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Relevant bibliographies by topics / Biological data / Journal articles

Journal articles on the topic 'Biological data'

To see the other types of publications on this topic, follow the link: Biological data.

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Hashmi, Faiz. "Elementary approach towards Biological Data Mining." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 1109–14. http://dx.doi.org/10.31142/ijtsrd7198.

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2

Wong, Bang. "Visualizing biological data." Nature Methods 9, no. 12 (December 2012): 1131. http://dx.doi.org/10.1038/nmeth.2258.

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3

Zaki, Mohammed J., Naren Ramakrishnan, and Srinivasan Parthasarathy. "Biological Data Mining." Scientific Programming 16, no. 1 (2008): 3. http://dx.doi.org/10.1155/2008/897294.

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4

Li, Peter. "Biological Data Extinction." OMICS: A Journal of Integrative Biology 7, no. 1 (January 2003): 49–50. http://dx.doi.org/10.1089/153623103322006599.

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5

Sakamoto, Ryoichi, and Shumpei Kojima. "Review of dolphinfish biological and fishing data in Japanese waters." Scientia Marina 63, no. 3-4 (December 30, 1999): 375–85. http://dx.doi.org/10.3989/scimar.1999.63n3-4375.

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6

NISHIDA, Kozo. "Biological Data and Visualization." Journal of the Visualization Society of Japan 40, no. 156 (2020): 2. http://dx.doi.org/10.3154/jvs.40.156_2.

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7

Kritchevsky, David. "Commentary: Monitoring biological data." Accountability in Research 1, no. 2 (October 1990): 85–86. http://dx.doi.org/10.1080/08989629008573778.

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8

Lacroix, Z. "Biological data integration: wrapping data and tools." IEEE Transactions on Information Technology in Biomedicine 6, no. 2 (June 2002): 123–28. http://dx.doi.org/10.1109/titb.2002.1006299.

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9

Abraham, Michael H., Joelle M. R. Gola, Rachel Kumarsingh, J. Enrique Cometto-Muniz, and William S. Cain. "Connection between chromatographic data and biological data." Journal of Chromatography B: Biomedical Sciences and Applications 745, no. 1 (August 2000): 103–15. http://dx.doi.org/10.1016/s0378-4347(00)00130-4.

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10

Ziegel, Eric R., and E. Roberts. "Sequential Data in Biological Experiments." Technometrics 36, no. 2 (May 1994): 230. http://dx.doi.org/10.2307/1270256.

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11

Evanko, Daniel. "Supplement on visualizing biological data." Nature Methods 7, S3 (March 2010): S1. http://dx.doi.org/10.1038/nmeth0310-s1.

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12

Carpendale, Sheelagh, Min Chen, Daniel Evanko, Nils Gehlenborg, Carsten Gorg, Larry Hunter, Francis Rowland, Margaret-Anne Storey, and Hendrik Strobelt. "Ontologies in Biological Data Visualization." IEEE Computer Graphics and Applications 34, no. 2 (March 2014): 8–15. http://dx.doi.org/10.1109/mcg.2014.33.

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13

Machiraju, Raghu, Carsten Gorg, and Arthur Olson. "Visual Analytics for Biological Data." IEEE Computer Graphics and Applications 34, no. 2 (2014): 24–25. http://dx.doi.org/10.1109/mcg.2014.38.

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14

Meyer, Miriah. "Designing Visualizations For Biological Data." Leonardo 46, no. 3 (June 2013): 270–71. http://dx.doi.org/10.1162/leon_a_00568.

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Visualization is now a vital component of the biological discovery process. This article presents visualization design studies as a promising approach for creating effective, visualization tools for biological data.

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15

Wong, L. "Technologies for integrating biological data." Briefings in Bioinformatics 3, no. 4 (January 1, 2002): 389–404. http://dx.doi.org/10.1093/bib/3.4.389.

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16

Parker, Michael. "Biological data access through Gopher." Trends in Biochemical Sciences 18, no. 12 (December 1993): 485–86. http://dx.doi.org/10.1016/s0968-0004(10)80001-5.

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17

Gropp, Robert E. "Big, Integrative, Open Biological Data." BioScience 66, no. 4 (March 30, 2016): 263–64. http://dx.doi.org/10.1093/biosci/biw045.

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18

Cohen, Nicholas. "Biological relevance of data variability." Brain, Behavior, and Immunity 18, no. 6 (November 2004): 495–96. http://dx.doi.org/10.1016/j.bbi.2004.06.004.

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19

Bhowmick, Sourav S., and Hasan M. Jamil. "Biological Data Management (BIDM 2003)." Data & Knowledge Engineering 53, no. 1 (April 2005): 1–2. http://dx.doi.org/10.1016/j.datak.2004.06.010.

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20

Field, Christopher A. "Modeling biological data: Several vignettes." Canadian Journal of Statistics 36, no. 3 (September 2008): 341–54. http://dx.doi.org/10.1002/cjs.5550360301.

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21

Rechenmann, François. "Data modeling: the key to biological data integration." EMBnet.journal 18, B (November 9, 2012): 59. http://dx.doi.org/10.14806/ej.18.b.550.

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22

Zhao, J., A. Miles, G. Klyne, and D. Shotton. "Linked data and provenance in biological data webs." Briefings in Bioinformatics 10, no. 2 (March 1, 2009): 139–52. http://dx.doi.org/10.1093/bib/bbn044.

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23

., Shyam Mohan J. S. "DATA REDUCTION TECHNIQUES FOR HIGH DIMENSIONAL BIOLOGICAL DATA." International Journal of Research in Engineering and Technology 05, no. 02 (February 25, 2016): 319–24. http://dx.doi.org/10.15623/ijret.2016.0502058.

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24

Kormeier, Benjamin, Klaus Hippe, and Ralf Hofestädt. "Data Warehouses in Bioinformatics: Integration of Molecular Biological Data." it - Information Technology 53, no. 5 (September 2011): 241–49. http://dx.doi.org/10.1524/itit.2011.0649.

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25

Raschid, Louiqa. "Data Modeling and Data Management for the Biological Enterprise." OMICS: A Journal of Integrative Biology 7, no. 1 (January 2003): 51–55. http://dx.doi.org/10.1089/153623103322006607.

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26

Srivastava, Chandan. "Biological Data Analysis: Error and Uncertainty." World Journal of Computer Application and Technology 1, no. 3 (November 2013): 67–74. http://dx.doi.org/10.13189/wjcat.2013.010302.

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27

Eliceiri, K. W., C. Rueden, W. A. Mohler, W. L. Hibbard, and J. G. White. "Analysis of Multidimensional Biological Image Data." BioTechniques 33, no. 6 (December 2002): 1268–73. http://dx.doi.org/10.2144/02336bt01.

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28

Grewal, Rumdeep Kaur, and Sampa Das. "Microarray data analysis: Gaining biological insights." Journal of Biomedical Science and Engineering 06, no. 10 (2013): 996–1005. http://dx.doi.org/10.4236/jbise.2013.610124.

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29

El-Bayomi, Kh M., El A. Rady, M. S. El-Tarabany, and Fatma D. Mohammed. "Statistical Analysis of Biological Survival Data." Zagazig Veterinary Journal 42, no. 1 (March 1, 2014): 129–39. http://dx.doi.org/10.21608/zvjz.2014.59478.

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30

Punjani, Dipti N., and Kishor H. Atkotiya. "Data Mining Techniques in Biological Research." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 339–43. http://dx.doi.org/10.26438/ijcse/v7i4.339343.

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31

Song, Bosheng, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, and Xiangzheng Fu. "Pretraining model for biological sequence data." Briefings in Functional Genomics 20, no. 3 (May 2021): 181–95. http://dx.doi.org/10.1093/bfgp/elab025.

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Abstract With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.

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32

Zhou, Shuigeng, Fa Zhang, and Le Zhang. "Bioinformatics in Biological Big Data Era." Current Bioinformatics 13, no. 5 (September 4, 2018): 435–36. http://dx.doi.org/10.2174/157489361305180806123102.

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33

Fry, J. C. "Biological Data Analysis: A Practical Approach." Biometrics 50, no. 1 (March 1994): 318. http://dx.doi.org/10.2307/2533236.

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34

Boddy, Lynne. "Deciphering biological data: a multifaceted problem." Journal of Biogeography 28, no. 3 (January 12, 2002): 409. http://dx.doi.org/10.1046/j.1365-2699.2001.0544a.x.

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35

DAVIDSON, S. B., C. OVERTON, and P. BUNEMAN. "Challenges in Integrating Biological Data Sources." Journal of Computational Biology 2, no. 4 (January 1995): 557–72. http://dx.doi.org/10.1089/cmb.1995.2.557.

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36

Wittkop, Tobias, Dorothea Emig, Sita Lange, Sven Rahmann, Mario Albrecht, John H. Morris, Sebastian Böcker, Jens Stoye, and Jan Baumbach. "Partitioning biological data with transitivity clustering." Nature Methods 7, no. 6 (June 2010): 419–20. http://dx.doi.org/10.1038/nmeth0610-419.

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37

Herbert, Katherine G., and Jason T. L. Wang. "Biological data cleaning: a case study." International Journal of Information Quality 1, no. 1 (2007): 60. http://dx.doi.org/10.1504/ijiq.2007.013376.

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38

Schatz, Michael C. "Biological data sciences in genome research." Genome Research 25, no. 10 (October 2015): 1417–22. http://dx.doi.org/10.1101/gr.191684.115.

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39

Li, Xiaowan, and Fei Zhu. "On Clustering Algorithms for Biological Data." Engineering 05, no. 10 (2013): 549–52. http://dx.doi.org/10.4236/eng.2013.510b113.

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40

Roberts, Dave, and Alex Hardisty. "Getting a handle on biological data." Nature 484, no. 7394 (April 2012): 318. http://dx.doi.org/10.1038/484318d.

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41

Jeffers, J. N. R. "Data logging in the biological sciences." Journal of Applied Statistics 12, no. 2 (January 1985): 107–12. http://dx.doi.org/10.1080/02664768500000016.

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42

Pook, S., G. Vaysseix, and E. Barillot. "Zomit: biological data visualization and browsing." Bioinformatics 14, no. 9 (October 1, 1998): 807–14. http://dx.doi.org/10.1093/bioinformatics/14.9.807.

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43

Johnson, Michael L. "Review of Fry, Biological Data Analysis." Biophysical Journal 67, no. 2 (August 1994): 937. http://dx.doi.org/10.1016/s0006-3495(94)80557-0.

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44

Al Hasan, Mohammad, Jun Huan, Jake Chen, and Mohammed J. Zaki. "Biological Knowledge Discovery and Data Mining." Scientific Programming 20, no. 1 (2012): 1–2. http://dx.doi.org/10.1155/2012/252681.

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45

LIU, HUIQING, and LIMSOON WONG. "DATA MINING TOOLS FOR BIOLOGICAL SEQUENCES." Journal of Bioinformatics and Computational Biology 01, no. 01 (April 2003): 139–67. http://dx.doi.org/10.1142/s0219720003000216.

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We describe a methodology, as well as some related data mining tools, for analyzing sequence data. The methodology comprises three steps: (a) generating candidate features from the sequences, (b) selecting relevant features from the candidates, and (c) integrating the selected features to build a system to recognize specific properties in sequence data. We also give relevant techniques for each of these three steps. For generating candidate features, we present various types of features based on the idea of k-grams. For selecting relevant features, we discuss signal-to-noise, t-statistics, and entropy measures, as well as a correlation-based feature selection method. For integrating selected features, we use machine learning methods, including C4.5, SVM, and Naive Bayes. We illustrate this methodology on the problem of recognizing translation initiation sites. We discuss how to generate and select features that are useful for understanding the distinction between ATG sites that are translation initiation sites and those that are not. We also discuss how to use such features to build reliable systems for recognizing translation initiation sites in DNA sequences.

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46

Stubbs, A., C. Stubbs, L. Best, and L. Smith. "Perceptual judgments, psychophysics, and biological data." Journal of Vision 7, no. 9 (March 30, 2010): 930. http://dx.doi.org/10.1167/7.9.930.

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47

Sung, Wing-Kin. "Pan-omics analysis of biological data." Methods 102 (June 2016): 1–2. http://dx.doi.org/10.1016/j.ymeth.2016.05.004.

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48

Wu, Fang-Xiang, and Min Li. "Deep learning for biological/clinical data." Neurocomputing 324 (January 2019): 1–2. http://dx.doi.org/10.1016/j.neucom.2018.05.047.

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49

Harrington, Eoghan D., Lars J. Jensen, and Peer Bork. "Predicting biological networks from genomic data." FEBS Letters 582, no. 8 (February 21, 2008): 1251–58. http://dx.doi.org/10.1016/j.febslet.2008.02.033.

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50

Noor, Elad, Sarah Cherkaoui, and Uwe Sauer. "Biological insights through omics data integration." Current Opinion in Systems Biology 15 (June 2019): 39–47. http://dx.doi.org/10.1016/j.coisb.2019.03.007.

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