph assemblers [44], as it provides a succinct way of representing huge string graphs which would otherwise exceed the main memory limit. Constructing the BWT index for a million reads is by itself not an easy task, let alone optimize for the GPU. Another class of assemblers, the DBG-based assemblers, also faces the same problem. This thesis presents construction algorithms for both the BWT and DBGs in a succinct form. In our experiments, we constructed the succinct DBG for a metagenomics data set with over 200 gigabases in 3 hours, and the resulting DBG only consumed 31.2 GB of memory. We also constructed the BWT index for 10 million 100-bp reads in 40 minutes using 4 quad-core machines. Lastly, we introduce a SNP detection tool, iSNPcall, which detects SNPs from a set of reads. Given a set of user-supplied annotated SNPs, iSNPcall focuses only on alignments covering these SNPs, which greatly accelerates the detection of SNPs at the prescribed loci. The annotated SNPs also helps us distinguish sequencing errors from authentic SNPs alleles easily. This is in contrast to the traditional de novo method which aligns reads onto the reference genome and then filters inauthentic mismatches according to some probabilities. After comparing on several applications, iSNPcall was found to give a higher accuracy than the de novo method, especially for samples with low coverage.<br>published_or_final_version<br>Computer Science<br>Doctoral<br>Doctor of Philosophy

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Wu, Man-kit Edward. "Improved indexes for next generation bioinformatics applications." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43224222.

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Zhang, Yi. "NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/83.

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Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice junction classification, gene regulatory network reconstruction, and lesion detection in mammograms. A critical step in defining gene structures and mRNA transcript variants is to accurately identify splice junctions. In the first work, we built the first deep learning-based splice junction classifier, DeepSplice. It outperforms the state-of-the-art classification tools in terms of both classification accuracy and computational efficiency. To uncover transcription factors governing metabolic reprogramming in non-small-cell lung cancer patients, we developed TFmeta, a machine learning approach to reconstruct relationships between transcription factors and their target genes in the second work. Our approach achieves the best performance on benchmark data sets. In the third work, we designed deep learning-based architectures to perform lesion detection in both 2D and 3D whole mammogram images.

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Kumar, Deept. "Redescription Mining: Algorithms and Applications in Bioinformatics." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/27518.

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Scientific data mining purports to extract useful knowledge from massive datasets curated through computational science efforts, e.g., in bioinformatics, cosmology, geographic sciences, and computational chemistry. In the recent past, we have witnessed major transformations of these applied sciences into data-driven endeavors. In particular, scientists are now faced with an overload of vocabularies for describing domain entities. All of these vocabularies offer alternative and mostly complementary (sometimes, even contradictory) ways to organize information and each vocabulary provides a different perspective into the problem being studied. To further knowledge discovery, computational scientists need tools to help uniformly reason across vocabularies, integrate multiple forms of characterizing datasets, and situate knowledge gained from one study in terms of others. This dissertation defines a new pattern class called redescriptions that provides high level capabilities for reasoning across domain vocabularies. A redescription is a shift of vocabulary, or a different way of communicating the same information; redescription mining finds concerted sets of objects that can be defined in (at least) two ways using given descriptors. We present the CARTwheels algorithm for mining redescriptions by exploiting equivalences of partitions induced by distinct descriptor classes as well as applications of CARTwheels to several bioinformatics datasets. We then outline how we can build more complex data mining operations by cascading redescriptions to realize a story, leading to a new data mining capability called storytelling. Besides applications to characterizing gene sets, we showcase its uses in other datasets as well. Finally, we extend the core CARTwheels algorithm by introducing a theoretical framework, based on partitions, to systematically explore redescription space; generalizing from mining redescriptions (and stories) within a single domain to relating descriptors across different domains, to support complex relational data mining scenarios; and exploiting structure of the underlying descriptor space to yield more effective algorithms for specific classes of datasets.<br>Ph. D.

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Books on the topic "Bioinformatics applications"

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1978-, Stajich Jason Eric, Hansen David, and SpringerLink (Online service), eds. Bioinformatics: Tools and Applications. Springer-Verlag New York, 2009.

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Cai, Zhipeng, Oliver Eulenstein, Daniel Janies, and Daniel Schwartz, eds. Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38036-5.

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Măndoiu, Ion, Raj Sunderraman, and Alexander Zelikovsky, eds. Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79450-9.

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Cai, Zhipeng, Ion Mandoiu, Giri Narasimhan, Pavel Skums, and Xuan Guo, eds. Bioinformatics Research and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57821-3.

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Basu, Mitra, Yi Pan, and Jianxin Wang, eds. Bioinformatics Research and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08171-7.

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Măndoiu, Ion, and Alexander Zelikovsky, eds. Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72031-7.

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Borodovsky, Mark, Johann Peter Gogarten, Teresa M. Przytycka, and Sanguthevar Rajasekaran, eds. Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13078-6.

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Bourgeois, Anu, Pavel Skums, Xiang Wan, and Alex Zelikovsky, eds. Bioinformatics Research and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38782-6.

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Bleris, Leonidas, Ion Măndoiu, Russell Schwartz, and Jianxin Wang, eds. Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30191-9.

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Măndoiu, Ion, Giri Narasimhan, and Yanqing Zhang, eds. Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01551-9.

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Book chapters on the topic "Bioinformatics applications"

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Gupta, S. "Bioinformatics—Research Applications." In Bioinformatics: Applications in Life and Environmental Sciences. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-8880-3_12.

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Nguyen, Hung T., Vladik Kreinovich, Berlin Wu, and Gang Xiang. "Applications to Bioinformatics." In Computing Statistics under Interval and Fuzzy Uncertainty. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24905-1_32.

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Jabalia, Neetu. "Bioinformatics Resources." In Omics Approaches, Technologies And Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2925-8_7.

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Ramsden, Jeremy J. "Medical applications." In Bioinformatics: An Introduction. Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2950-9_16.

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Cannata, Nicola, Flavio Corradini, Emanuela Merelli, Francesca Piersigilli, and Leonardo Vito. "Towards Bioinformatics Resourceomes." In Biomedical Data and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02193-0_2.

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Wu, Duojiao, David E. Sanin, and Xiangdong Wang. "Clinical Applications and Systems Biomedicine." In Translational Bioinformatics. Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-017-7543-4_13.

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Halligan, Brian D. "Collaborative-Based Bioinformatics Applications." In Collaborative Computational Technologies for Biomedical Research. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118026038.ch23.

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Lourido, Lucia, Paula Diez, Noelia Dasilva, et al. "Protein Microarrays: Overview, Applications and Challenges." In Translational Bioinformatics. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9202-8_8.

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Savova, Guergana, John Pestian, Brian Connolly, Timothy Miller, Yizhao Ni, and Judith W. Dexheimer. "Natural Language Processing: Applications in Pediatric Research." In Translational Bioinformatics. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1104-7_12.

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Li, Hailong, Zhaowei Ren, Sheng Ren, Xinyu Guo, Xiaoting Zhu, and Long Jason Lu. "Network Analysis and Applications in Pediatric Research." In Translational Bioinformatics. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1104-7_13.

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Conference papers on the topic "Bioinformatics applications"

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"Applications II: Bioinformatics." In CLADE 2005. Proceedings Challenges of Large Applications in Distributed Environments, 2005. IEEE, 2005. http://dx.doi.org/10.1109/clade.2005.1520900.

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Li, Xubin, Wenrui Jiang, Yi Jiang, and Quan Zou. "Hadoop Applications in Bioinformatics." In 2012 7th Open Cirrus Summit (OCS). IEEE, 2012. http://dx.doi.org/10.1109/ocs.2012.40.

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Majhi, Vinayak, Sudip Paul, and Rachna Jain. "Bioinformatics for Healthcare Applications." In 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE, 2019. http://dx.doi.org/10.1109/aicai.2019.8701277.

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Navathe, Shamkant B. "Bioinformatics databases and applications." In the 43rd annual southeast regional conference. ACM Press, 2005. http://dx.doi.org/10.1145/1167350.1167352.

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Dollas, Apostolos. "Reconfigurable Architectures for Bioinformatics Applications." In 2010 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE, 2010. http://dx.doi.org/10.1109/isvlsi.2010.111.

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Qiu, Xiaohong, Jaliya Ekanayake, Scott Beason, et al. "Cloud technologies for bioinformatics applications." In the 2nd Workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1646468.1646474.

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Cannataro, Mario, Pietro H. Guzzi, Giuseppe Tradigo, and Pierangelo Veltri. "Scalable Biomedical and Bioinformatics Applications." In 3rd International ICST Conference on Scalable Information Systems. ICST, 2008. http://dx.doi.org/10.4108/icst.infoscale2008.3511.

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Giorgetti, Alejandro. "Structural bioinformatics: advances and applications." In FROM PHYSICS TO BIOLOGY: The Interface between Experiment and Computation - BIFI 2006 II International Congress. AIP, 2006. http://dx.doi.org/10.1063/1.2345623.

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Kumar, Vineet. "Cloud computing using bioinformatics MapReduce applications." In 2016 Symposium on Colossal Data Analysis and Networking (CDAN). IEEE, 2016. http://dx.doi.org/10.1109/cdan.2016.7570893.

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Lanc, Irena, Peter Bui, Douglas Thain, and Scott Emrich. "Adapting bioinformatics applications for heterogeneous systems." In the second international workshop. ACM Press, 2011. http://dx.doi.org/10.1145/1996023.1996025.

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Reports on the topic "Bioinformatics applications"

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Srivastava, Anuj. A Statistical Theory for Shape Analysis of Curves and Surfaces with Applications in Image Analysis, Biometrics, Bioinformatics and Medical Diagnostics. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532601.

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Anderson, J., and T. Critchlow. Design of Wrapper Integration Within the DataFoundry Bioinformatics Application. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/15002882.

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Zurawski, Jason, Jennifer Schopf, Hans Addleman, and Doug Southworth. Arcadia University Bioinformatics Application Deep Dive. Final report. KINBERCON 2019, Philadelphia, PA, April 3, 2019. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1542424.

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