Academic literature on the topic 'Sequence Feature'
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Journal articles on the topic "Sequence Feature"
Liu, Zhi-Hua, Dian Jiao, and Xiao Sun. "Classifying Genomic Sequences by Sequence Feature Analysis." Genomics, Proteomics & Bioinformatics 3, no. 4 (2005): 201–5. http://dx.doi.org/10.1016/s1672-0229(05)03027-5.
Full textCaspi, Yaron, Denis Simakov, and Michal Irani. "Feature-Based Sequence-to-Sequence Matching." International Journal of Computer Vision 68, no. 1 (March 1, 2006): 53–64. http://dx.doi.org/10.1007/s11263-005-4842-z.
Full textZhang, Tianjiao, Rongjie Wang, Qinghua Jiang, and Yadong Wang. "An Information Gain-based Method for Evaluating the Classification Power of Features Towards Identifying Enhancers." Current Bioinformatics 15, no. 6 (November 11, 2020): 574–80. http://dx.doi.org/10.2174/1574893614666191120141032.
Full textSHAN, YING, HARPREET S. SAWHNEY, and ART POPE. "CLUSTERING MULTIPLE IMAGE SEQUENCES WITH A SEQUENCE-TO-SEQUENCE SIMILARITY MEASURE." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 04 (June 2005): 551–64. http://dx.doi.org/10.1142/s0218001405004149.
Full textPugin, E. V., and A. L. Zhiznyakov. "CLASSIFICATION OF FEATURES OF IMAGE SEQUENCES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W6 (May 18, 2015): 79–81. http://dx.doi.org/10.5194/isprsarchives-xl-5-w6-79-2015.
Full textVezzi, Francesco, Giuseppe Narzisi, and Bud Mishra. "Feature-by-Feature – Evaluating De Novo Sequence Assembly." PLoS ONE 7, no. 2 (February 3, 2012): e31002. http://dx.doi.org/10.1371/journal.pone.0031002.
Full textTian, Huixin, and Qiangqiang Xu. "Time Series Prediction Method Based on E-CRBM." Electronics 10, no. 4 (February 8, 2021): 416. http://dx.doi.org/10.3390/electronics10040416.
Full textAL-SHAHIB, ALI, RAINER BREITLING, and DAVID GILBERT. "FRANKSUM: NEW FEATURE SELECTION METHOD FOR PROTEIN FUNCTION PREDICTION." International Journal of Neural Systems 15, no. 04 (August 2005): 259–75. http://dx.doi.org/10.1142/s0129065705000281.
Full textEt. al., Muthulakshmi M,. "A Novel Feature Extraction from Genome Sequences For Taxonomic Classification Of Living Organisms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 1436–51. http://dx.doi.org/10.17762/turcomat.v12i2.1364.
Full textHuang, Wen-Lin, Chun-Wei Tung, Chyn Liaw, Hui-Ling Huang, and Shinn-Ying Ho. "Rule-Based Knowledge Acquisition Method for Promoter Prediction in Human andDrosophilaSpecies." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/327306.
Full textDissertations / Theses on the topic "Sequence Feature"
Smith, Stephen Mark. "Feature based image sequence understanding." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316951.
Full textSung, Raymond Chun Wai. "Automatic assembly feature recognition and disassembly sequence generation." Thesis, Heriot-Watt University, 2001. http://hdl.handle.net/10399/478.
Full textLai, Man Lok Michael. "Image sequence coding using intensity-based feature separation." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284145.
Full textTangirala, Karthik. "Unsupervised feature construction approaches for biological sequence classification." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/19123.
Full textDepartment of Computing and Information Sciences
Doina Caragea
Recent advancements in biological sciences have resulted in the availability of large amounts of sequence data (DNA and protein sequences). Biological sequence data can be annotated using machine learning techniques, but most learning algorithms require data to be represented by a vector of features. In the absence of biologically informative features, k-mers generated using a sliding window-based approach are commonly used to represent biological sequences. A larger k value typically results in better features; however, the number of k-mer features is exponential in k, and many k-mers are not informative. Feature selection is widely used to reduce the dimensionality of the input feature space. Most feature selection techniques use feature-class dependency scores to rank the features. However, when the amount of available labeled data is small, feature selection techniques may not accurately capture feature-class dependency scores. Therefore, instead of working with all k-mers, this dissertation proposes the construction of a reduced set of informative k-mers that can be used to represent biological sequences. This work resulted in three novel unsupervised approaches to construct features: 1. Burrows Wheeler Transform-based approach, that uses the sorted permutations of a given sequence to construct sequential features (subsequences) that occur multiple times in a given sequence. 2. Community detection-based approach, that uses a community detection algorithm to group similar subsequences into communities and refines the communities to form motifs (group of similar subsequences). Motifs obtained using the community detection-based approach satisfy the ZOMOPS constraint (Zero, One or Multiple Occurrences of a Motif Per Sequence). All possible unique subsequences of the obtained motifs are then used as features to represent the sequences. 3. Hybrid-based approach, that combines the Burrows Wheeler Transform-based approach and the community detection-based approach to allow certain mismatches to the features constructed using the Burrows Wheeler Transform-based approach. To evaluate the predictive power of the features constructed using the proposed approaches, experiments were conducted in three learning scenarios: supervised, semi-supervised, and domain adaptation for both nucleotide and protein sequence classification problems. The performance of classifiers learned using features generated with the proposed approaches was compared with the performance of the classifiers learned using k-mers (with feature selection) and feature hashing (another unsupervised dimensionality reduction technique). Experimental results from the three learning scenarios showed that features constructed with the proposed approaches were typically more informative than k-mers and feature hashing.
Nilsson, Daniel. "Genomic feature identification in trypanosomatid parasites /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-789-8/.
Full textBozkurt, Burcin. "Prediction Of Protein Subcellular Localization Using Global Protein Sequence Feature." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/3/1135292/index.pdf.
Full textand self-organizing maps (SOM)"
are used. For classication purposes, support vector machines (SVM)"
, a method of statistical learning theory recently introduced to bioinformatics is used. The aim of the combination of feature extraction, clustering and classification methods is to design an acccurate system that predicts the subcellular localization of proteins presented into the system. Our scheme for combining several methods is cascading or serial combination according to its architecture. In the cascading architecture, the output of a method serves as the input of the other model used.
Islamaj, Rezarta. "Feature generation and analysis applied to sequence classification for splice-site prediction." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7745.
Full textThesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Lakshmanan, Arun. "Practice makes imperfect? sequence learning and the discontinuous acquisition of feature use skills /." [Bloomington, Ind.] : Indiana University, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3331241.
Full textTitle from PDF t.p. (viewed on Jul 23, 2009). Source: Dissertation Abstracts International, Volume: 69-11, Section: A, page: 4418. Adviser: Shanker Krishnan.
Ali, Isse. "Analysing and predicting differences between methylated and unmethylated DNA sequence features." Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/12616.
Full textAbril, Ferrando Josep Francesc. "Comparative analysis of eukaryotic gene sequence features." Doctoral thesis, Universitat Pompeu Fabra, 2005. http://hdl.handle.net/10803/7108.
Full textl'increment del nombre de tècniques experimentals de les que es disposa,
permetrà obtenir el catàleg complet de les funcions cel.lulars de
diferents organismes, incloent-hi la nostra espècie. Aquest catàleg
definirà els fonaments sobre els que es podrà entendre millor com els
organismes funcionen a nivell molecular. Al mateix temps es tindran més
pistes sobre els canvis que estan associats amb les malalties. Per tant,
la seqüència en brut, tal i com s'obté dels projectes de seqüenciació de
genomes, no té cap valor sense les anàlisis i la subsegüent anotació de
les característiques que defineixen aquestes funcions. Aquesta tesi
presenta la nostra contribució en tres aspectes relacionats de
l'anotació dels gens en genomes eucariotes.
Primer, la comparació a nivell de seqüència entre els genomes humà i de
ratolí es va dur a terme mitjançant un protocol semi-automàtic. El
programa de predicció de gens SGP2 es va desenvolupar a partir
d'elements d'aquest protocol. El concepte al darrera de l'SGP2 és que
les regions de similaritat obtingudes amb el programa TBLASTX, es fan
servir per augmentar la puntuació dels exons predits pel programa
geneid, amb el que s obtenen conjunts d'anotacions més acurats
d'estructures gèniques. SGP2 té una especificitat que és prou gran com
per que es puguin validar experimentalment via RT-PCR. La validació de
llocs d'splicing emprant la tècnica de la RT-PCR és un bon exemple de
com la combinació d'aproximacions computacionals i experimentals
produeix millors resultats que per separat.
S'ha dut a terme l'anàlisi descriptiva a nivell de seqüència dels llocs
d'splicing obtinguts sobre un conjunt fiable de gens ortòlegs per humà,
ratolí, rata i pollastre. S'han explorat les diferències a nivell de
nucleòtid entre llocs U2 i U12, pel conjunt d'introns ortòlegs que se'n
deriva d'aquests gens. S'ha trobat que els senyals d'splicing ortòlegs
entre humà i rossegadors, així com entre rossegadors, estan més
conservats que els llocs no relacionats. Aquesta conservació addicional
pot ser explicada però a nivell de conservació basal dels introns.
D'altra banda, s'ha detectat més conservació de l'esperada entre llocs
d'splicing ortòlegs entre mamífers i pollastre. Els resultats obtinguts
també indiquen que les classes intròniques U2 i U12 han evolucionat
independentment des de l'ancestre comú dels mamífers i les aus. Tampoc
s'ha trobat cap cas convincent d'interconversió entre aquestes dues
classes en el conjunt d'introns ortòlegs generat, ni cap cas de
substitució entre els subtipus AT-AC i GT-AG d'introns U12. Al contrari,
el pas de GT-AG a GC-AG, i viceversa, en introns U2 no sembla ser inusual.
Finalment, s'han implementat una sèrie d'eines de visualització per
integrar anotacions obtingudes pels programes de predicció de gens i per
les anàlisis comparatives sobre genomes. Una d'aquestes eines, el
gff2ps, s'ha emprat en la cartografia dels genomes humà, de la mosca del
vinagre i del mosquit de la malària, entre d'altres. El programa
gff2aplot i els filtres associats, han facilitat la tasca d'integrar
anotacions de seqüència amb els resultats d'eines per la cerca
d'homologia, com ara el BLAST. S'ha adaptat també el concepte de
pictograma a l'anàlisi comparativa de llocs d splicing ortòlegs, amb el
desenvolupament del programa compi.
El aumento incesante del número de secuencias genómicas, junto con el
incremento del número de técnicas experimentales de las que se dispone,
permitirá la obtención del catálogo completo de las funciones celulares
de los diferentes organismos, incluida nuestra especie. Este catálogo
definirá las bases sobre las que se pueda entender mejor el
funcionamiento de los organismos a nivel molecular. Al mismo tiempo, se
obtendrán más pistas sobre los cambios asociados a enfermedades. Por
tanto, la secuencia en bruto, tal y como se obtiene en los proyectos de
secuenciación masiva, no tiene ningún valor sin los análisis y la
posterior anotación de las características que definen estas funciones.
Esta tesis presenta nuestra contribución a tres aspectos relacionados de
la anotación de los genes en genomas eucariotas.
Primero, la comparación a nivel de secuencia entre el genoma humano y el
de ratón se llevó a cabo mediante un protocolo semi-automático. El
programa de predicción de genes SGP2 se desarrolló a partir de elementos
de dicho protocolo. El concepto sobre el que se fundamenta el SGP2 es
que las regiones de similaridad obtenidas con el programa TBLASTX, se
utilizan para aumentar la puntuación de los exones predichos por el
programa geneid, con lo que se obtienen conjuntos más precisos de
anotaciones de estructuras génicas. SGP2 tiene una especificidad
suficiente como para validar esas anotaciones experimentalmente vía
RT-PCR. La validación de los sitios de splicing mediante el uso de la
técnica de la RT-PCR es un buen ejemplo de cómo la combinación de
aproximaciones computacionales y experimentales produce mejores
resultados que por separado.
Se ha llevado a cabo el análisis descriptivo a nivel de secuencia de los
sitios de splicing obtenidos sobre un conjunto fiable de genes ortólogos
para humano, ratón, rata y pollo. Se han explorado las diferencias a
nivel de nucleótido entre sitios U2 y U12 para el conjunto de intrones
ortólogos derivado de esos genes. Se ha visto que las señales de
splicing ortólogas entre humanos y roedores, así como entre roedores,
están más conservadas que las no ortólogas. Esta conservación puede ser
explicada en parte a nivel de conservación basal de los intrones. Por
otro lado, se ha detectado mayor conservación de la esperada entre
sitios de splicing ortólogos entre mamíferos y pollo. Los resultados
obtenidos indican también que las clases intrónicas U2 y U12 han
evolucionado independientemente desde el ancestro común de mamíferos y
aves. Tampoco se ha hallado ningún caso convincente de interconversión
entre estas dos clases en el conjunto de intrones ortólogos generado, ni
ningún caso de substitución entre los subtipos AT-AC y GT-AG en intrones
U12. Por el contrario, el paso de GT-AG a GC-AG, y viceversa, en
intrones U2 no parece ser inusual.
Finalmente, se han implementado una serie de herramientas de
visualización para integrar anotaciones obtenidas por los programas de
predicción de genes y por los análisis comparativos sobre genomas. Una
de estas herramientas, gff2ps, se ha utilizado para cartografiar los
genomas humano, de la mosca del vinagre y del mosquito de la malaria. El
programa gff2aplot y los filtros asociados, han facilitado la tarea de
integrar anotaciones a nivel de secuencia con los resultados obtenidos
por herramientas de búsqueda de homología, como BLAST. Se ha adaptado
también el concepto de pictograma al análisis comparativo de los sitios
de splicing ortólogos, con el desarrollo del programa compi.
The constantly increasing amount of available genome sequences, along
with an increasing number of experimental techniques, will help to
produce the complete catalog of cellular functions for different
organisms, including humans. Such a catalog will define the base from
which we will better understand how organisms work at the molecular
level. At the same time it will shed light on which changes are
associated with disease. Therefore, the raw sequence from genome
sequencing projects is worthless without the complete analysis and
further annotation of the genomic features that define those functions.
This dissertation presents our contribution to three related aspects of
gene annotation on eukaryotic genomes.
First, a comparison at sequence level of human and mouse genomes was
performed by developing a semi-automatic analysis pipeline. The SGP2
gene-finding tool was developed from procedures used in this pipeline.
The concept behind SGP2 is that similarity regions obtained by TBLASTX
are used to increase the score of exons predicted by geneid, in order to
produce a more accurate set of gene structures. SGP2 provides a
specificity that is high enough for its predictions to be experimentally
verified by RT-PCR. The RT-PCR validation of predicted splice junctions
also serves as example of how combined computational and experimental
approaches will yield the best results.
Then, we performed a descriptive analysis at sequence level of the
splice site signals from a reliable set of orthologous genes for human,
mouse, rat and chicken. We have explored the differences at nucleotide
sequence level between U2 and U12 for the set of orthologous introns
derived from those genes. We found that orthologous splice signals
between human and rodents and within rodents are more conserved than
unrelated splice sites. However, additional conservation can be
explained mostly by background intron conservation. Additional
conservation over background is detectable in orthologous mammalian and
chicken splice sites. Our results also indicate that the U2 and U12
intron classes have evolved independently since the split of mammals and
birds. We found neither convincing case of interconversion between these
two classes in our sets of orthologous introns, nor any single case of
switching between AT-AC and GT-AG subtypes within U12 introns. In
contrast, switching between GT-AG and GC-AG U2 subtypes does not appear
to be unusual.
Finally, we implemented visualization tools to integrate annotation
features for gene- finding and comparative analyses. One of those tools,
gff2ps, was used to draw the whole genome maps for human, fruitfly and
mosquito. gff2aplot and the accompanying parsers facilitate the task of
integrating sequence annotations with the output of homologybased tools,
like BLAST.We have also adapted the concept of pictograms to the
comparative analysis of orthologous splice sites, by developing compi.
Books on the topic "Sequence Feature"
Maroni, Gustavo. An atlas of Drosophila genes: Sequences and molecular features. New York: Oxford University Press, 1993.
Find full textWhat's a sequencer?: A basic guide to their features and use. Milwaukee, WI, U.S.A: H. Leonard Pub. Corp., 1990.
Find full textEmile, Menasché, ed. What's a sequencer?: A basic guide to their features and use. 2nd ed. Milwaukee: Hal Leonard, 2001.
Find full textNeelanarayanan, ed. Feature Analysis for Abnormality Detection in Breast Thermogram Sequences Subject to Cold Stress. VIT University Chennai, India: Association of Scientists, Developers and Faculties, 2014.
Find full textWebb, Graham. The animated film encyclopedia: A complete guide to American shorts, features and sequences 1900-1979. Jefferson, N.C: McFarland, 2000.
Find full textWebb, Graham. The animated film encyclopedia: A complete guide to American shorts, features, and sequences, 1900-1979. Jefferson, N.C: McFarland & Co., 2000.
Find full textThe animated film encyclopedia: A complete guide to American shorts, features, and sequences, 1900-1979. Jefferson, N.C: McFarland & Co., 2006.
Find full textThe animated film encyclopedia: A complete guide to American shorts, features and sequences, 1900-1999. 2nd ed. Jefferson, N.C: McFarland & Co., 2011.
Find full textScherbak, Aleksey, Vladimir Andreev, and Afanasiy Zubov. IT risk management. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1900623.
Full textDavdiev, Kurban, and Ayub Omarov. Repair of cars and engines: final qualifying work. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1014616.
Full textBook chapters on the topic "Sequence Feature"
Anselmetti, B., A. Chep, K. Mawussi, and F. Villeneuve. "Feature-State Approach for Operation Sequence Generation." In Integrated Design and Manufacturing in Mechanical Engineering, 93–102. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5588-5_10.
Full textSaeys, Yvan, and Yves Van de Peer. "Enhancing Coding Potential Prediction for Short Sequences Using Complementary Sequence Features and Feature Selection." In Knowledge Discovery and Emergent Complexity in Bioinformatics, 107–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71037-0_7.
Full textFaraggiana, R., F. Castelli, M. Gerbaldi, and M. Floquet. "The LI I 6708 Feature in CP Stars." In Upper Main Sequence Stars with Anomalous Abundances, 451–53. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4714-6_70.
Full textMaitzen, H. M., and H. Hensberge. "The Variability of the λ5200 Feature in CP2 Stars." In Upper Main Sequence Stars with Anomalous Abundances, 183–87. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4714-6_29.
Full textBailer, Werner. "A Feature Sequence Kernel for Video Concept Classification." In Lecture Notes in Computer Science, 359–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17832-0_34.
Full textByrne, Charles J. "Feature Diameter as a Function of Sequence Number." In The Moon's Largest Craters and Basins, 15–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22032-1_3.
Full textYang, Yong, Longbing Cao, and Li Liu. "Time-Sensitive Feature Mining for Temporal Sequence Classification." In PRICAI 2010: Trends in Artificial Intelligence, 315–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15246-7_30.
Full textDai, Jingchao, Kaiqi Yuan, Yuexiang Xie, and Ying Shen. "Feature-Aware Attentive Convolutional Neural Network for Sequence Processing." In Knowledge Science, Engineering and Management, 313–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29563-9_28.
Full textRen, Jiadong, Changzhen Hu, Kunsheng Wang, and Dongmei Zhang. "Software Fault Feature Clustering Algorithm Based on Sequence Pattern." In Web Information Systems and Mining, 439–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05250-7_46.
Full textTangirala, Karthik, Nic Herndon, and Doina Caragea. "Community Detection-Based Feature Construction for Protein Sequence Classification." In Bioinformatics Research and Applications, 331–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19048-8_28.
Full textConference papers on the topic "Sequence Feature"
Chan, Patrick P. K., Xian Hu, Lili Zhao, Daniel S. Yeung, Dapeng Liu, and Lei Xiao. "Convolutional Neural Networks based Click-Through Rate Prediction with Multiple Feature Sequences." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/277.
Full textKim, Yong Se, Eric Wang, Choong Soo Lee, and Hyung Min Rho. "Feature-Based Machining Precedence Reasoning and Sequence Planning." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/cie-5707.
Full textCaragea, Cornelia, Adrian Silvescu, and Prasenjit Mitra. "Protein Sequence Classification Using Feature Hashing." In 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2011. http://dx.doi.org/10.1109/bibm.2011.91.
Full textLan, Yihua, Haozheng Ren, Cunhua Li, Xuefeng Zhao, and Zhifang Min. "Feature Based Sequence Image Stitching Method." In 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677208.
Full textKobayashi, Takumi. "Feature Sequence Representation Via Slow Feature Analysis For Action Classification." In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.125.
Full textBhujle, Hemalata. "Feature-preserving 3D fluorescence image sequence denoising." In the Tenth Indian Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/3009977.3009983.
Full textRavaut, Frederic, and Georges Stamon. "Image sequence analysis and face feature extraction." In Electronic Imaging '97, edited by Georges G. Grinstein and Robert F. Erbacher. SPIE, 1997. http://dx.doi.org/10.1117/12.270328.
Full textChen, Mei, Qingmei Xiao, Kazuyuki Matsumoto, and Xin Luo. "Detecting Repeating Pattern in Fingerprint Feature Sequence." In 2015 Fifth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2015. http://dx.doi.org/10.1109/imccc.2015.396.
Full textWang, Xianwang, Qing Zhang, Ruigang Yang, Brent Seales, and Melody Carswell. "Feature-based texture mapping from video sequence." In I3D08: Symposium on Interactive 3D Graphics and Games. New York, NY, USA: ACM, 2008. http://dx.doi.org/10.1145/1342250.1357018.
Full textCHOU, WEI-YAO, TUN-WEN PAI, JIM ZONE-CHANG LAI, WEN-SHYONG TZOU, MARGARET DAH-TSYR CHANG, HAO-TENG CHANG, WEI-YI CHOU, and TAN-CHI FAN. "MULTIPLE INDEXING SEQUENCE ALIGNMENT FOR GROUP FEATURE IDENTIFICATION." In Proceedings of the 3rd Annual RECOMB Workshop. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2008. http://dx.doi.org/10.1142/9781848162525_0019.
Full textReports on the topic "Sequence Feature"
Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, December 2014. http://dx.doi.org/10.32747/2014.7594391.bard.
Full textKellerLynn, Katie. John Muir National Historic Site: Geologic resources inventory report. National Park Service, December 2021. http://dx.doi.org/10.36967/nrr-2288497.
Full textClark, Todd E., Gergely Ganics, and Elmar Mertens. Constructing fan charts from the ragged edge of SPF forecasts. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-202236.
Full textSlattery, S. R., P. J. Barnett, A. J. M. Pugin, D. R. Sharpe, D. Goodyear, R E Gerber, S. Holysh, and S. Davies. Tunnel-channel complexes in the Zephyr area, Ontario: potential high-yield aquifers. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331410.
Full textGutnick, David, and David L. Coplin. Role of Exopolysaccharides in the Survival and Pathogenesis of the Fire Blight Bacterium, Erwinia amylovora. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568788.bard.
Full textDavidson, Irit, Hsing-Jien Kung, and Richard L. Witter. Molecular Interactions between Herpes and Retroviruses in Dually Infected Chickens and Turkeys. United States Department of Agriculture, January 2002. http://dx.doi.org/10.32747/2002.7575275.bard.
Full textOstersetzer-Biran, Oren, and Alice Barkan. Nuclear Encoded RNA Splicing Factors in Plant Mitochondria. United States Department of Agriculture, February 2009. http://dx.doi.org/10.32747/2009.7592111.bard.
Full textOstersetzer-Biran, Oren, and Jeffrey Mower. Novel strategies to induce male sterility and restore fertility in Brassicaceae crops. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7604267.bard.
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