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Journal articles on the topic 'SILICO ANALYSIS'

Author: Grafiati

Published: 11 September 2023

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1

Barik, Dr Bibhuti Prasad. "In Silico Observations and Analysis of Metabolic Pathways." International Journal of Scientific Research 2, no. 11 (June 1, 2012): 44–48. http://dx.doi.org/10.15373/22778179/nov2013/14.

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2

Chaudhary, Hammad Tufail, and Shahida Hasnain. "IN-SILICO ANALYSIS." Professional Medical Journal 23, no. 02 (October 10, 2016): 217–22. http://dx.doi.org/10.29309/tpmj/2016.23.02.1074.

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ntroduction: Different pathogen reducing technologies are being implementedwhich includes S-303. CD-61 is important receptor for clotting. Pathogen reducing agents arebeing studied extensively to probe its effects. Objective: We conducted this study to reviewthe docking of S-303 at CD-61, to look into the effect of S-303 on function of platelets. StudyDesign: This was an observational study. Setting: In-silico study. Period: March 2015 toAugust 2015. Method: The study was carried out in-silico. PDB (Protein data bank) code ofTirofiban bound to CD-61 was 2vdm. CD-61 was docked with Tirofiban using online dockingtools i.e. Patchdock and Firedock. Then, S-303 and CD-61 were also docked. Best dockingposes to active sites of 2vdm were found. Interactions of ligands and CD-61 were obtained.Then comparison of Hydrogen Bonds, Hydrogen Bond Lengths, Hydrophobic bonds of 2vdmmolecule and best poses of docking results were done. Patchdock and Firdock results of bestposes were also analyzed using SPSS-16. Results: The Hydrogen bonds and Hydrogen bondlength and hydrophobic bonds of docking results were compared to 2vdm. 2 best poses wereobtained for docking of tirofiban to CD-61. No docking to active site was observed in Patchdockand firedock for S-303to CD-61. Conclusion: S-303 did not bind to the active site of CD-61. Wecan assume that S-303 doe

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3

Gunamalai, Lavanya, and C. Jaynthy C.Jaynthy. "In Silico Molecular Interaction Analysis Of Type I Collagen Telopeptides With Cyclodextrins." International Journal of Scientific Research 3, no. 8 (June 1, 2012): 25–27. http://dx.doi.org/10.15373/22778179/august2014/8.

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4

TADA, Yukio, and Mie TOGAWA. "Biomechanical Analysis of Hip Joint in Silico." Proceedings of Conference of Kansai Branch 2002.77 (2002): _4–45_—_4–46_. http://dx.doi.org/10.1299/jsmekansai.2002.77._4-45_.

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5

Joy, Amitha. "MLH1 gene: An in silico analysis." Journal of Computational Biology and Bioinformatics Research 5, no. 1 (April 30, 2013): 1–5. http://dx.doi.org/10.5897/jcbbr12.012.

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6

Barbosa, Aulus Estevão, and Paulo Marinho. "In silico analysis of Eucalyptus thioredoxins." Genetics and Molecular Biology 28, no. 3 suppl (2005): 539–47. http://dx.doi.org/10.1590/s1415-47572005000400008.

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7

Denomme, Gregory. "In Silico Analysis in Transfusion Medicine." Vox Sanguinis 83 (August 2002): 111–13. http://dx.doi.org/10.1111/j.1423-0410.2002.tb05280.x.

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8

Gunn, S., J. R. Hotchkiss, and P. Crooke. "In silico analysis of mechanical ventilation." Journal of Critical Care 21, no. 4 (December 2006): 358–59. http://dx.doi.org/10.1016/j.jcrc.2006.10.027.

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9

Florczuk, Patrycja, and Joanna Gruszczyńska. "GENETIC BACKGROUND OF CHONDRODYSPLASIA IN DOMESTIC DOG (CANIS LUPUS FAMILIARIS) – IN SILICO ANALYSIS." Acta Scientiarum Polonorum Zootechnica 15, no. 4 (January 10, 2017): 5–14. http://dx.doi.org/10.21005/asp.2016.15.4.01.

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10

KaplareviÄ‡, Milica, Marija GaÄiÄ‡, Georgia Karanasiou, Dimitris Fotiadis, and Nenad FilipoviÄ‡. "COST-EFFECTIVENESS ANALYSIS OF IN SILICO CLINICAL TRIALS OF VASCULAR STENTS." Journal of the Serbian Society for Computational Mechanics 16, no. 2 (December 1, 2022): 105–16. http://dx.doi.org/10.24874/jsscm.2022.16.02.08.

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Today, it takes ten to twelve years on average to complete a clinical trial before a new drug is approved and brought to marktet. Moreover, the evaluation of the efficacy and safety of drugs or devices has been performed in the linear and sequential manner with limited change over the past decade. The InSilc project is an EU funded project (www.insilc.eu) within which the InSilc platform was developed for designing, developing and assessing coronary stents. The InSilc platform contains the following modules: Mechanical Modelling Module, 3D Reconstruction and Plaque Characterization Tool, Deployment Module, Fluid Dynamics Module, Drug Delivery Module, Degradation Module, Myocardial Perfusion Module, Virtual Population Physiology and Virtual Population Database. We analyze the cost of three different in silico scenarios for clinical study. In Scenario 1, two different stent designs are compared according to the ISO standard for in silico mechanical tests. Scenario 2 predicts the stenting outcome for a virtual anatomy where design/material could be changed. Scenario 3 compares two stents using the same virtual anatomies from the Virtual vessel database. Cost-effectiveness analysis was performed for real clinical trials with metallic and BVS stent and in silico clinical trials. It was observed that in silico clinical trials are almost 90 times cheaper than real clinical trials for 1000 patients. In Silico clinical trials will not completely replace real clinical studies but the evidence shows that they can significantly reduce the cost of a real clinical study which will open a new avenue for future hybrid real and in silico clinical trials.

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11

Wang, Xiao, Haowen Jing, Maoliang Zhang, Jianwei Li, Yan Ma, and Liang Yan. "Analysis of Alkali in Bayer Red Mud: Content and Occurrence State in Different Structures." Sustainability 15, no. 17 (August 22, 2023): 12686. http://dx.doi.org/10.3390/su151712686.

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The application of large amounts of red mud in the field of building materials is one of the main ways to reuse this material, but the high alkali content of red mud limits its application. In this paper, the washable alkali, removable alkali, and lattice alkali contents of Bayer red mud were studied, and the occurrence states of potassium and sodium in red mud were studied using XRD, IR, XPS, and NMR. On this basis, the removal mechanism for potassium and sodium in red mud was analyzed. The results showed that the Na in the red mud was mainly deposited in the shelf silicon voids of hydroxy sodalite (Na8(AlSiO4)6(OH)2(H2O)2) in the form of Si-O-Na or Al-O-Na. K is deposited in the shelf silico-oxygen void of potassium feldspar (KAlSi3O8) in the form of Si-O-K or Al-O-K. The washable Na and K contents of the mud were 13.7% and 4.47%; the alkali removal agent CaO removed 83.1% and 50.8% of Na and K in the red mud; and the lattice alkali Na and K contents were 3.20% and 44.8%, respectively. In the process of red mud dealkalization, Ca2+ ions can enter the internal voids of the hydroxyl sodalite and potassium feldspar silica skeleton and then replace Al3+ in the Si-O skeleton and Na+ and K+ in the skeleton voids. The replacement reaction changes the silica tetrahedron network structure, resulting in the disintegration of the frame-like silica tetrahedron in the hydroxyl sodalite and potassium feldspar, forming an isolated, island-like silica tetrahedron in hydrated garnet.

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12

Sharanee Kumar, Ilakiya, Nurul Aniss Amran, and Kalaivani Nadarajah. "In silico Analysis of qBFR4 and qLBL5 in Conferring Quantitative Resistance Against Rice Blast." Journal of Pure and Applied Microbiology 12, no. 4 (December 30, 2018): 1703–18. http://dx.doi.org/10.22207/jpam.12.4.03.

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13

Kharor, Neeraj, Poonam Bansal, Raman Kumar, and Jasbir Singh. "In Silico Structure Analysis of Type 2 Diabetes Associated Toll Like Receptor-4 (Tlr4)." Journal of Advances and Scholarly Researches in Allied Education 15, no. 7 (September 1, 2018): 130–37. http://dx.doi.org/10.29070/15/57841.

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14

Usta, Mustafa, Abdullah Güller, and Hikmet Murat Sipahioglu. "Detection, in silico analysis and molecular diversity of phytoplasmas from solanaceous crops in Turkey." Plant Protection Science 58, No. 1 (December 17, 2021): 31–39. http://dx.doi.org/10.17221/115/2021-pps.

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Phytoplasma-like symptoms of leaf yellowing and calyx malformation were observed in eggplant (Solanum melongena L.), upward leaves and fruit malformation in pepper (Capsicum annuum L.), and aerial tuber formation in potato (S. tuberosum L.) during the survey performed in the late season (August to September) of 2015 and 2016 in Van province (Turkey). A total of 100 samples were tested by nested-PCR using universal primer pairs to assess the sanitary status of the solanaceous crops and to characterise the phytoplasma isolates. Among them, seven samples resulted in a 1.25 kb DNA fragment, and five (two eggplants, two peppers, and one potato) were molecularly characterised (Accession No.: KY579357, KT595210, MF564267, MF564266, and MH683601). BLAST and the virtual restriction fragment length polymorphism (RFLP) analysis of 16S rRNA genes revealed the presence of two distinct phytoplasma infections in solanaceous crops: ‘Candidatus Phytoplasma trifolii’ a member of the clover proliferation group (16SrVI) and subgroup A and ‘Candidatus P. solani’ a member of the stolbur group (16SrXII) and subgroup A. The virtual RFLP analysis and calculated coefficients of RFLP pattern similarities further revealed a remarkable genetic diversity among the ‘Candidatus P. solani’ isolates infecting pepper (similarity coefficient of 0.90) and eggplant (similarity coefficients of 0.98 and 1.00) at the same geographical area. This is the first report of the natural occurrence of ‘Candidadtus P. trifolii’ in potato from the Eastern Anatolia region, Turkey.

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15

Yadav, Usha, Sandeep Yadav, and Dinesh C. Sharma. "In Silico Analysis of Structural Photosynthetic Genes of Arabidopsis thaliana for Unique Mirror Repeats." Indian Journal of Science and Technology 15, no. 3 (January 17, 2022): 127–35. http://dx.doi.org/10.17485/ijst/v15i3.1833.

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16

kumari, Preeti, Brijesh Tripathi, Rashmi Rashmi, Ajay Narayan Gangopadhyay, Dr S. Shamal Dr. S. Shamal, Tribhuvan Mohan Mohapatra, and Dr Royana Singh. "In-silico modeling of EDNRB Protein Structure and its mutational analysis in Hirschsprung Disease." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 182–86. http://dx.doi.org/10.15373/22501991/apr2014/59.

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17

Tuteja, Renu. "In silico analysis ofPlasmodiumspecies specific UvrD helicase." Communicative & Integrative Biology 6, no. 2 (March 2013): e23125. http://dx.doi.org/10.4161/cib.23125.

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18

Subasi, Omer, Atacan Oral, Sinan Noyan, Orcun Tuncozgur, and Ismail Lazoglu. "In silico analysis of modular bone plates." Journal of the Mechanical Behavior of Biomedical Materials 124 (December 2021): 104847. http://dx.doi.org/10.1016/j.jmbbm.2021.104847.

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19

Pruess, Manuela, and Rolf Apweiler. "Bioinformatics Resources for In Silico Proteome Analysis." Journal of Biomedicine and Biotechnology 2003, no. 4 (2003): 231–36. http://dx.doi.org/10.1155/s1110724303209219.

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In the growing field of proteomics, tools for the in silico analysis of proteins and even of whole proteomes are of crucial importance to make best use of the accumulating amount of data. To utilise this data for healthcare and drug development, first the characteristics of proteomes of entire species—mainly the human—have to be understood, before secondly differentiation between individuals can be surveyed. Specialised databases about nucleic acid sequences, protein sequences, protein tertiary structure, genome analysis, and proteome analysis represent useful resources for analysis, characterisation, and classification of protein sequences. Different from most proteomics tools focusing on similarity searches, structure analysis and prediction, detection of specific regions, alignments, data mining, 2D PAGE analysis, or protein modelling, respectively, comprehensive databases like the proteome analysis database benefit from the information stored in different databases and make use of different protein analysis tools to provide computational analysis of whole proteomes.

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20

Baraniuk, James N., Peter McGarvey, Baris E. Suzek, Shruti Rao, Samir Lababidi, Andrea Sutherland, Richard Forshee, and Subha Madhavan. "In silico Analysis of Vaccination Adverse Events." Journal of Allergy and Clinical Immunology 135, no. 2 (February 2015): AB104. http://dx.doi.org/10.1016/j.jaci.2014.12.1271.

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21

Palanisamy, R., G. Pothikasalam, V. Kumaresan, P. Bhatt, A. Roy, J. Arockiaraj, and M. Pasupuleti. "In silico analysis of freshwater prawn transglutaminase." Fish & Shellfish Immunology 34, no. 6 (June 2013): 1727. http://dx.doi.org/10.1016/j.fsi.2013.03.279.

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22

Murray, David, Peter Doran, Padraic MacMathuna, and Alan C. Moss. "In silico gene expression analysis – an overview." Molecular Cancer 6, no. 1 (2007): 50. http://dx.doi.org/10.1186/1476-4598-6-50.

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23

Juan, Gloria. "In silico analysis of cell cycle progression." Cytometry Part A 85, no. 9 (June 17, 2014): 741–42. http://dx.doi.org/10.1002/cyto.a.22498.

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24

GENÇ, Tülay TURGUT, Ataberk ÇAKAN, and Melih GÜNAY. "CRABTREE POZİTİF VE CRABTREE NEGATİF MAYA TÜRLERİNDE GCR1 GENİNİN IN SILICO ANALİZİ." Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences 8, no. 17 (September 25, 2021): 207–16. http://dx.doi.org/10.38065/euroasiaorg.698.

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The use of fermentation in the presence of oxygen and at high glucose concentrations is referred to as the Crabtree effect. Yeast species that have the Crabtree effect are called Crabtree positive, and yeast species that do not have the Crabtree effect are called Crabtree negative. While Crabtree negative yeast strains are mostly used for heterologous protein production in the industrial field, Crabtree positive yeast strains are used to understand metabolic events in cancer cells. The genes encoding the enzymes involved in the glycolytic pathway in S. cerevisiae yeast cells are controlled by Gcr1p. Gcr1p binds to CT elements located in the promoter regions of glycolytic genes and activates their transcription. In our study, Crabtree positive and negative yeast strains containing Sc-Gcr1p similar proteins were determined, and protein similarity analyzes and promoter analyzes of genes encoding the relevant proteins in these yeast strains were compared in silico using different databases and analysis programs. For this purpose, SGD, UNIPROT, NCBI-Genome and Yeastract databases and BLASTp-NCBI, MEGA-X and Chromatin Folding V2 programs were used. Using the SGD database, 32 different yeast strains were identified that matched with Sc-Gcr1p. Five different Crabtree positive and 5 different Crabtree negative yeast strains were selected from these yeast strains and in silico analyzes were performed using these yeast strains. After protein analysis and promoter analysis, it was determined that the similarities and differences between yeast species were not specific for Crabtree positive and Crabtree negative yeast species, but varied between species.

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25

Allen, Timothy E., Markus J. Herrgård, Mingzhu Liu, Yu Qiu, Jeremy D. Glasner, Frederick R. Blattner, and Bernhard Ø. Palsson. "Genome-Scale Analysis of the Uses of the Escherichia coli Genome: Model-Driven Analysis of Heterogeneous Data Sets." Journal of Bacteriology 185, no. 21 (November 1, 2003): 6392–99. http://dx.doi.org/10.1128/jb.185.21.6392-6399.2003.

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ABSTRACT The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model.

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Montes-Olivas, Sandra, Danny Legge, Abbie Lund, Alexander G. Fletcher, Ann C. Williams, Lucia Marucci, and Martin Homer. "In-silico and in-vitro morphometric analysis of intestinal organoids." PLOS Computational Biology 19, no. 8 (August 14, 2023): e1011386. http://dx.doi.org/10.1371/journal.pcbi.1011386.

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Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.

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27

Nahar, Nusrat, Ridwan Bin Rashid, ANM Hamidul Kabir, and Mohammad Sharifur Rahman. "In Silico Virulence and Resistance Profile Analysis of Staphylococcus species." Bangladesh Pharmaceutical Journal 20, no. 1 (April 5, 2017): 71–84. http://dx.doi.org/10.3329/bpj.v20i1.32096.

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In silico studies of the genes of Staphylococcus spp. might establish some correlations with multiple pathological factors. Sixty isolates of Staphylococcus spp. have been studied here targeting virulence and antibiotic resistance genes through in silico tools. Here, in silico PCR (polymerase chain reaction) amplification detected both virulence and antibiotic resistance genes. Study revealed that most of the isolates harboured either cap5 (40%) or cap8 (31.67%) locus gene. Staphylococcal enterotoxin was detected in 63.33% of the isolates. The sea gene, responsible for food poisoning, was detected in 26.67% of the isolates. The tst positive isolates (5%), responsible for toxic shock syndrome, were present in only genotype 8. No exfoliative toxin was detected. The icaA gene, responsible for intracellular adherence, appeared in 80% of the isolates. Alpha hemolysin gene, hla, was detected in 63.33% of the isolates. Sixty-five percent of the isolates harboured the mecA genes. Both ?-lactamase (blaZ) and erythromycin resistance, ermA genes were available in 38.33% of the isolates. In silico pulsed field gel electrophoresis (PFGE) digestion was able to divide isolates into 23 genotypes. Genotype 8 and 11 harboured tetracycline resistance genes, tetM and tetK. The tetM gene (18.33%) was more prevalent than tetK gene (11.67%). Genotype 1 and 11 were considered more virulent than others. Genotype 11 also carried six antibiotic resistance genes but did not carry the genes msrA, msrB, ermB and ermC. The data generated here might aid in the prediction of the virulence and resistance profile based on genotyping as well as contribute in vaccine development.Bangladesh Pharmaceutical Journal 20(1): 71-84, 2017

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28

Kiryanova, O. Yu, I. I. Kiryanov, B. R. Kuluev, R. R. Garafutdinov, A. V. Chemeris, and I. M. Gubaydullin. "Multiplex in silico RAPD-Analysis for Genome Barcoding." Mathematical Biology and Bioinformatics 17, no. 2 (September 27, 2022): 208–29. http://dx.doi.org/10.17537/2022.17.208.

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In this work, we propose a new method for identifying organisms of multiplex polymerase chain reaction (PCR) with arbitrary primers in silico (multiplex in silico RAPD-analysis) for the unique identification of living organisms. The results of computer modeling search of possible primer annealing sites in genomic DNA, and their convertation into the genomic barcode format, are proposed. These data with information about used primers that can be unique for genomes. A comparative analysis of genomic barcodes of species of related plant species was carried out in order to classify them on the level of species and lines in the future. A pairwise analysis of the location of the same or similar amplicons within different subgenomes and genomes is presented. The genomes of wheat and Aegilops in FASTA files format are presented as the research samples. The proposed method makes possible to predict the success of the multiplex polymerase chain reaction using special primers in the laboratory. This technology allows the analysis of the entire genomic DNA, rather than fragments of the genome.

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29

Parenyuk, O. Yu, I. O. Simutin, D. O. Samofalova, Yu V. Ruban, V. V. Illienko, N. H. Nesterova, and I. M. Gudkov. "Approaches to in silico analysis of micobiome biodiversity metrics of radionuclide contaminated soils." Bìoresursi ì prirodokoristuvannâ 9, no. 5-6 (September 28, 2017): 10–16. http://dx.doi.org/10.31548/bio2017.05.002.

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30

Singh, Bharat, and Yasha Hasija. "Osteoporosis Gene Interactome: A comprehensive in silico analysis." Canadian Journal of Biotechnology 1, Special Issue (October 5, 2017): 44. http://dx.doi.org/10.24870/cjb.2017-a31.

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31

Tee, Sim-Hui, and Siew-Kien Mah. "In Silico Analysis of Oncogenes for Renal Cancer." International Journal on Advanced Science, Engineering and Information Technology 2, no. 5 (2012): 379. http://dx.doi.org/10.18517/ijaseit.2.5.228.

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32

Srivastava, Ankita, and A. A. Mahdi. "In Silico Analysis of Leishmania donovani Glycolytic Enzyme." Indian Journal of Scientific Research 12, no. 1 (August 31, 2021): 55. http://dx.doi.org/10.32606/ijsr.v12.i1.00010.

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Kadu, Nilesh S. "Pyrrolone Antimalarials: Pharmacophoric Analysis using In-Silico Techniques." International Journal for Research in Applied Science and Engineering Technology 7, no. 12 (December 31, 2019): 62–64. http://dx.doi.org/10.22214/ijraset.2019.12010.

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34

Parveen, Farzana, and Vineet Kumar Mishra. "In silico analysis of Myoglobin in Channa striata." Bioinformation 10, no. 1 (January 29, 2014): 19–22. http://dx.doi.org/10.6026/97320630010019.

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35

Liu, Ji. "In silico analysis ofS100gene expression in gastric cancer." World Chinese Journal of Digestology 23, no. 14 (2015): 2208. http://dx.doi.org/10.11569/wcjd.v23.i14.2208.

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36

Bortoluzzi, Stefania, and Gian Antonio Danieli. "Towards an in silico analysis of transcription patterns." Trends in Genetics 15, no. 3 (March 1999): 118–19. http://dx.doi.org/10.1016/s0168-9525(98)01682-5.

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37

Rajendran, Vasanthi, Sameer Hassan, Jerrine Joseph, Nagamiah Selvakumar, and Vanaja Kumar. "In silico sequence and structure analysis for mycobacteriophages." Asian Pacific Journal of Tropical Biomedicine 2, no. 1 (January 2012): S377—S379. http://dx.doi.org/10.1016/s2221-1691(12)60191-9.

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38

Chou, Chih-Fong, and Miwako Ozaki. "In silico analysis of NRG1 evolution in vertebrates." Neuroscience Research 65 (January 2009): S89. http://dx.doi.org/10.1016/j.neures.2009.09.361.

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39

Yousef, Fátima, and Manuel Espinosa-Urgel. "In silico analysis of large microbial surface proteins." Research in Microbiology 158, no. 6 (July 2007): 545–50. http://dx.doi.org/10.1016/j.resmic.2007.04.006.

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40

Rai, Pratishtha, Allyn Howlett, and Sudha M. Cowsik. "Structural Analysis of CRIP1a by in Silico Approaches." Biophysical Journal 106, no. 2 (January 2014): 208a. http://dx.doi.org/10.1016/j.bpj.2013.11.1219.

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41

Kalmykova, S. D., G. P. Arapidi, A. S. Urban, M. S. Osetrova, V. D. Gordeeva, V. T. Ivanov, and V. M. Govorun. "In Silico Analysis of Peptide Potential Biological Functions." Russian Journal of Bioorganic Chemistry 44, no. 4 (July 2018): 367–85. http://dx.doi.org/10.1134/s106816201804009x.

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42

Li, Danyan. "In silico analysis of a lung cancer vaccine." Biophysical Journal 122, no. 3 (February 2023): 285a—286a. http://dx.doi.org/10.1016/j.bpj.2022.11.1622.

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43

Tekcan, Akin. "In Silico Analysis of FMR1 Gene Missense SNPs." Cell Biochemistry and Biophysics 74, no. 2 (February 15, 2016): 109–27. http://dx.doi.org/10.1007/s12013-016-0722-0.

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44

Chen, S. H., C. Y. Lin, and C. M. Kuo. "In Silico Analysis of Crustacean Hyperglycemic Hormone Family." Marine Biotechnology 7, no. 3 (June 2005): 193–206. http://dx.doi.org/10.1007/s10126-004-0020-5.

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Malik, Adeel, and Seung Bum Kim. "A comprehensive in silico analysis of sortase superfamily." Journal of Microbiology 57, no. 6 (May 27, 2019): 431–43. http://dx.doi.org/10.1007/s12275-019-8545-5.

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Maliar, Tibor, Mária Maliarová, Andrea Purdešová, Timotej Jankech, Ivana Gerhardtová, Patrik Beňovič, Václav Dvořáček, Michal Jágr, and Jana Viskupičová. "The Adapted POM Analysis of Avenanthramides In Silico." Pharmaceuticals 16, no. 5 (May 9, 2023): 717. http://dx.doi.org/10.3390/ph16050717.

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POM analysis and related approaches are significant tools based on calculating various physico-chemical properties and predicting biological activity, ADME parameters, and toxicity of a molecule. These methods are used to evaluate a molecule’s potential to become a drug candidate. Avenanthramides (AVNs) are promising secondary metabolites specific to Avena spp. (oat). They comprise the amides of anthranilic acid linked to various polyphenolic acids with or without post-condensation molecule transformation. These natural compounds have been reported to exert numerous biological effects, including antioxidant, anti-inflammatory, hepatoprotective, antiatherogenic, and antiproliferative properties. To date, almost 50 various AVNs have been identified. We performed a modified POM analysis of 42 AVNs using MOLINSPIRATION, SWISSADME, and OSIRIS software. The evaluation of primary in silico parameters revealed significant differences among individual AVNs, highlighting the most promising candidates. These preliminary results may help coordinate and initiate other research projects focused on particular AVNs, especially those with predicted bioactivity, low toxicity, optimal ADME parameters, and promising perspectives.

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DIN, KHUZMA, AMIZA MAT AMIN, FISAL AHMAD, AMIN ISMAIL, and ADAWIYAH SURIZA SHUIB. "IN SILICO ANALYSIS OF EDIBLE BIRD’S NEST PROTEINS AS POTENTIAL PRECURSORS FOR BIOACTIVE PEPTIDES." Malaysian Applied Biology 51, no. 2 (June 29, 2022): 53–62. http://dx.doi.org/10.55230/mabjournal.v51i2.1997.

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The present study aimed to perform an in silico evaluation of edible bird’s nest protein as potential precursors of bioactive peptides, as well as to determine whether such peptides can be released by selected proteolytic enzymes. Six edible bird’s nest (EBN) protein sequences from a previous study were chosen as potential precursors to produce bioactive peptides via in silico method using the BIOPEP database. AMCase protein sequences gave the highest number of bioactivities (16 to 18) and nucleobindin-2 protein gave the lowest number of bioactivities (9) among the other protein sequences. It was found that the most potential bioactive peptides from EBN proteins are angiotensin-converting enzyme (ACE) inhibitors and dipeptidyl peptidase-IV (DPPIV) inhibitors. Furthermore, in silico proteolysis using six selected enzymes was employed to release both dominant bioactivities in EBN proteins, which were ACE and DPP-IV inhibitors. This study shows that a combination of enzymes, chymotrypsin, and papain, produced the highest number of activities for both ACE and DPP-IV inhibitor peptides with the frequency of occurrence of bioactive peptides of 0.0968 and 0.1104, respectively. The toxic prediction tool, ToxinPred, found that all EBN peptides derived by in silico analysis were non-toxic. The current study proposed that EBN can serve as a potential source of bioactive peptides.

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Ekawasti, F., U. Cahyaningsih, S. Sadi’ah, N. L. P. I. Dharmayanti, and D. T. Subekti. "In silico restriction site analysis for characterization of Toxoplasma gondii isolate." IOP Conference Series: Earth and Environmental Science 976, no. 1 (February 1, 2022): 012001. http://dx.doi.org/10.1088/1755-1315/976/1/012001.

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Abstract Toxoplasma gondii infection is a serious major public health concern. Toxoplasmosis is a disease that affects humans and warm-blooded animals all over the world. The virulence and severity of the sickness may be influenced by parasite load. The three clonal lineages for biological research are T. gondii genotypes I, II, and III. Using primer genes for gra1, rop1, and mic3, which have been identified as essential proteins for tachyzoite invasion and replication in host cells. PCR was used to determine the genotype of a T. gondii isolate from the Indonesian research center for veterinary science. In silico restriction site analysis was performed on T. gondii isolates using CLC sequence viewer 8.0 software to assess sequence data for the existence of restriction enzyme patterns. Predictable restriction fragment length polymorphism using In silico analysis. T. gondii sequencing genes are In silico digested with different restriction enzymes. The findings show that they can easily distinguish archetypal parasites from biotypes and estimate the genetic diversity of the parasites. According to the interpretation of the data, isolate T. gondii belongs to strain RH genotype I.

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Maseko, Nomaswazi N., Emma T. Steenkamp, Brenda D. Wingfield, and P. Markus Wilken. "An in Silico Approach to Identifying TF Binding Sites: Analysis of the Regulatory Regions of BUSCO Genes from Fungal Species in the Ceratocystidaceae Family." Genes 14, no. 4 (March 31, 2023): 848. http://dx.doi.org/10.3390/genes14040848.

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Transcriptional regulation controls gene expression through regulatory promoter regions that contain conserved sequence motifs. These motifs, also known as regulatory elements, are critically important to expression, which is driving research efforts to identify and characterize them. Yeasts have been the focus of such studies in fungi, including in several in silico approaches. This study aimed to determine whether in silico approaches could be used to identify motifs in the Ceratocystidaceae family, and if present, to evaluate whether these correspond to known transcription factors. This study targeted the 1000 base-pair region upstream of the start codon of 20 single-copy genes from the BUSCO dataset for motif discovery. Using the MEME and Tomtom analysis tools, conserved motifs at the family level were identified. The results show that such in silico approaches could identify known regulatory motifs in the Ceratocystidaceae and other unrelated species. This study provides support to ongoing efforts to use in silico analyses for motif discovery.

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SIMON, V., M. TODEA, and S. SIMON. "THERMAL INVESTIGATION OF SiO2-Bi2O3 HEAVY METAL GLASSES." International Journal of Modern Physics B 19, no. 20 (August 10, 2005): 3293–99. http://dx.doi.org/10.1142/s0217979205032073.

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Structural changes induced by temperature rising in binary silico-bismuthate glasses are investigated by differential thermal analysis. Several exothermic peaks are recorded for all samples. Progressive substitution of Bi 2 O 3 by SiO 2 contributes to the structural relaxation of vitreous network and leads to diminishing of the melting temperature, even as SiO 2 content reaches 40 mol%. Glass stability is enhanced by addition of silicon dioxide.

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