Journal articles: 'Coding region'

1

Lee, Eun Kyung, and Myriam Gorospe. "Coding region." RNA Biology 8, no. 1 (January 2011): 44–48. http://dx.doi.org/10.4161/rna.8.1.13863.

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Yu, Ren Kai, Jun Xuan Wang, You Ming Sun, and Yang Liu. "The Performance Analysis of Two Conventional Linear Pre-Coding Schemes in Massive MIMO System with Imperfect CSIT." Applied Mechanics and Materials 668-669 (October 2014): 1386–90. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1386.

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For this paper, we analyze the achievable sum rate of zero-forcing (ZF) pre-coding and Maximum Ratio Transmission (MRT) pre-coding with Matrix Normalization in massive MIMO system with Imperfect CSIT. We compare the performances of these two pre-codings and find that ZF pre-coding outperforming MRT pre-coding in the high SNR region while MRT pre-coding outperforming ZF pre-coding in the low SNR region. Then we derive the threshold of the pre-coding selection and provide the procedure of pre-coding schemes selection.

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Xinwei Li, and Kang Sun. "Region-Relationship Based Fingerprint Coding." International Journal of Digital Content Technology and its Applications 7, no. 6 (March 31, 2013): 1251–58. http://dx.doi.org/10.4156/jdcta.vol7.issue6.143.

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Nister, David, and Charilaos Christopoulos. "Lossless region of interest coding." Signal Processing 78, no. 1 (October 1999): 1–17. http://dx.doi.org/10.1016/s0165-1684(99)00044-4.

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Oh-Jin Kwon and R. Chellappa. "Region adaptive subband image coding." IEEE Transactions on Image Processing 7, no. 5 (May 1998): 632–48. http://dx.doi.org/10.1109/83.668022.

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Guo, Tao, and Raymond W. Yeung. "The Explicit Coding Rate Region of Symmetric Multilevel Diversity Coding." IEEE Transactions on Information Theory 66, no. 2 (February 2020): 1053–77. http://dx.doi.org/10.1109/tit.2019.2947493.

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Casas, Josep R., and Luis Torres. "A region-based subband coding scheme." Signal Processing: Image Communication 10, no. 1-3 (July 1997): 173–200. http://dx.doi.org/10.1016/s0923-5965(97)00024-6.

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Kim, Wook-Joong, Jong-Won Yi, and Seong-Dae Kim. "Quality scalable coding of selected region:." Signal Processing: Image Communication 15, no. 3 (November 1999): 181–88. http://dx.doi.org/10.1016/s0923-5965(98)00053-8.

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Bing Xiong, Xiaojiu Fan, Ce Zhu, Xuan Jing, and Qiang Peng. "Face Region Based Conversational Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 7 (July 2011): 917–31. http://dx.doi.org/10.1109/tcsvt.2011.2133530.

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Zingaretti, P., M. Gasparroni, and L. Vecci. "Fast chain coding of region boundaries." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 4 (April 1998): 407–15. http://dx.doi.org/10.1109/34.677272.

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King, Barry P., Tayyaba I. Khan, Guruprasad P. Aithal, Farhad Kamali, and Ann K. Daly. "Upstream and coding region CYP2C9 polymorphisms." Pharmacogenetics 14, no. 12 (December 2004): 813–22. http://dx.doi.org/10.1097/00008571-200412000-00004.

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Joshi, C. P., J. Weng, and H. T. Nguyen. "Wheat ubiquitin gene exhibits a conserved protein coding region and a diverged 3? non-coding region." Plant Molecular Biology 16, no. 5 (May 1991): 907–8. http://dx.doi.org/10.1007/bf00015082.

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Fujii, Ken, Yutaka Fujii, Takeshi Noda, Yukiko Muramoto, Tokiko Watanabe, Ayato Takada, Hideo Goto, Taisuke Horimoto, and Yoshihiro Kawaoka. "Importance of both the Coding and the Segment-Specific Noncoding Regions of the Influenza A Virus NS Segment for Its Efficient Incorporation into Virions." Journal of Virology 79, no. 6 (March 15, 2005): 3766–74. http://dx.doi.org/10.1128/jvi.79.6.3766-3774.2005.

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ABSTRACT The genome of influenza A virus consists of eight single-strand negative-sense RNA segments, each comprised of a coding region and a noncoding region. The noncoding region of the NS segment is thought to provide the signal for packaging; however, we recently showed that the coding regions located at both ends of the hemagglutinin and neuraminidase segments were important for their incorporation into virions. In an effort to improve our understanding of the mechanism of influenza virus genome packaging, we sought to identify the regions of NS viral RNA (vRNA) that are required for its efficient incorporation into virions. Deletion analysis showed that the first 30 nucleotides of the 3′ coding region are critical for efficient NS vRNA incorporation and that deletion of the 3′ segment-specific noncoding region drastically reduces NS vRNA incorporation into virions. Furthermore, silent mutations in the first 30 nucleotides of the 3′ NS coding region reduced the incorporation efficiency of the NS segment and affected virus replication. These results suggested that segment-specific noncoding regions together with adjacent coding regions (especially at the 3′ end) form a structure that is required for efficient influenza A virus vRNA packaging.

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Rambaruth, R., W. Christmas, and J. Kittler. "Interframe texture coding of uncovered regions for a region-based scheme." Electronics Letters 34, no. 17 (1998): 1655. http://dx.doi.org/10.1049/el:19981115.

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Pokkuluri, Kiran Sree, Ramesh Babu Inampudi, and S. S. S. N. Usha Devi Nedunuri. "IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction." Advances in Bioinformatics 2014 (July 15, 2014): 1–7. http://dx.doi.org/10.1155/2014/261362.

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Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000). The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata) and MCC (modified clonal classifier) to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992) datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006) dataset and nonpromoters from EID (Saxonov et al., 2000) and UTRdb (Pesole et al., 2002) datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.

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Askelöf, Joel, Mathias Larsson Carlander, and Charilaos Christopoulos. "Region of interest coding in JPEG 2000." Signal Processing: Image Communication 17, no. 1 (January 2002): 105–11. http://dx.doi.org/10.1016/s0923-5965(01)00026-1.

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Salembier, P., L. Torres, F. Meyer, and Chuang Gu. "Region-based video coding using mathematical morphology." Proceedings of the IEEE 83, no. 6 (June 1995): 843–57. http://dx.doi.org/10.1109/5.387088.

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Marin, R. M., M. Sulc, and J. Vanicek. "Searching the coding region for microRNA targets." RNA 19, no. 4 (February 12, 2013): 467–74. http://dx.doi.org/10.1261/rna.035634.112.

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Sanchez, V., A. Basu, and M. K. Mandal. "Prioritized Region of Interest Coding in JPEG2000." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 9 (September 2004): 1149–55. http://dx.doi.org/10.1109/tcsvt.2004.833168.

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Jerbi, A., Jian Wang, and S. Shirani. "Error-resilient region-of-interest video coding." IEEE Transactions on Circuits and Systems for Video Technology 15, no. 9 (September 2005): 1175–81. http://dx.doi.org/10.1109/tcsvt.2005.852619.

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Syed, Y. F., and K. R. Rao. "Low resolution region discriminator for wavelet coding." Electronics Letters 37, no. 12 (2001): 748. http://dx.doi.org/10.1049/el:20010518.

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Cagnazzo, M., G. Poggi, and L. Verdoliva. "Region-Based Transform Coding of Multispectral Images." IEEE Transactions on Image Processing 16, no. 12 (December 2007): 2916–26. http://dx.doi.org/10.1109/tip.2007.909315.

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Petrou, M., Peixin Hou, Sei-Ichiro Kamata, and C. I. Underwood. "Region-based image coding with multiple algorithms." IEEE Transactions on Geoscience and Remote Sensing 39, no. 3 (March 2001): 562–70. http://dx.doi.org/10.1109/36.911114.

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Francois, E., J. F. Vial, and B. Chupeau. "Coding algorithm with region-based motion compensation." IEEE Transactions on Circuits and Systems for Video Technology 7, no. 1 (1997): 97–108. http://dx.doi.org/10.1109/76.554421.

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Gilge, M. "Visual communication by region-oriented transform coding." Signal Processing 21, no. 1 (September 1990): 95. http://dx.doi.org/10.1016/0165-1684(90)90030-3.

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Gilge, Michael. "Visual communication by region-oriented transform coding." Signal Processing 20, no. 2 (June 1990): 185–86. http://dx.doi.org/10.1016/0165-1684(90)90129-m.

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Oh, Chang Seok, Soong Deok Lee, Yi-Suk Kim, and Dong Hoon Shin. "The Use and Effectiveness of Triple Multiplex System for Coding Region Single Nucleotide Polymorphism in Mitochondrial DNA Typing of Archaeologically Obtained Human Skeletons from Premodern Joseon Tombs of Korea." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/850648.

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Previous study showed that East Asian mtDNA haplogroups, especially those of Koreans, could be successfully assigned by the coupled use of analyses on coding region SNP markers and control region mutation motifs. In this study, we tried to see if the same triple multiplex analysis for coding regions SNPs could be also applicable to ancient samples from East Asia as the complementation for sequence analysis of mtDNA control region. By the study on Joseon skeleton samples, we know that mtDNA haplogroup determined by coding region SNP markers successfully falls within the same haplogroup that sequence analysis on control region can assign. Considering that ancient samples in previous studies make no small number of errors in control region mtDNA sequencing, coding region SNP analysis can be used as good complimentary to the conventional haplogroup determination, especially of archaeological human bone samples buried underground over long periods.

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Nair B J, Bipin, and Rahul Reghunath. "Coding and functional defect region prediction of placental protein in an embryo cell of first trimester using ANN approach." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 167. http://dx.doi.org/10.14419/ijet.v7i1.9.9756.

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The protein coding and functional regions in DNA sequences has become an exciting task in bioinformatics. In particular, the coding region has a 3-base periodicity, which helps for exon identification. Many signal processing tools and techniques have been successfully applied to identify tasks, but still need to be improved in this direction. In our work, we employ ANN classifier to predict coding and functional region of proteinin human embryo cell protein in first trimester, and evaluate their performances according to the comparison energy levels of coding region. The obtained from the threshold energy level, results show that in a box plot finally predict the mutation.

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Perić, Zoran H., Lazar Z–. Velimirović, and Milan R. Dinčić. "Improved Linearization of the Optimal Compression Function for Laplacian Source." Journal of Electrical Engineering 65, no. 3 (May 1, 2014): 179–83. http://dx.doi.org/10.2478/jee-2014-0028.

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Abstract In this paper, linearization of the optimal compression function is done and hierarchical coding (by coding the regions firstly and then the cells inside the region) is applied, achieving simple and fast process of coding and decoding. The signal at the entrance of the scalar quantizer is modeled by Laplacian probability density function. It is shown that the linearization of inner regions very little influences distortion and therefore only the last region should be optimized. Two methods of optimization of the last region are proposed, that improve performances of the scalar quantizer, and obtained SQNR (signal-to-quantization noise ratio) is close to that of the nonlinear optimal compression function.

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Kline, Margaret C., Peter M. Vallone, Janette W. Redman, David L. Duewer, Cassandra D. Calloway, and John M. Butler. "Mitochondrial DNA Typing Screens with Control Region and Coding Region SNPs." Journal of Forensic Sciences 50, no. 2 (2005): 1–9. http://dx.doi.org/10.1520/jfs2004293.

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Kokare, Parmeshwar, and Dr MasoodhuBanu. N.M. "Review on using Region of interest for HEVC." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 93. http://dx.doi.org/10.14419/ijet.v7i2.4.11173.

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High efficiency video coding (HEVC) is the latest video compression standard. The coding efficiency of HEVC is 50% more than the preceding standard Advanced video coding (AVC). HEVC has gained this by introducing many advanced techniques such as adaptive block partitioning system known as quadtree, tiles for parallelization, improved entropy coding called Context-Adaptive Binary Arithmetic Coding (CABAC), 35 intra prediction modes (IPMs), etc. all these techniques have increased the complexity of encoding process due to which real time application of HEVC for video transfer is not yet convenient. The main objective of this paper is to provide a review of the recent developments in HEVC, particularly focusing on using region of interest (ROI) for reducing the encoding process time. Summaries of the different approaches to identify the ROI are discussed and a new method is explained.

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Yang, Yang, and Hai Ge Li. "XML Query Based on Indexed Sequential Table." Advanced Materials Research 532-533 (June 2012): 1177–81. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1177.

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The current study based on XML index and query mostly focuses on encoding and the structural relation. Region codings are widely used to improve XML query. In this paper postorder-traversal region coding is proposed. The postorder of a node’s all descendants consists of the region. Judging and ensuring structural relation of any two nodes just depend on this region, if the postorder of a node is in a region, ancestor/descendant structural relation can be ensured. Consequently, postorder-traversal region coding can effectively judge structural relation and avoid traversing the XML document tree. Based on region coding, many constructive structural query algorithms have been put forward. As we all know that Stack-Tree-Desc algorithm is one of these fine algorithms, AList and DList only need separately scan one time to judge structural relation, however some unnecessary nodes still be scanned. In order to solve this problem, Indexed Sequential Table algorithm is introduced. The optimized algorithm introduces Indexed Sequential Table to avoid scanning unwanted nodes when the two lists join to locate next node which participates in structural join. In this case, some nodes of AList and DList which don’t participate in structural joins can be jumped, the query efficiency is enhanced. As a result, ordered scanning is prevented, the consuming time of XML query shortens accordingly. Experiment results demonstrate the effectiveness of the improved coding and algorithm.

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Wooderchak-Donahue, Whitney L., Jamie McDonald, Andrew Farrell, Gulsen Akay, Matt Velinder, Peter Johnson, Chad VanSant-Webb, et al. "Genome sequencing reveals a deep intronic splicing ACVRL1 mutation hotspot in Hereditary Haemorrhagic Telangiectasia." Journal of Medical Genetics 55, no. 12 (September 22, 2018): 824–30. http://dx.doi.org/10.1136/jmedgenet-2018-105561.

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IntroductionHereditary haemorrhagic telangiectasia (HHT) is a genetically heterogeneous disorder caused by mutations in the genes ENG, ACVRL1, and SMAD4. Yet the genetic cause remains unknown for some families even after exhaustive exome analysis. We hypothesised that non-coding regions of the known HHT genes may harbour variants that disrupt splicing in these cases.MethodsDNA from 35 individuals with clinical findings of HHT and 2 healthy controls from 13 families underwent whole genome sequencing. Additionally, 87 unrelated cases suspected to have HHT were evaluated using a custom designed next-generation sequencing panel to capture the coding and non-coding regions of ENG, ACVRL1 and SMAD4. Individuals from both groups had tested negative previously for a mutation in the coding region of known HHT genes. Samples were sequenced on a HiSeq2500 instrument and data were analysed to identify novel and rare variants.ResultsEight cases had a novel non-coding ACVRL1 variant that disrupted splicing. One family had an ACVRL1intron 9:chromosome 3 translocation, the first reported case of a translocation causing HHT. The other seven cases had a variant located within a ~300 bp CT-rich ‘hotspot’ region of ACVRL1intron 9 that disrupted splicing.ConclusionsDespite the difficulty of interpreting deep intronic variants, our study highlights the importance of non-coding regions in the disease mechanism of HHT, particularly the CT-rich hotspot region of ACVRL1intron 9. The addition of this region to HHT molecular diagnostic testing algorithms will improve clinical sensitivity.

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Lai, Ching-Fang, Chih-Ying Chen, and Lo-Chun Au. "Comparison between the Repression Potency of siRNA Targeting the Coding Region and the 3′-Untranslated Region of mRNA." BioMed Research International 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/637850.

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Small interfering RNAs (siRNAs) are applied for post-transcriptional gene silencing by binding target mRNA. A target coding region is usually chosen, although the3′-untranslated region (3′-UTR) can also be a target. This study elucidates whether the coding region or3′-UTR elicits higher repression. pFLuc and pRLuc are two reporter plasmids. A segment ofFLucgene was PCR-amplified and inserted behind the stop codon of theRLucgene of the pRLuc. Similarly, a segment ofRLucgene was inserted behind the stop codon ofFLuc. Two siFLuc and two siRLuc were siRNAs designed to target the central portions of these segments. Therefore, the siRNA encountered the same targets and flanking sequences. Results showed that the two siFLuc elicited higher repression when theFLucsegment resided in the coding region. Conversely, the two siRLuc showed higher repression when theRLucsegment was in the3′-UTR. These results indicate that both the coding region and the3′-UTR can be more effective targets. The thermodynamic stability of the secondary structures was analyzed. The siRNA elicited higher repression in the coding region when the target configuration was stable, and needed to be solved by translation. A siRNA may otherwise favor the target at3′-UTR.

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Moreno, M. L., U. Pauli, S. Chrysogelos, J. L. Stein, and G. S. Stein. "Persistence of a micrococcal nuclease sensitive region spanning the promoter–coding region junction of a cell cycle regulated human H4 histone gene throughout the cell cycle." Biochemistry and Cell Biology 66, no. 2 (February 1, 1988): 132–37. http://dx.doi.org/10.1139/o88-017.

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We have examined the chromatin structure of the cell cycle regulated human H4 histone gene FO108 A at various times during the cell cycle, by treating nuclei isolated from synchronized HeLa S3 cells with micrococcal nuclease. Purified DNA was fractionated electrophoretically, transferred to nitrocellulose, and hybridized to small (150–250 nucleotides) radiolabeled probes from various portions of the promoter and coding regions of the gene. Our results indicate the existence of a micrococcal nuclease sensitive region located between positions −60 and +90 base pairs (bp) from the start codon of the gene, which includes the TATA box. This nuclease-sensitive region persists at all the cell cycle times analyzed. Hybridization with a 250-bp probe containing only coding region sequences reveals a disrupted nucleosomal ladder during early S phase, when this H4 histone gene replicates and exhibits an enhanced level of transcription. By mid-S phase, the regular nucleosomal structure of the coding region is restored and persists during subsequent phases of the cell cycle. The disruption of a normal nucleosomal organization in the promoter and mRNA coding regions of this H4 histone gene is also supported by the sensitivity of these sequences to S1 nuclease.

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Wu, Jing. "Testing the Coding Potential of Conserved Short Genomic Sequences." Advances in Bioinformatics 2010 (March 8, 2010): 1–8. http://dx.doi.org/10.1155/2010/287070.

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Proposed is a procedure to test whether a genomic sequence contains coding DNA, called a coding potential region. The procedure tests the coding potential of conserved short genomic sequence, in which the assumptions on the probability models of gene structures are relaxed. Thus, it is expected to provide additional candidate regions that contain coding DNAs to the current genomic database. The procedure was applied to the set of highly conserved human-mouse sequences in the genome database at the University of California at Santa Cruz. For sequences containing RefSeq coding exons, the procedure detected 91.3% regions having coding potential in this set, which covers 83% of the human RefSeq coding exons, at a 2.6% false positive rate. The procedure detected 12,688 novel short regions with coding potential at the false discovery rate <0.05; 65.7% of the novel regions are between annotated genes.

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ZHANG, Shu, Yong CAI, and Mei XIE. "Face Detection Based on Local Region Sparse Coding." Journal of Software 24, no. 11 (January 3, 2014): 2747–57. http://dx.doi.org/10.3724/sp.j.1001.2013.04484.

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Anello, M., M. Silbestro, F. Veiga, V. Trasorras, L. Vidal Rioja, and F. Di Rocco. "P4057 Characterization of MITF coding region in llamas." Journal of Animal Science 94, suppl_4 (September 1, 2016): 107. http://dx.doi.org/10.2527/jas2016.94supplement4107x.

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Sanderson, H., and G. Crebbin. "Region-based image coding using polynomial intensity functions." IEE Proceedings - Vision, Image, and Signal Processing 143, no. 1 (1996): 15. http://dx.doi.org/10.1049/ip-vis:19960200.

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Yang, H., M. Long, and H. M. Tai. "Region-of-interest image coding based on EBCOT." IEE Proceedings - Vision, Image, and Signal Processing 152, no. 5 (2005): 590. http://dx.doi.org/10.1049/ip-vis:20041164.

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Naud, J. F., and D. M. Gibson. "A new coding region polymorphism of human IgLC2." European Journal of Immunogenetics 28, no. 1 (February 2001): 97–99. http://dx.doi.org/10.1046/j.1365-2370.2001.00239.x.

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Yang, YANG, and LI Hai-ge. "XML Structural Join Based on Extended Region Coding." Physics Procedia 33 (2012): 1374–80. http://dx.doi.org/10.1016/j.phpro.2012.05.225.

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Hu, Chunyue, Dongyang Li, Zhenyan Sun, Ning Zhang, and Jianjun Lei. "Region-based trilateral filter for depth video coding." International Journal of Embedded Systems 11, no. 2 (2019): 163. http://dx.doi.org/10.1504/ijes.2019.098293.

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Lei, Jianjun, Ning Zhang, Zhenyan Sun, Dongyang Li, and Chunyue Hu. "Region-based trilateral filter for depth video coding." International Journal of Embedded Systems 11, no. 2 (2019): 163. http://dx.doi.org/10.1504/ijes.2019.10019711.

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Yamamoto, Masao, Ichiro Matsuda, Susumu Itoh, and Toshio Utsunomiya. "Region-Oriented Orthogonal Transform Coding for Still Images." Journal of the Institute of Television Engineers of Japan 48, no. 5 (1994): 613–16. http://dx.doi.org/10.3169/itej1978.48.613.

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Bae, Cheol-Soo, and Hyun-yul Kim. "A FAST ALGORITHM FOR REGION-ORIENTED TEXTURE CODING." Journal of Korea Institute of Information, Electronics, and Communication Technology 7, no. 4 (December 30, 2014): 205–11. http://dx.doi.org/10.17661/jkiiect.2014.7.4.205.

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Choi, Young-Gyu, Chong-Hwan Choi, and Ha-Young Cheong. "A Fast Algorithm for Region-Oriented Texture Coding." Journal of Korea Institute of Information, Electronics, and Communication Technology 9, no. 6 (December 30, 2016): 519–25. http://dx.doi.org/10.17661/jkiiect.2016.9.6.519.

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Duursma, A. M., M. Kedde, M. Schrier, C. le Sage, and R. Agami. "miR-148 targets human DNMT3b protein coding region." RNA 14, no. 5 (March 27, 2008): 872–77. http://dx.doi.org/10.1261/rna.972008.

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Penedo, M., W. A. Pearlman, P. G. Tahoces, M. Souto, and J. J. Vidal. "Region-based wavelet coding methods for digital mammography." IEEE Transactions on Medical Imaging 22, no. 10 (October 2003): 1288–96. http://dx.doi.org/10.1109/tmi.2003.817812.

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Badger, J. H., and G. J. Olsen. "CRITICA: coding region identification tool invoking comparative analysis." Molecular Biology and Evolution 16, no. 4 (April 1, 1999): 512–24. http://dx.doi.org/10.1093/oxfordjournals.molbev.a026133.

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Journal articles on the topic 'Coding region'

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