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Journal articles on the topic 'Biomolecular Visualization'

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1

DOI, Junta. "Biomolecular Visualization." Journal of the Visualization Society of Japan 10, no. 39 (1990): 222–27. http://dx.doi.org/10.3154/jvs.10.222.

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2

Duncan, Bruce S., Tom J. Macke, and Arthur J. Olson. "Biomolecular visualization using AVS." Journal of Molecular Graphics 13, no. 5 (1995): 271–82. http://dx.doi.org/10.1016/0263-7855(95)00067-4.

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3

Song, Cheng Long, Chen Zou, Wen Ke Wang, and Si Kun Li. "An Integrated Framework for Biological Data Visualization." Advanced Materials Research 846-847 (November 2013): 1145–48. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1145.

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In the field of bioinformatics visualization, integrating software and data in different levels is the development trend. This paper presents an integration framework for biomolecular structure and genome sequences visualization. The framework can effectively support the data and software interoperability of biomolecular structure / genome sequences visualization. Based on the framework, we developed an integrated visualization system, which provides some new comprehensive visualization functions. Preliminary trial showed that the framework has a good prospect in the research of bioinformatics
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Perlasca, Paolo, Marco Frasca, Cheick Tidiane Ba, et al. "Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools." PLOS ONE 15, no. 12 (2020): e0244241. http://dx.doi.org/10.1371/journal.pone.0244241.

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The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that e
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Xie, Jiang, Zhonghua Zhou, Kai Lu, Luonan Chen, and Wu Zhang. "Visualization of biomolecular networks' comparison on cytoscape." Tsinghua Science and Technology 18, no. 5 (2013): 515——521. http://dx.doi.org/10.1109/tst.2013.6616524.

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6

He, Weiwei, Yen-Lin Chen, Serdal Kirmizialtin, and Lois Pollack. "Visualization of biomolecular structures by WAXS and MD." Acta Crystallographica Section A Foundations and Advances 77, a1 (2021): a124. http://dx.doi.org/10.1107/s0108767321098755.

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7

Yi Ronggui, Xie Jiang, Zhang Huiran, Zhang Wu, and Shigeo Kawata. "BNVC: A Web-Oriented Biomolecular Network Visualization Platform." Journal of Next Generation Information Technology 4, no. 3 (2013): 151–59. http://dx.doi.org/10.4156/jnit.vol4.issue3.18.

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8

Kozlíková, B., M. Krone, M. Falk, et al. "Visualization of Biomolecular Structures: State of the Art Revisited." Computer Graphics Forum 36, no. 8 (2016): 178–204. http://dx.doi.org/10.1111/cgf.13072.

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9

Ando, Toshio, Takayuki Uchihashi, Noriyuki Kodera, et al. "High-speed AFM and nano-visualization of biomolecular processes." Pflügers Archiv - European Journal of Physiology 456, no. 1 (2007): 211–25. http://dx.doi.org/10.1007/s00424-007-0406-0.

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10

You, Qian, Shiaofen Fang, and Jake Yue Chen. "Gene Terrain: Visual Exploration of Differential Gene Expression Profiles Organized in Native Biomolecular Interaction Networks." Information Visualization 9, no. 1 (2008): 1–12. http://dx.doi.org/10.1057/ivs.2008.3.

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We propose a new network visualization technique using scattered data interpolation and surface rendering, based upon a foundation layout of a scalar field. Contours of the interpolated surfaces are generated to support multi-scale visual interaction for data exploration. Our framework visualizes quantitative attributes of nodes in a network as a continuous surface by interpolating the scalar field, therefore avoiding scalability issues typical in conventional network visualizations while also maintaining the topological properties of the original network. We applied this technique to the stud
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Zhang, Hui Ran, Xiao Long Shen, Jiang Xie, and Dong Bo Dai. "A Web-Based Tool for Visualization of Biomolecular Network Comparison." Applied Mechanics and Materials 556-562 (May 2014): 5482–87. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5482.

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Analyzing similarities and differences between biomolecular networks comparison through website intuitively could be a convenient and effective way for researchers. Although several network comparison visualization tools have been developed, none of them can be integrated into websites. In this paper, a web-based tool kit named dynamically adaptive Visualization of Biomolecular Network Comparison (Bio-NCV) is designed and developed. The proposed tool is based on Cytyoscape.js – a popular open-source library for analyzing and visualizing networks. Bio-NCV integrates arjor.js which including the
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12

Lyubchenko, Yuri L. "Direct AFM visualization of the nanoscale dynamics of biomolecular complexes." Journal of Physics D: Applied Physics 51, no. 40 (2018): 403001. http://dx.doi.org/10.1088/1361-6463/aad898.

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13

Bocu, Razvan. "Dynamic Monitoring of Time-Dependent Evolution of Biomolecules Using Quantum Dots-Based Biosensors Assemblies." Biosensors 14, no. 8 (2024): 380. http://dx.doi.org/10.3390/bios14080380.

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The dynamic monitoring of biomolecules that are part of cell membranes generally constitutes a challenge. Electrochemiluminescence (ECL) biosensor assemblies provide clear advantages concerning microscopic imaging. Therefore, this paper proposes and analyzes a quantum dots-based biosensor assembly. Thus, particular attention is granted to biomolecules that are part of cell membranes. Additionally, this paper describes and analyzes a quantum dots-based biosensor assembly, which is used to implement a fully functional color ECL visualization system that allows for cellular and biomolecular struc
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14

Haiying Wang, F. Azuaje, and N. Black. "Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks." IEEE Transactions on Nanobioscience 1, no. 4 (2002): 146–66. http://dx.doi.org/10.1109/tnb.2003.809465.

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15

Wang, Quan, and W. E. Moerner. "Single-molecule motions enable direct visualization of biomolecular interactions in solution." Nature Methods 11, no. 5 (2014): 555–58. http://dx.doi.org/10.1038/nmeth.2882.

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16

Segura, Joan, Yana Rose, Sebastian Bittrich, Chunxiao Bi, Jose Duarte, and Stephen K. Burley. "BPS2025 - Enhancing exploration and visualization of biomolecular 3D information at RCSB.org." Biophysical Journal 124, no. 3 (2025): 642a. https://doi.org/10.1016/j.bpj.2024.11.3300.

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Kokkinopoulou, Maria, Johanna Simon, Katharina Landfester, Volker Mailänder, and Ingo Lieberwirth. "Visualization of the protein corona: towards a biomolecular understanding of nanoparticle-cell-interactions." Nanoscale 9, no. 25 (2017): 8858–70. http://dx.doi.org/10.1039/c7nr02977b.

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18

Beckham, Josh T., Daniel R. Dries, Bonnie L. Hall, et al. "Seeing Eye to Eye? Comparing Faculty and Student Perceptions of Biomolecular Visualization Assessments." Education Sciences 14, no. 1 (2024): 94. http://dx.doi.org/10.3390/educsci14010094.

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While visual literacy has been identified as a foundational skill in life science education, there are many challenges in teaching and assessing biomolecular visualization skills. Among these are the lack of consensus about what constitutes competence and limited understanding of student and instructor perceptions of visual literacy tasks. In this study, we administered a set of biomolecular visualization assessments, developed as part of the BioMolViz project, to both students and instructors at multiple institutions and compared their perceptions of task difficulty. We then analyzed our find
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19

Stone, John E., Ryan McGreevy, Barry Isralewitz, and Klaus Schulten. "GPU-accelerated analysis and visualization of large structures solved by molecular dynamics flexible fitting." Faraday Discuss. 169 (2014): 265–83. http://dx.doi.org/10.1039/c4fd00005f.

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Hybrid structure fitting methods combine data from cryo-electron microscopy and X-ray crystallography with molecular dynamics simulations for the determination of all-atom structures of large biomolecular complexes. Evaluating the quality-of-fit obtained from hybrid fitting is computationally demanding, particularly in the context of a multiplicity of structural conformations that must be evaluated. Existing tools for quality-of-fit analysis and visualization have previously targeted small structures and are too slow to be used interactively for large biomolecular complexes of particular inter
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20

Sehnal, David, Sebastian Bittrich, Mandar Deshpande, et al. "Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures." Nucleic Acids Research 49, W1 (2021): W431—W437. http://dx.doi.org/10.1093/nar/gkab314.

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Abstract Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomp
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21

Wang, Lincong, Hui Qiao, Chen Cao, Shutan Xu, and Shuxue Zou. "An Accurate Model for Biomolecular Helices and Its Application to Helix Visualization." PLOS ONE 10, no. 6 (2015): e0129653. http://dx.doi.org/10.1371/journal.pone.0129653.

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22

Ando, Toshio, Takayuki Uchihashi, and Takeshi Fukuma. "High-speed atomic force microscopy for nano-visualization of dynamic biomolecular processes." Progress in Surface Science 83, no. 7-9 (2008): 337–437. http://dx.doi.org/10.1016/j.progsurf.2008.09.001.

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23

Jin, Gang, Pentti Tengvall, Ingemar Lundström, and Hans Arwin. "A Biosensor Concept Based on Imaging Ellipsometry for Visualization of Biomolecular Interactions." Analytical Biochemistry 232, no. 1 (1995): 69–72. http://dx.doi.org/10.1006/abio.1995.9959.

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24

Barreto Gomes, Diego E., Shao-Chian Chen, John E. Stone, Emad Tajkhorshid, and Rafael C. Bernardi. "BPS2025 - Enhancing molecular visualization, dynamics, and structural analysis tools for biomolecular studies." Biophysical Journal 124, no. 3 (2025): 316a. https://doi.org/10.1016/j.bpj.2024.11.1750.

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25

Burch, Charmita, Josh Beckham, Kristen Procko, et al. "Abstract 1286 Voicing Visual Literacy: Student Interviews to Improve Biomolecular Visualization Assessments." Journal of Biological Chemistry 301, no. 5 (2025): 108576. https://doi.org/10.1016/j.jbc.2025.108576.

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26

R. Shaw, Olivia, and Jodi A. Hadden-Perilla. "TactViz: A VMD Plugin for Tactile Visualization of Protein Structures." Journal of Science Education for Students with Disabilities 23, no. 1 (2020): 1–8. http://dx.doi.org/10.14448/jsesd.12.0015.

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Scientific disciplines spanning biology, biochemistry, and biophysics involve the study of proteins and their functions. Visualization of protein structures represents a barrier to education and research in these disciplines for students who are blind or visually impaired. Here, we present a software plugin for readily producing variable-height tactile graphics of proteins using the free biomolecular visualization software Visual Molecular Dynamics (VMD) and protein structure data that is publicly available through the Protein Data Bank. Our method also supports interactive tactile visualizati
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27

Bayarri, Genís, Pau Andrio, Josep Lluís Gelpí, Adam Hospital, and Modesto Orozco. "Using interactive Jupyter Notebooks and BioConda for FAIR and reproducible biomolecular simulation workflows." PLOS Computational Biology 20, no. 6 (2024): e1012173. http://dx.doi.org/10.1371/journal.pcbi.1012173.

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Interactive Jupyter Notebooks in combination with Conda environments can be used to generate FAIR (Findable, Accessible, Interoperable and Reusable/Reproducible) biomolecular simulation workflows. The interactive programming code accompanied by documentation and the possibility to inspect intermediate results with versatile graphical charts and data visualization is very helpful, especially in iterative processes, where parameters might be adjusted to a particular system of interest. This work presents a collection of FAIR notebooks covering various areas of the biomolecular simulation field,
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28

Haghizadeh, Anahita, Mariam Iftikhar, Shiba S. Dandpat, and Trey Simpson. "Looking at Biomolecular Interactions through the Lens of Correlated Fluorescence Microscopy and Optical Tweezers." International Journal of Molecular Sciences 24, no. 3 (2023): 2668. http://dx.doi.org/10.3390/ijms24032668.

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Understanding complex biological events at the molecular level paves the path to determine mechanistic processes across the timescale necessary for breakthrough discoveries. While various conventional biophysical methods provide some information for understanding biological systems, they often lack a complete picture of the molecular-level details of such dynamic processes. Studies at the single-molecule level have emerged to provide crucial missing links to understanding complex and dynamic pathways in biological systems, which are often superseded by bulk biophysical and biochemical studies.
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29

Walter, Peter, Sam Ansari, and Volkhard Helms. "The ABC (Analysing Biomolecular Contacts)-database." Journal of Integrative Bioinformatics 4, no. 1 (2007): 31–39. http://dx.doi.org/10.1515/jib-2007-50.

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Abstract As protein-protein interactions are one of the basic mechanisms in most cellular processes, it is desirable to understand the molecular details of protein-protein contacts and ultimately be able to predict which proteins interact. Interface areas on a protein surface that are involved in protein interactions exhibit certain characteristics. Therefore, several attempts were made to distinguish protein interactions from each other and to categorize them. One way of classification are the groups of transient and permanent interactions. Previously two of the authors analysed several prope
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30

Schlick, Tamar. "Engineering Teams Up with Computer-Simulation and Visualization Tools to Probe Biomolecular Mechanisms." Biophysical Journal 85, no. 1 (2003): 1–4. http://dx.doi.org/10.1016/s0006-3495(03)74448-8.

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31

Dries, Daniel R., Diane M. Dean, Laura L. Listenberger, Walter R. P. Novak, Margaret A. Franzen, and Paul A. Craig. "An expanded framework for biomolecular visualization in the classroom: Learning goals and competencies." Biochemistry and Molecular Biology Education 45, no. 1 (2016): 69–75. http://dx.doi.org/10.1002/bmb.20991.

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32

Kurosu, Jun, Kaname Kanai, and Jun’ya Tsutsumi. "Label-free visualization of nano-thick biomolecular binding by electric-double-layer modulation." Sensors and Actuators B: Chemical 382 (May 2023): 133548. http://dx.doi.org/10.1016/j.snb.2023.133548.

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Mitton-Fry, Rachel, Josh Beckham, Roderico Acevedo, et al. "Abstract 1211 Adapting Project Management Strategies to Build a Biomolecular Visualization Assessment Repository." Journal of Biological Chemistry 301, no. 5 (2025): 108574. https://doi.org/10.1016/j.jbc.2025.108574.

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Peterson, Celeste, Kristen “KP” Procko, Josh Beckham, Shelly Engelman, and Melanie Berkmen. "Abstract 2652 Opportunities and challenges in adopting virtual reality for teaching biomolecular visualization." Journal of Biological Chemistry 301, no. 5 (2025): 108610. https://doi.org/10.1016/j.jbc.2025.108610.

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Amyot, Romain, Noriyuki Kodera, and Holger Flechsig. "Atom Filtering Algorithm and GPU-Accelerated Calculation of Simulation Atomic Force Microscopy Images." Algorithms 17, no. 1 (2024): 38. http://dx.doi.org/10.3390/a17010038.

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Simulation of atomic force microscopy (AFM) computationally emulates experimental scanning of a biomolecular structure to produce topographic images that can be correlated with measured images. Its application to the enormous amount of available high-resolution structures, as well as to molecular dynamics modelling data, facilitates the quantitative interpretation of experimental observations by inferring atomistic information from resolution-limited measured topographies. The computation required to generate a simulated AFM image generally includes the calculation of contacts between the scan
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Vymětal, Jiří, David Jakubec, Jakub Galgonek, and Jiří Vondrášek. "Amino Acid Interactions (INTAA) web server v2.0: a single service for computation of energetics and conservation in biomolecular 3D structures." Nucleic Acids Research 49, W1 (2021): W15—W20. http://dx.doi.org/10.1093/nar/gkab377.

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Abstract Interactions among amino acid residues are the principal contributor to the stability of the three-dimensional structure of a protein. The Amino Acid Interactions (INTAA) web server (https://bioinfo.uochb.cas.cz/INTAA/) has established itself as a unique computational resource, which enables users to calculate the contribution of individual residues in a biomolecular structure to its total energy using a molecular mechanical scoring function. In this update, we describe major additions to the web server which help solidify its position as a robust, comprehensive resource for biomolecu
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37

Le, Kathy H., Jared Adolf-Bryfogle, Jason C. Klima, et al. "PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design." Biophysicist 2, no. 1 (2021): 108–22. http://dx.doi.org/10.35459/tbp.2019.000147.

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ABSTRACT Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of 16 modules that teach topics in biomolecular structure
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38

Wu, Zhaolong, Enbo Chen, Shuwen Zhang, Yinping Ma, and Youdong Mao. "Visualizing Conformational Space of Functional Biomolecular Complexes by Deep Manifold Learning." International Journal of Molecular Sciences 23, no. 16 (2022): 8872. http://dx.doi.org/10.3390/ijms23168872.

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The cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving the conformational continuum of important biomolecular complexes at the atomic level is essential to understanding their functional mechanisms and guiding structure-based drug discovery. Here, we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions that approximately visualize the conformational space of biomolecular complexes
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Nemoto, Tomomi. "Visualization and Analysis of Cellular and Biomolecular Dynamics by using Ultra-Short Pulse Laser." Nippon Laser Igakkaishi 30, no. 4 (2009): 435–40. http://dx.doi.org/10.2530/jslsm.30.435.

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Baroroh, S.Si., M.Biotek., Umi, Zahra Silmi Muscifa, Wanda Destiarani, Fauzian Giansyah Rohmatullah, and Muhammad Yusuf. "Molecular interaction analysis and visualization of protein-ligand docking using Biovia Discovery Studio Visualizer." Indonesian Journal of Computational Biology (IJCB) 2, no. 1 (2023): 22. http://dx.doi.org/10.24198/ijcb.v2i1.46322.

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Molecular docking interpretation is one of the crucial part before determining the result. Docking is commonly used to study the biomolecular interaction, usually for protein-ligand interaction, and to study about the molecular mechanism. Analysis of molecular interaction can help user to determine the strengthened of docking results, besides free energy of binding. In this protocol, analysis of molecular interaction as well as the surface characteristic of receptor was discussed in detail. In addition, the visualization to obtain suitable pictures for publications also included. The entire pr
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41

Botello-Smith, Wesley M., Qin Cai, and Ray Luo. "Biological applications of classical electrostatics methods." Journal of Theoretical and Computational Chemistry 13, no. 03 (2014): 1440008. http://dx.doi.org/10.1142/s0219633614400082.

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Continuum electrostatics modeling of solvation based on the Poisson–Boltzmann (PB) equation has gained wide acceptance in biomolecular applications such as energetic analysis and structural visualization. Successful application of the PB solvent models requires careful calibration of the solvation parameters. Extensive testing and validation is also important to ensure accuracy in their applications. Limitation in the continuum modeling of solvation is also a known issue in certain biomolecular applications. Growing interest in membrane systems has further spurred developmental efforts to allo
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42

Amyot, Romain, and Holger Flechsig. "BioAFMviewer: An interactive interface for simulated AFM scanning of biomolecular structures and dynamics." PLOS Computational Biology 16, no. 11 (2020): e1008444. http://dx.doi.org/10.1371/journal.pcbi.1008444.

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We provide a stand-alone software, the BioAFMviewer, which transforms biomolecular structures into the graphical representation corresponding to the outcome of atomic force microscopy (AFM) experiments. The AFM graphics is obtained by performing simulated scanning over the molecular structure encoded in the corresponding PDB file. A versatile molecular viewer integrates the visualization of PDB structures and control over their orientation, while synchronized simulated scanning with variable spatial resolution and tip-shape geometry produces the corresponding AFM graphics. We demonstrate the a
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43

Heylen, Dries, Jannes Peeters, Jan Aerts, Gökhan Ertaylan, and Jef Hooyberghs. "BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation." PLOS ONE 18, no. 12 (2023): e0295361. http://dx.doi.org/10.1371/journal.pone.0295361.

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One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisat
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Gaur, Pankaj, Ajay Kumar, Shalmoli Bhattacharyya, and Subrata Ghosh. "Biomolecular recognition at the cellular level: geometrical and chemical functionality dependence of a low phototoxic cationic probe for DNA imaging." Journal of Materials Chemistry B 4, no. 28 (2016): 4895–900. http://dx.doi.org/10.1039/c6tb00787b.

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Uchihashi, Takayuki, and Simon Scheuring. "Applications of high-speed atomic force microscopy to real-time visualization of dynamic biomolecular processes." Biochimica et Biophysica Acta (BBA) - General Subjects 1862, no. 2 (2018): 229–40. http://dx.doi.org/10.1016/j.bbagen.2017.07.010.

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46

Nersisyan, Lilit, Ruben Samsonyan, and Arsen Arakelyan. "CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows." F1000Research 3 (August 14, 2014): 145. http://dx.doi.org/10.12688/f1000research.4410.2.

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The KEGG pathway database is a widely accepted source for biomolecular pathway maps. In this paper we present the CyKEGGParser app (http://apps.cytoscape.org/apps/cykeggparser) for Cytoscape 3 that allows manipulation with KEGG pathway maps. Along with basic functionalities for pathway retrieval, visualization and export in KGML and BioPAX formats, the app provides unique features for computer-assisted adjustment of inconsistencies in KEGG pathway KGML files and generation of tissue- and protein-protein interaction specific pathways. We demonstrate that using biological context-specific KEGG p
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Gopal, Sahana, Ciro Chiappini, James P. K. Armstrong, et al. "Immunogold FIB-SEM: Combining Volumetric Ultrastructure Visualization with 3D Biomolecular Analysis to Dissect Cell-Environment Interactions." Advanced Materials 31, no. 32 (2019): 1900488. http://dx.doi.org/10.1002/adma.201900488.

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48

Beckham, Josh, Pamela Mertz, Roderico Acevedo, et al. "Abstract 1094 Validation of Biomolecular Visualization Assessments through Large Scale Field Testing and Student Focus Groups." Journal of Biological Chemistry 300, no. 3 (2024): 105896. http://dx.doi.org/10.1016/j.jbc.2024.105896.

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Procko, Kristen, Josh Beckham, Bonnie Hall, et al. "Abstract 1089 A Tale of Two Perspectives: Comparing Student and Faculty Perceptions of Biomolecular Visualization Assessment." Journal of Biological Chemistry 300, no. 3 (2024): 105895. http://dx.doi.org/10.1016/j.jbc.2024.105895.

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50

Satya Sri Pulla, Sai, Vennela Chowdary Gullapalli, Pothuri Vishnu, et al. "COMPUTATIONAL VALIDATION AND ANALYSIS OF SEMI-QUANTITATIVE DATA USING IN-SILICO APPROACHES." International Journal of Advanced Research 11, no. 11 (2023): 1060–64. http://dx.doi.org/10.21474/ijar01/17910.

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With the advent use of computers in every daily life the computational approacheshas collaborative effort between biologists and computer scientists and thus covers a wide variety of traditionalcomputer science domains, including data retrieval, data integration,data cleaning, data modeling, data mining, data warehousing, data managing, ontologies, simulation, parallel computing, agent-based technology, grid computing, and visualization. However, applying each of these domains to biomolecular and biomedicalapplications raises specific and unexpectedly challenging research issues. This review i
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