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Journal articles on the topic 'Analysis of biological data'

Author: Grafiati

Published: 6 September 2023

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1

Dwivedi, Vivek Dhar, Indra Prasad Tripathi, Aman Chandra Kaushik, Shiv Bharadwaj, and Sarad Kumar Mishra. "Biological Data Analysis Program (BDAP): a multitasking biological sequence analysis program." Neural Computing and Applications 30, no. 5 (2016): 1493–501. http://dx.doi.org/10.1007/s00521-016-2772-z.

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2

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

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3

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 (2002): 1268–73. http://dx.doi.org/10.2144/02336bt01.

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4

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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5

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 (2014): 129–39. http://dx.doi.org/10.21608/zvjz.2014.59478.

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6

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

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7

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

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8

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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9

Stansfield, William D., and Matthew A. Carlton. "Bayesian Statistics for Biological Data: Pedigree Analysis." American Biology Teacher 66, no. 3 (2004): 177–82. http://dx.doi.org/10.2307/4451651.

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10

Topaz, Chad M., Lori Ziegelmeier, and Tom Halverson. "Topological Data Analysis of Biological Aggregation Models." PLOS ONE 10, no. 5 (2015): e0126383. http://dx.doi.org/10.1371/journal.pone.0126383.

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11

Zhang, Weiping, Jingzhi Yang, Yanling Fang, Huanyu Chen, Yihua Mao, and Mohit Kumar. "Analytical fuzzy approach to biological data analysis." Saudi Journal of Biological Sciences 24, no. 3 (2017): 563–73. http://dx.doi.org/10.1016/j.sjbs.2017.01.027.

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12

Norris, Richard H. "Biological Monitoring: The Dilemma of Data Analysis." Journal of the North American Benthological Society 14, no. 3 (1995): 440–50. http://dx.doi.org/10.2307/1467210.

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13

Kim, Tae Yong, Hyun Uk Kim, and Sang Yup Lee. "Data integration and analysis of biological networks." Current Opinion in Biotechnology 21, no. 1 (2010): 78–84. http://dx.doi.org/10.1016/j.copbio.2010.01.003.

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14

Haynes, Paul A., Steven P. Gygi, Daniel Figeys, and Ruedi Aebersold. "Proteome analysis: Biological assay or data archive?" Electrophoresis 19, no. 11 (1998): 1862–71. http://dx.doi.org/10.1002/elps.1150191104.

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15

Moussati, Omar, and Mohamed Benyettou. "Analysis of Microarray Data." Circulation in Computer Science 2, no. 1 (2017): 5–8. http://dx.doi.org/10.22632/ccs-2017-251-42.

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The computerized interpretation of biological information has taken a great interest in the scientific community, since it opens up very rich perspectives for the understanding of biological phenomena. These phenomena require collaboration between biologists, doctors, computer scientists, mathematicians and physicists. In this article we studied one of the most important subjects of bioinformatics, it is the biochip.We presented the various steps involved in the analysis of microarray data, Then we applied the KPPV method to the biochip data.

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16

Baudot, Pierre, Monica Tapia, Daniel Bennequin, and Jean-Marc Goaillard. "Topological Information Data Analysis." Entropy 21, no. 9 (2019): 869. http://dx.doi.org/10.3390/e21090869.

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This paper presents methods that quantify the structure of statistical interactions within a given data set, and were applied in a previous article. It establishes new results on the k-multivariate mutual-information ( I k ) inspired by the topological formulation of Information introduced in a serie of studies. In particular, we show that the vanishing of all I k for 2 ≤ k ≤ n of n random variables is equivalent to their statistical independence. Pursuing the work of Hu Kuo Ting and Te Sun Han, we show that information functions provide co-ordinates for binary variables, and that they are analytically independent from the probability simplex for any set of finite variables. The maximal positive I k identifies the variables that co-vary the most in the population, whereas the minimal negative I k identifies synergistic clusters and the variables that differentiate–segregate the most in the population. Finite data size effects and estimation biases severely constrain the effective computation of the information topology on data, and we provide simple statistical tests for the undersampling bias and the k-dependences. We give an example of application of these methods to genetic expression and unsupervised cell-type classification. The methods unravel biologically relevant subtypes, with a sample size of 41 genes and with few errors. It establishes generic basic methods to quantify the epigenetic information storage and a unified epigenetic unsupervised learning formalism. We propose that higher-order statistical interactions and non-identically distributed variables are constitutive characteristics of biological systems that should be estimated in order to unravel their significant statistical structure and diversity. The topological information data analysis presented here allows for precisely estimating this higher-order structure characteristic of biological systems.

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17

Paparountas, Triantafyllos, Maria Nefeli Nikolaidou-Katsaridou, Gabriella Rustici, and Vasilis Aidinis. "Data Mining and Meta-Analysis on DNA Microarray Data." International Journal of Systems Biology and Biomedical Technologies 1, no. 3 (2012): 1–39. http://dx.doi.org/10.4018/ijsbbt.2012070101.

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Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

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18

Nounou, Mohamed, Hazem Nounou, Nader Meskin, and Aniruddha Datta. "Multiscale denoising of biological data: A comparative analysis." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): CSP27. http://dx.doi.org/10.5339/qfarf.2012.csp27.

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19

Zimeras, Stelios. "Exploratory Point Pattern Analysis for Modeling Biological Data." International Journal of Systems Biology and Biomedical Technologies 2, no. 1 (2013): 1–13. http://dx.doi.org/10.4018/ijsbbt.2013010101.

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Data in the form of sets of points, irregular distributed in a region of space could be identified in varies biological applications for examples the cell nuclei in a microscope section of tissue. These kinds of data sets are defined as spatial point patterns and the presentation of the positions in the space are defined as points. The spatial pattern generated by a biological process, can be affected by the physical scale on which the process is observed. With these spatial maps, the biologists will usually want a detailed description of the observed patterns. One way to achieve this is by forming a parametric stochastic model and fitting it to the data. The estimated values of the parameters could be used to compare similar data sets providing statistical measures for fitting models. Also a fitted model can provide an explanation of the biological processes. Model fitting especially for large data sets is difficult. For that reason, statistical methods can apply with main purpose to formulate a hypothesis for the implementation of biological process. Spatial statistics could be implemented using advance statistical techniques that explicitly analyses and simulates point structures data sets. Typically spatial point patterns are data that explain the location of point events. The author’s interest is the investigation of the significance of these patterns. In this work, an investigation of biological spatial data is analyzed, using advance statistical modeling techniques like kriging.

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20

Morain, Stanley A. "Emerging Technology for Biological Data Collection and Analysis." Annals of the Missouri Botanical Garden 80, no. 2 (1993): 309. http://dx.doi.org/10.2307/2399786.

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21

Burgman, Mark. "Biological Data Analysis: A Practical Approach.John C. Fry." Quarterly Review of Biology 69, no. 1 (1994): 89–90. http://dx.doi.org/10.1086/418448.

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22

Peterson, Kent W. "Practical Applications in Analysis of Biological Monitoring Data." Journal of Occupational and Environmental Medicine 32, no. 4 (1990): 377. http://dx.doi.org/10.1097/00043764-199004000-00064.

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23

Madeira, S. C., and A. L. Oliveira. "Biclustering algorithms for biological data analysis: a survey." IEEE/ACM Transactions on Computational Biology and Bioinformatics 1, no. 1 (2004): 24–45. http://dx.doi.org/10.1109/tcbb.2004.2.

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24

Nounou, M. N., H. N. Nounou, N. Meskin, A. Datta, and E. R. Dougherty. "Multiscale Denoising of Biological Data: A Comparative Analysis." IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, no. 5 (2012): 1539–45. http://dx.doi.org/10.1109/tcbb.2012.67.

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25

Zhou, Fang, Luo Qingming, Zhang Guoqing, and Li Ixue. "Biological networks to the analysis of microarray data." Progress in Natural Science 16, no. 12 (2006): 1242–51. http://dx.doi.org/10.1080/10020070612330137.

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26

Ahmad, Iftikhar, Muhammad Javed Iqbal, and Mohammad Basheri. "Biological Data Classification and Analysis Using Convolutional Neural Network." Journal of Medical Imaging and Health Informatics 10, no. 10 (2020): 2459–65. http://dx.doi.org/10.1166/jmihi.2020.3179.

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The size of data gathered from various ongoing biological and clinically studies is increasing at an exponential rate. The bio-inspired data mainly comprises of genes of DNA, protein and variety of proteomics and genetic diseases. Additionally, DNA microarray data is also available for early diagnosis and prediction of various types of cancer diseases. Interestingly, this data may store very vital information about genes, their structure and important biological function. The huge volume and constant increase in the extracted bio data has opened several challenges. Many bioinformatics and machine learning models have been developed but those fail to address key challenges presents in the efficient and accurate analysis of variety of complex biologically inspired data such as genetic diseases etc. The reliable and robust process of classifying the extracted data into different classes based on the information hidden in the sample data is also a very interesting and open problem. This research work mainly focuses to overcome major challenges in the accurate protein classification keeping in view of the success of deep learning models in natural language processing since it assumes the proteins sequences as a language. The learning ability and overall classification performance of the proposed system can be validated with deep learning classification models. The proposed system can have the superior ability to accurately classify the mentioned datasets than previous approaches and shows better results. The in-depth analysis of multifaceted biological data may also help in the early diagnosis of diseases that causes due to mutation of genes and to overcome arising challenges in the development of large-scale healthcare systems.

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27

Ahmad, Iftikhar, Muhammad Javed Iqbal, and Mohammad Basheri. "Biological Data Classification and Analysis Using Convolutional Neural Network." Journal of Medical Imaging and Health Informatics 10, no. 10 (2020): 2459–65. http://dx.doi.org/10.1166/jmihi.2020.31792459.

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Abstract:

The size of data gathered from various ongoing biological and clinically studies is increasing at an exponential rate. The bio-inspired data mainly comprises of genes of DNA, protein and variety of proteomics and genetic diseases. Additionally, DNA microarray data is also available for early diagnosis and prediction of various types of cancer diseases. Interestingly, this data may store very vital information about genes, their structure and important biological function. The huge volume and constant increase in the extracted bio data has opened several challenges. Many bioinformatics and machine learning models have been developed but those fail to address key challenges presents in the efficient and accurate analysis of variety of complex biologically inspired data such as genetic diseases etc. The reliable and robust process of classifying the extracted data into different classes based on the information hidden in the sample data is also a very interesting and open problem. This research work mainly focuses to overcome major challenges in the accurate protein classification keeping in view of the success of deep learning models in natural language processing since it assumes the proteins sequences as a language. The learning ability and overall classification performance of the proposed system can be validated with deep learning classification models. The proposed system can have the superior ability to accurately classify the mentioned datasets than previous approaches and shows better results. The in-depth analysis of multifaceted biological data may also help in the early diagnosis of diseases that causes due to mutation of genes and to overcome arising challenges in the development of large-scale healthcare systems.

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28

Olson, N. Eric. "The microarray data analysis process: From raw data to biological significance." NeuroRX 3, no. 3 (2006): 373–83. http://dx.doi.org/10.1016/j.nurx.2006.05.005.

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29

Olson, N. Eric. "The microarray data analysis process: From raw data to biological significance." Neurotherapeutics 3, no. 3 (2006): 373–83. http://dx.doi.org/10.1007/bf03206660.

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30

Bertrand, Daniel, and Charles-Roland Bader. "Datac: A multipurpose biological data analysis program based on a mathematical interpreter." International Journal of Bio-Medical Computing 18, no. 3-4 (1986): 193–202. http://dx.doi.org/10.1016/0020-7101(86)90016-4.

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31

AHMED, WAMIQ MANZOOR, MUHAMMAD NAEEM AYYAZ, BARTEK RAJWA, FARRUKH KHAN, ARIF GHAFOOR, and J. PAUL ROBINSON. "SEMANTIC ANALYSIS OF BIOLOGICAL IMAGING DATA: CHALLENGES AND OPPORTUNITIES." International Journal of Semantic Computing 01, no. 01 (2007): 67–85. http://dx.doi.org/10.1142/s1793351x07000032.

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Microscopic imaging is one of the most common techniques for investigating biological systems. In recent years there has been a tremendous growth in the volume of biological imaging data owing to rapid advances in optical instrumentation, high-speed cameras and fluorescent probes. Powerful semantic analysis tools are required to exploit the full potential of the information content of these data. Semantic analysis of multi-modality imaging data, however, poses unique challenges. In this paper we outline the state-of-the-art in this area along with the challenges facing this domain. Information extraction from biological imaging data requires modeling at multiple levels of detail. While some applications require only quantitative analysis at the level of cells and subcellular objects, others require modeling of spatial and temporal changes associated with dynamic biological processes. Modeling of biological data at different levels of detail allows not only quantitative analysis but also the extraction of high-level semantics. Development of powerful image interpretation and semantic analysis tools has the potential to significantly help in understanding biological processes, which in turn will result in improvements in drug development and healthcare.

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32

Bocquet-Appel, Jean-Pierre, and Robert R. Sokal. "Spatial Autocorrelation Analysis of Trend Residuals in Biological Data." Systematic Zoology 38, no. 4 (1989): 333. http://dx.doi.org/10.2307/2992399.

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33

El-Bayomi, Khairy, Fatma Mohamed, Mahmoud Eltarabany, and Hagar Gouda. "Application of Different Biostatistical Methods in Biological Data Analysis." Zagazig Veterinary Journal 47, no. 2 (2019): 203–12. http://dx.doi.org/10.21608/zvjz.2019.11121.1034.

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34

Hyka, Oleksii. "Data analysis system for surface potential of biological tissues." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 5 (2022): 159–62. http://dx.doi.org/10.15199/48.2022.05.29.

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35

C.P.Chandran, N. Sevugapandi,. "Analysis of Microarray based Biological Pathway using Data Mining." International Journal of Innovative Research in Science, Engineering and Technology 04, no. 07 (2015): 5326–31. http://dx.doi.org/10.15680/ijirset.2015.0407038.

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36

On, Natthakan Iam, Tossapon Boongoen, Simon Garrett, and Chris Price. "New cluster ensemble approach to integrative biological data analysis." International Journal of Data Mining and Bioinformatics 8, no. 2 (2013): 150. http://dx.doi.org/10.1504/ijdmb.2013.055495.

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37

Raza, Khalid. "Formal concept analysis for knowledge discovery from biological data." International Journal of Data Mining and Bioinformatics 18, no. 4 (2017): 281. http://dx.doi.org/10.1504/ijdmb.2017.088138.

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38

Raza, Khalid. "Formal concept analysis for knowledge discovery from biological data." International Journal of Data Mining and Bioinformatics 18, no. 4 (2017): 281. http://dx.doi.org/10.1504/ijdmb.2017.10009312.

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39

Schwan, H. P. "Analysis of Dielectric Data: Experience Gained with Biological Materials." IEEE Transactions on Electrical Insulation EI-20, no. 6 (1985): 913–22. http://dx.doi.org/10.1109/tei.1985.348727.

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40

Cruz, António, Joel P. Arrais, and Penousal Machado. "Interactive and coordinated visualization approaches for biological data analysis." Briefings in Bioinformatics 20, no. 4 (2018): 1513–23. http://dx.doi.org/10.1093/bib/bby019.

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AbstractThe field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein–protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

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41

Keenan, Thomas P., and Stephen A. Krawetz. "Computer video acquisition and analysis system for biological data." Bioinformatics 4, no. 1 (1988): 203–10. http://dx.doi.org/10.1093/bioinformatics/4.1.203.

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42

Gaines, Steven D., and William R. Rice. "Analysis of Biological Data When there are Ordered Expectations." American Naturalist 135, no. 2 (1990): 310–17. http://dx.doi.org/10.1086/285047.

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43

Alt, Wolfgang. "Model-supported data analysis: some biological principles and examples." Journal of Mathematical Biology 61, no. 6 (2009): 899–903. http://dx.doi.org/10.1007/s00285-009-0310-7.

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44

Dokter, Adriaan M., Peter Desmet, Jurriaan H. Spaaks, et al. "bioRad: biological analysis and visualization of weather radar data." Ecography 42, no. 5 (2018): 852–60. http://dx.doi.org/10.1111/ecog.04028.

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45

Csuti, Blair, C. R. Margules, and M. P. Austin. "Nature Conservation: Cost Effective Biological Surveys and Data Analysis." Journal of Wildlife Management 56, no. 3 (1992): 621. http://dx.doi.org/10.2307/3808885.

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46

Rosenzweig, Cynthia. "Post IPCC AR4 biological and physical impact data analysis." IOP Conference Series: Earth and Environmental Science 6, no. 9 (2009): 092005. http://dx.doi.org/10.1088/1755-1307/6/9/092005.

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47

Vaidyanathan, Seetharaman, John S. Fletcher, Alex Henderson, Nicholas P. Lockyer, and John C. Vickerman. "Exploratory analysis of TOF-SIMS data from biological surfaces." Applied Surface Science 255, no. 4 (2008): 1599–602. http://dx.doi.org/10.1016/j.apsusc.2008.05.135.

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48

Tjärnhage, Torbjörn, Marianne Strömqvist, Göran Olofsson, et al. "Multivariate data analysis of fluorescence signals from biological aerosols." Field Analytical Chemistry & Technology 5, no. 4 (2001): 171–76. http://dx.doi.org/10.1002/fact.1018.

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49

Klein, Karsten, Oliver Koch, Nils Kriege, Petra Mutzel, and Till Schäfer. "Visual Analysis of Biological Activity Data with Scaffold Hunter." Molecular Informatics 32, no. 11-12 (2013): 964–75. http://dx.doi.org/10.1002/minf.201300087.

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50

Park, Ji-Won, Hyegeun Min, Young-Pil Kim, et al. "Multivariate analysis of ToF-SIMS data for biological applications." Surface and Interface Analysis 41, no. 8 (2009): 694–703. http://dx.doi.org/10.1002/sia.3049.

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