Journal articles: 'Bioinformatics approach'

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Grant, Gregory. "Bioinformatics — The Machine Learning Approach." Computers & Chemistry 24, no. 1 (2000): 139–41. http://dx.doi.org/10.1016/s0097-8485(00)80015-7.

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Leunissen, J. "Bioinformatics: The Machine Learning Approach." Briefings in Bioinformatics 3, no. 3 (2002): 321–23. http://dx.doi.org/10.1093/bib/3.3.321.

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Radivojac, P. "Protein Structure Prediction: Bioinformatics Approach." Briefings in Bioinformatics 5, no. 2 (2004): 207–9. http://dx.doi.org/10.1093/bib/5.2.207.

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Gough, N. R. "A Bioinformatics Approach to Addiction." Science Signaling 1, no. 5 (2008): ec43-ec43. http://dx.doi.org/10.1126/stke.15ec43.

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Lempicki, Richard. "045 Bioinformatics approach in cryopreservation." Cryobiology 67, no. 3 (2013): 411. http://dx.doi.org/10.1016/j.cryobiol.2013.09.051.

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Ong, Quang, Phuc Nguyen, Nguyen Phuong Thao, and Ly Le. "Bioinformatics Approach in Plant Genomic Research." Current Genomics 17, no. 4 (2016): 368–78. http://dx.doi.org/10.2174/1389202917666160331202956.

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Garhwal, Abhimanyu Singh, and Wei Qi Yan. "BIIGA: Bioinformatics inspired image grouping approach." Multimedia Tools and Applications 78, no. 11 (2018): 14355–77. http://dx.doi.org/10.1007/s11042-018-6817-4.

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Anurogo, Dito, and Arli Aditya Parikesit. "Bioinformatics Approach towards Transcriptomics of Filaggrin." Journal of Agromedicine and Medical Sciences 2, no. 3 (2016): 8. http://dx.doi.org/10.19184/ams.v2i3.3254.

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Filaggrin, or filaments which combines protein, is one of the important structural protein that works for the development, maintenance, and the formation of the skin as an intact barrier. Filaggrin breakdown products regulate the hydration of the skin; contribute to the acidic pH of the skin, which in turn is essential for the activity of various proteases in the stratum corneum desquamation and lipid synthesis. Filaggrin produced by keratinocytes granular as a major precursor called profilaggrin, encoded by the FLG gene, located in the epidermal differentiation complex on chromosome 1 (1q21 locus). The locus contains a group of genes involved in epidermal differentiation. Filaggrin deficiency has some consequences on the organization and function of epidermal with important implications such as increased risk for atopic disease or a microbial infection. FLG mutation, a gene that encodes filaggrin, has been shown to cause ichthyosis vulgaris, increasing the risk of atopic dermatitis and other atopic diseases. This research examined the FLG gene based bioinformatics approach to search for conserved region of representative mammals that encode coding (m) and non-coding (nc) RNAs. Expected mRNA expression can be used as a diagnostic and therapeutic agent against deficiencies and filaggrin mutations.Key words: filaggrin, FLG, profilaggrin, filaggrin deficiency, bioinformatics.

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Uzun, Alper, Surendra Sharma, and James Padbury. "A Bioinformatics Approach to Preterm Birth." American Journal of Reproductive Immunology 67, no. 4 (2012): 273–77. http://dx.doi.org/10.1111/j.1600-0897.2012.01122.x.

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Rakesh, Palepu Narasimha. "A Data Science Approach to Bioinformatics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3860–69. http://dx.doi.org/10.22214/ijraset.2021.37221.

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Computer aided drug design (CADD) which uses the computational advance towards to develop, discover and scrutinize and examine drugs and alike biologically agile molecules. CADD is a specialized stream which uses the computational techniques to mimic drug-receptor interactions. CADD procedures are so much dependent on the tools of bioinformatics, databases & applications. There are so many advantages of computer aided drug discovery; it saves lot of time which is one of the main advantages followed by low cost and more accuracy. CADD required less manpower to work. There are different types of CADD such as ligand and structure based design. Objectives of the Computer aided drug design are to boost up the screening process, to test the rational of drug design, to efficiently screen and to remove hopeless ones as early as possible. In Drug designing the selected molecule should be organic small molecule, complementary in shape to the target and oppositely charged to the biomolecular target. The molecule will interacts and binds with the target which activates or inhibits the function of a biomolecule such as a protein or lipid. The main basic goal in the drug design is to forecast whether a given molecule will bind to target and if thus how strongly. Molecular mechanics techniques also used to provide the semi quantitative prediction of the binding affinity. These techniques use machine learning, linear regression, neural nets or other statistical methods to derive predictive binding affinity equations. Preferably, the computational technique will be able to forecast the affinity prior to a compound is synthesized, saving huge time and cost. Computational techniques have quickened the discovery by decreasing the number of iterations required and have often produced the novel structures.

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Cordes, Frank, Rolf Kaiser, and Joachim Selbig. "Bioinformatics approach to predicting HIV drug resistance." Expert Review of Molecular Diagnostics 6, no. 2 (2006): 207–15. http://dx.doi.org/10.1586/14737159.6.2.207.

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Somanathan, Indira, and Chris Baysdorfer. "A bioinformatics approach to identify telomere sequences." BioTechniques 65, no. 1 (2018): 20–25. http://dx.doi.org/10.2144/btn-2018-0057.

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Sridhar, GR, CH Divakar, T. Hanuman, and AllamAppa Rao. "Bioinformatics approach to extract information from genes." International Journal of Diabetes in Developing Countries 26, no. 4 (2006): 149. http://dx.doi.org/10.4103/0973-3930.33179.

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Pascar, Jane, and Christopher H. Chandler. "A bioinformatics approach to identifyingWolbachiainfections in arthropods." PeerJ 6 (September 3, 2018): e5486. http://dx.doi.org/10.7717/peerj.5486.

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Wolbachiais the most widespread endosymbiont, infecting >20% of arthropod species, and capable of drastically manipulating the host’s reproductive mechanisms. Conventionally, diagnosis has relied on PCR amplification; however, PCR is not always a reliable diagnostic technique due to primer specificity, strain diversity, degree of infection and/or tissue sampled. Here, we look for evidence ofWolbachiainfection across a wide array of arthropod species using a bioinformatic approach to detect theWolbachiagenesftsZ, wsp,and thegroEoperon in next-generation sequencing samples available through the NCBI Sequence Read Archive. For samples showing signs of infection, we attempted to assemble entireWolbachiagenomes, and in order to better understand the relationships between hosts and symbionts, phylogenies were constructed using the assembled gene sequences. Out of the 34 species with positively identified infections, eight species of arthropod had not previously been recorded to harborWolbachiainfection. All putative infections cluster with known representative strains belonging to supergroup A or B, which are known to only infect arthropods. This study presents an efficient bioinformatic approach for post-sequencing diagnosis and analysis ofWolbachiainfection in arthropods.

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Stein, Lincoln D., Sam Cartinhour, Danielle Thierry-Mieg, and Jean Thierry-Mieg. "JADE: An approach for interconnecting bioinformatics databases." Gene 209, no. 1-2 (1998): GC39—GC43. http://dx.doi.org/10.1016/s0378-1119(97)00672-0.

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Garhwal, Abhimanyu Singh, and Wei Qi Yan. "BIIIA: a bioinformatics-inspired image identification approach." Multimedia Tools and Applications 78, no. 8 (2018): 9537–52. http://dx.doi.org/10.1007/s11042-018-6551-y.

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Potla, Pratibha, Shabana Amanda Ali, and Mohit Kapoor. "A bioinformatics approach to microRNA-sequencing analysis." Osteoarthritis and Cartilage Open 3, no. 1 (2021): 100131. http://dx.doi.org/10.1016/j.ocarto.2020.100131.

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18

Kahn, Scott. "Bioinformatics: a holistic approach to drug discovery." Drug Discovery Today 7, no. 12 (2002): 633–34. http://dx.doi.org/10.1016/s1359-6446(02)02295-x.

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Eckardt, Nancy A. "A Bioinformatics Approach to Investigating Leaf Development." Plant Cell 20, no. 9 (2008): 2283. http://dx.doi.org/10.1105/tpc.108.200912.

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Best, Mark A. "Bioinformatics: the Machine Learning Approach, 2nd edn." Journal of the Royal Statistical Society: Series A (Statistics in Society) 167, no. 1 (2004): 184. http://dx.doi.org/10.1111/j.1467-985x.2004.298_2.x.

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Dixon, Ruth M., and Jonathan A. Jones. "Mapping Mutations in Legislation: A Bioinformatics Approach." Parliamentary Affairs 72, no. 1 (2018): 21–41. http://dx.doi.org/10.1093/pa/gsy006.

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Olivero-Verbel, Jesús, María Cabarcas-Montalvo, and Carlos Ortega-Zúñiga. "Theoretical targets for TCDD: A bioinformatics approach." Chemosphere 80, no. 10 (2010): 1160–66. http://dx.doi.org/10.1016/j.chemosphere.2010.06.020.

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Bicego, Manuele, and Pietro Lovato. "A bioinformatics approach to 2D shape classification." Computer Vision and Image Understanding 145 (April 2016): 59–69. http://dx.doi.org/10.1016/j.cviu.2015.11.011.

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Ferguson, Adam R., Ellen D. Stück, and Jessica L. Nielson. "Syndromics: A Bioinformatics Approach for Neurotrauma Research." Translational Stroke Research 2, no. 4 (2011): 438–54. http://dx.doi.org/10.1007/s12975-011-0121-1.

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Mathé, Ewy, Ben Busby, and Helen Piontkivska. "Matchmaking in Bioinformatics." F1000Research 7 (February 9, 2018): 171. http://dx.doi.org/10.12688/f1000research.13705.1.

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Ever return from a meeting feeling elated by all those exciting talks, yet unsure how all those presented glamorous and/or exciting tools can be useful in your research? Or do you have a great piece of software you want to share, yet only a handful of people visited your poster? We have all been there, and that is why we organized the Matchmaking for Computational and Experimental Biologists Session at the latest ISCB/GLBIO’2017 meeting in Chicago (May 15-17, 2017). The session exemplifies a novel approach, mimicking “matchmaking”, to encouraging communication, making connections and fostering collaborations between computational and non-computational biologists. More specifically, the session facilitates face-to-face communication between researchers with similar or differing research interests, which we feel are critical for promoting productive discussions and collaborations. To accomplish this, three short scheduled talks were delivered, focusing on RNA-seq, integration of clinical and genomic data, and chromatin accessibility analyses. Next, small-table developer-led discussions, modeled after speed-dating, enabled each developer (including the speakers) to introduce a specific tool and to engage potential users or other developers around the table. Notably, we asked the audience whether any other tool developers would want to showcase their tool and we thus added four developers as moderators of these small-table discussions. Given the positive feedback from the tool developers, we feel that this type of session is an effective approach for promoting valuable scientific discussion, and is particularly helpful in the context of conferences where the number of participants and activities could hamper such interactions.

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Wu, Xiya, Wei Zhang, Yunhua Hu, and Xianghua Yi. "Bioinformatics Approach Reveals Systematic Mechanism Underlying Lung Adenocarcinoma." Tumori Journal 101, no. 3 (2015): 281–86. http://dx.doi.org/10.5301/tj.5000278.

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Machado Dias, Alvaro. "Bioinformatics Approach to BDNF and BDNF-Related Disorders." Current Neuropharmacology 9, no. 2 (2011): 318–29. http://dx.doi.org/10.2174/157015911795596586.

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VAN BERLO, ROGIER J. P., LODEWYK F. A. WESSELS, DICK DE RIDDER, and MARCEL J. T. REINDERS. "PROTEIN COMPLEX PREDICTION USING AN INTEGRATIVE BIOINFORMATICS APPROACH." Journal of Bioinformatics and Computational Biology 05, no. 04 (2007): 839–64. http://dx.doi.org/10.1142/s0219720007002953.

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Since protein complexes play a crucial role in biological cells, one of the major goals in bioinformatics is the elucidation of protein complexes. A general approach is to build a prediction rule based on multiple data sources, e.g. gene expression data and protein interaction data, to assess the likelihood of two proteins having complex association.a We critically revisit the step of predictor construction, i.e. the determination of a proper training set, an optimal classifier, and, most importantly, an optimal feature set. We use an exhaustive set of features, which includes the 2hop-feature as introduced by Wong et al.23 for predicting synthetic sick or lethal interactions. Post-processing of the likelihoods of protein interaction is then required to extract protein complexes. We propose a new protocol for combining these likelihood estimates. The protocol interprets the probabilities of complex association as output by the prediction rule as distances and employs hierarchical clustering to find groups of interacting proteins. In contrast to the computationally expensive search-and-score approach of Sharan et al.,19 this protocol is very fast and can be applied to fully connected graphs. The protocol identifies trusted protein complexes with high confidence. We show that the 2hop-feature is relevant for predicting protein complexes. Furthermore, several interesting hypotheses about new protein complexes have been generated. For example, our approach linked the protein FYV4 to the mitochondrial ribosomal subunit. Interestingly, it is known that this protein is located in the mitochondrion, but its biological role is unknown. Vid22 and YGR071C were also linked, which corresponds to the new TAP data of Krogan et al.14

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Markel, S. "Introduction to Bioinformatics: A Theoretical and Practical Approach." Briefings in Bioinformatics 4, no. 2 (2003): 199–200. http://dx.doi.org/10.1093/bib/4.2.199.

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Rao, Allam Appa, Hanuman Thota, Ramamurthy Adapala, et al. "Proteomic Analysis in Diabetic Cardiomyopathy using Bioinformatics Approach." Bioinformatics and Biology Insights 2 (January 2008): BBI.S313. http://dx.doi.org/10.4137/bbi.s313.

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Diabetic cardiomyopathy is a distinct clinical entity that produces asymptomatic heart failure in diabetic patients without evidence of coronary artery disease and hypertension. Abnormalities in diabetic cardiomyopathy include: myocardial hypertrophy, impairment of contractile proteins, accumulation of extracellular matrix proteins, formation of advanced glycation end products, and decreased left ventricular compliance. These abnormalities lead to the most common clinical presentation of diabetic cardiomyopathy in the form of diastolic dysfunction. We evaluated the role of various proteins that are likely to be involved in diabetic cardiomyopathy by employing multiple sequence alignment using ClustalW tool and constructed a Phylogenetic tree using functional protein sequences extracted from NCBI. Phylogenetic tree was constructed using Neighbour—Joining Algorithm in bioinformatics approach. These results suggest a causal relationship between altered calcium homeostasis and diabetic cardiomyopathy that implies that efforts directed to normalize calcium homeostasis could form a novel therapeutic approach.

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Ebrahimie, Esmaeil, Mansour Ebrahimi, Narjes Sarvestani, and Mahdi Ebrahimi. "Protein attributes contribute to halo-stability, bioinformatics approach." Saline Systems 7, no. 1 (2011): 1. http://dx.doi.org/10.1186/1746-1448-7-1.

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Li-Ming Wong, Sophia, and Michael G. Walker. "A bioinformatics approach to identifying fetal development genes." Gene Function & Disease 2, no. 5-6 (2001): 221–25. http://dx.doi.org/10.1002/1438-826x(200112)2:5/6<221::aid-gnfd221>3.0.co;2-#.

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Basyuni, M., R. Hayati, B. Pratomo, Lisnawita, and H. Sagami. "Bioinformatics approach of polyprenol reductase in Hevea brasiliensis." Journal of Physics: Conference Series 1235 (June 2019): 012045. http://dx.doi.org/10.1088/1742-6596/1235/1/012045.

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Giegerich, R. "A systematic approach to dynamic programming in bioinformatics." Bioinformatics 16, no. 8 (2000): 665–77. http://dx.doi.org/10.1093/bioinformatics/16.8.665.

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Kojic-Prodic, B., F. Kovacic, I. Lescic, S. Wilhelm, S. Tomic, and K. E. Jaeger. "Bioinformatics approach to characterization of SGNH/GDSL-hydrolases." Acta Crystallographica Section A Foundations of Crystallography 61, a1 (2005): c42. http://dx.doi.org/10.1107/s0108767305098235.

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Poe, David, Nirmala Venkatraman, Christine Hansen, and Gautam Singh. "Component-Based Approach for Educating Students in Bioinformatics." IEEE Transactions on Education 52, no. 1 (2009): 1–9. http://dx.doi.org/10.1109/te.2007.914943.

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Moustafa, Mohamed E., and Hussien A. Antar. "A Bioinformatics Approach to Characterize Mammalian Selenoprotein T." Biochemical Genetics 50, no. 9-10 (2012): 736–47. http://dx.doi.org/10.1007/s10528-012-9516-2.

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Lei, Liu, Liu Zhenzhong, Lin Lin, and Pan Bo. "Uncovering the pathogenesis of microtia using bioinformatics approach." International Journal of Pediatric Otorhinolaryngology 99 (August 2017): 30–35. http://dx.doi.org/10.1016/j.ijporl.2017.05.009.

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39

LUCK, MICHAEL, and EMANUELA MERELLI. "Agents in bioinformatics." Knowledge Engineering Review 20, no. 2 (2005): 117–25. http://dx.doi.org/10.1017/s0269888905000433.

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The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

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Katiyar, Amit, Shuchi Smita, Viswanathan Chinnusamy, Dev Mani Pandey, and Kailash Bansal. "Identification of miRNAs in sorghum by using bioinformatics approach." Plant Signaling & Behavior 7, no. 2 (2012): 246–59. http://dx.doi.org/10.4161/psb.18914.

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Khalid, RM, A. Bailey, and RJ Cox. "Bioinformatics Approach in Natural Products Discovery: A Cautionary Tale." Open Conference Proceedings Journal 4, no. 1 (2013): 33. http://dx.doi.org/10.2174/2210289201304010033.

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42

Bard, Jonathan B. L., Richard A. Baldock, and Duncan R. Davidson. "Elucidating the Genetic Networks of Development: A Bioinformatics Approach." Genome Research 8, no. 9 (1998): 859–63. http://dx.doi.org/10.1101/gr.8.9.859.

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43

Wu, Duojiao, Catherine M. Rice, and Xiangdong Wang. "Cancer bioinformatics: A new approach to systems clinical medicine." BMC Bioinformatics 13, no. 1 (2012): 71. http://dx.doi.org/10.1186/1471-2105-13-71.

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44

Alterovitz, Gil, and Marco F. Ramoni. "Bioinformatics and Proteomics: An Engineering Problem Solving-Based Approach." IEEE Transactions on Education 50, no. 1 (2007): 49–54. http://dx.doi.org/10.1109/te.2006.886454.

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Dey, Sumanta, Ashesh Nandy, Subhash C. Basak, Papiya Nandy, and Sukhen Das. "A Bioinformatics approach to designing a Zika virus vaccine." Computational Biology and Chemistry 68 (June 2017): 143–52. http://dx.doi.org/10.1016/j.compbiolchem.2017.03.002.

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Sharafi, Ebrahim, Jamshid Farmani, Ali Pakdin Parizi, and Ali Dehestani. "In Search of Engineered Prokaryotic Chlorophyllases: A Bioinformatics Approach." Biotechnology and Bioprocess Engineering 23, no. 5 (2018): 507–24. http://dx.doi.org/10.1007/s12257-018-0143-6.

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Jaeger, Daniel, Johannes Barth, Anna Niehues, and Christian Fufezan. "pyGCluster, a novel hierarchical clustering approach." Bioinformatics 30, no. 6 (2013): 896–98. http://dx.doi.org/10.1093/bioinformatics/btt626.

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48

Hočevar, Tomaž, and Janez Demšar. "A combinatorial approach to graphlet counting." Bioinformatics 30, no. 4 (2014): 559–65. http://dx.doi.org/10.1093/bioinformatics/btt717.

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Şen, Alper, Kamyar Kargar, Esma Akgün, and Mustafa Ç. Pınar. "Codon optimization: a mathematical programing approach." Bioinformatics 36, no. 13 (2020): 4012–20. http://dx.doi.org/10.1093/bioinformatics/btaa248.

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Abstract Motivation Synthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions. Results We develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations. Availability and implementation http://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.

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50

Gleizes, A., and A. Hénaut. "A global approach for contig construction." Bioinformatics 10, no. 4 (1994): 401–8. http://dx.doi.org/10.1093/bioinformatics/10.4.401.

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Journal articles on the topic 'Bioinformatics approach'

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