Relevant bibliographies by topics / Marketing Bioinformatics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Marketing Bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Marketing Bioinformatics"

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Brokaw, Stephen. "Bioinformatics:." Journal of Pharmaceutical Marketing & Management 16, no. 4 (2005): 65–80. http://dx.doi.org/10.1300/j058v16n04_04.

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Duvaud, Séverine, Chiara Gabella, Frédérique Lisacek, Heinz Stockinger, Vassilios Ioannidis, and Christine Durinx. "Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users." Nucleic Acids Research 49, W1 (2021): W216—W227. http://dx.doi.org/10.1093/nar/gkab225.

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Abstract The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss) creates, maintains and disseminates a portfolio of reliable and state-of-the-art bioinformatics services and resources for the storage, analysis and interpretation of biological data. Through Expasy (https://www.expasy.org), the Swiss Bioinformatics Resource Portal, the scientific community worldwide, freely accesses more than 160 SIB resources supporting a wide range of life science and biomedical research areas. In 2020, Expasy was redesigned through a user-centric approach, known as User-Centred Design (UCD), whose aim is to create user interfaces that are easy-to-use, efficient and targeting the intended community. This approach, widely used in other fields such as marketing, e-commerce, and design of mobile applications, is still scarcely explored in bioinformatics. In total, around 50 people were actively involved, including internal stakeholders and end-users. In addition to an optimised interface that meets users' needs and expectations, the new version of Expasy provides an up-to-date and accurate description of high-quality resources based on a standardised ontology, allowing to connect functionally-related resources.
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López-Fernández, Aurelio, Domingo S. Rodríguez-Baena, and Francisco Gómez-Vela. "gMSR: A Multi-GPU Algorithm to Accelerate a Massive Validation of Biclusters." Electronics 9, no. 11 (2020): 1782. http://dx.doi.org/10.3390/electronics9111782.

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Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinformatics, Biclustering techniques have become extremely time-consuming, also being huge the number of results generated, due to the continuous increase in the size of the databases over the last few years. For this reason, validation techniques must be adapted to this new environment in order to help researchers focus their efforts on a specific subset of results in an efficient, fast and reliable way. The aforementioned situation may well be considered as Big Data context. In this sense, multiple machine learning techniques have been implemented by the application of Graphic Processing Units (GPU) technology and CUDA architecture to accelerate the processing of large databases. However, as far as we know, this technology has not yet been applied to any bicluster validation technique. In this work, a multi-GPU version of one of the most used bicluster validation measure, Mean Squared Residue (MSR), is presented. It takes advantage of all the hardware and memory resources offered by GPU devices. Because of to this, gMSR is able to validate a massive number of biclusters in any Biclustering-based study within a Big Data context.
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Martinez, Edson Zangiacomi, and Jorge Alberto Achcar. "Trends in epidemiology in the 21st century: time to adopt Bayesian methods." Cadernos de Saúde Pública 30, no. 4 (2014): 703–14. http://dx.doi.org/10.1590/0102-311x00144013.

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2013 marked the 250th anniversary of the presentation of Bayes’ theorem by the philosopher Richard Price. Thomas Bayes was a figure little known in his own time, but in the 20th century the theorem that bears his name became widely used in many fields of research. The Bayes theorem is the basis of the so-called Bayesian methods, an approach to statistical inference that allows studies to incorporate prior knowledge about relevant data characteristics into statistical analysis. Nowadays, Bayesian methods are widely used in many different areas such as astronomy, economics, marketing, genetics, bioinformatics and social sciences. This study observed that a number of authors discussed recent advances in techniques and the advantages of Bayesian methods for the analysis of epidemiological data. This article presents an overview of Bayesian methods, their application to epidemiological research and the main areas of epidemiology which should benefit from the use of Bayesian methods in coming years.
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Brown, Michael G., and Paolina Centonze. "Exploiting Flaws in Big Data Systems." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 8 (2016): 6967–75. http://dx.doi.org/10.24297/ijct.v15i8.1498.

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In this journal we discuss the relevant security threats, vulnerabilities and prominent techniques to securing data lake. Over the last few years, Big Data solutions to data processing have made remarkable strides in the how we interpret data. Because of its ubiquitous nature and ability to handle various data types, it has become a valuable tool and asset to many other fields including bioinformatics solutions, marketing solutions, and social media. Ethical infractions also play a role in data processing and because of this, it is critical that comprehensive security solutions are determined and enforced under the appropriate conditions. Although modern security techniques are well developed, they still leave room for scrutiny as vulnerabilities continue to unfold. In this paper, we will examine and compare modern ethical exploit techniques as well ass techniques to promote greater authentication and overall perimeter security. The scrutinization of the current methods met through exploitation, be it physical or conceptive, is imperative in defining the understanding and verification of definite secure solutions. Â
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Makris, Christos, Georgios Pispirigos, and Michael Angelos Simos. "Text Semantic Annotation: A Distributed Methodology Based on Community Coherence." Algorithms 13, no. 7 (2020): 160. http://dx.doi.org/10.3390/a13070160.

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Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial intelligence tasks are shifting to semantic representations for addressing the inherent natural language polysemy and homonymy challenges. With extensive application in a broad range of scientific fields, such as digital marketing, bioinformatics, chemical engineering, neuroscience, and social sciences, community detection has attracted great scientific interest. Focusing on linguistics, by aiming to identify groups of densely interconnected subgroups of semantic ontologies, community detection application has proven beneficial in terms of disambiguation improvement and ontology enhancement. In this paper we introduce a novel distributed supervised knowledge-based methodology employing community detection algorithms for text annotation with Wikipedia Entities, establishing the unprecedented concept of community Coherence as a metric for local contextual coherence compatibility. Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration of less common annotations. The proposed methodology is propitious for wider adoption, attaining robust disambiguation performance.
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Aparna K. and Mydhili K. Nair. "Comprehensive Study and Analysis of Partitional Data Clustering Techniques." International Journal of Business Analytics 2, no. 1 (2015): 23–38. http://dx.doi.org/10.4018/ijban.2015010102.

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Data clustering has found significant applications in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. There are several types of data clustering such as partitional, hierarchical, spectral, density-based, mixture-modeling to name a few. Among these, partitional clustering is well suited for most of the applications due to the less computational requirement. An analysis of various literatures available on partitional clustering will not only provide good knowledge, but will also lead to find the recent problems in partitional clustering domain. Accordingly, it is planned to do a comprehensive study with the literature of partitional data clustering techniques. In this paper, thirty three research articles have been taken for survey from the standard publishers from 2005 to 2013 under two different aspects namely the technical aspect and the application aspect. The technical aspect is further classified based on partitional clustering, constraint-based partitional clustering and evolutionary programming-based clustering techniques. Furthermore, an analysis is carried out, to find out the importance of the different approaches that can be adopted, so that any new development in partitional data clustering can be made easier to be carried out by researchers.
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Yu, Peter K. "Data Exclusivities in the Age of Big Data, Biologics, and Plurilaterals." Texas A&M Law Review 6, no. 4 (2019): 22–33. http://dx.doi.org/10.37419/lr.v6.arg.2.

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The past decade has seen many new developments impacting the intellectual property system. The introduction of big data analytics has transformed the fields of biotechnology and bioinformatics while ushering in major advances in drug development, clinical practices, and medical financing. The arrival of biologics and personalized medicines has also revolutionized the healthcare and pharmaceutical industries. In addition, the emergence of bilateral, regional, and plurilateral trade agreements have raised serious, and at times difficult, questions concerning the evolution of domestic and international intellectual property standards. One topic linking all three developments together concerns the establishment of international standards to protect clinical trial data that have been submitted to regulatory authorities for the marketing approval of pharmaceutical products. During the negotiations for the Trans-Pacific Partnership (TPP), for example, the protection of clinical trial data submitted for the marketing approval of biologics was highly contentious. Although the United States’ withdrawal in January 2017 has since placed the TPP Agreement and its data exclusivity provisions for pharmaceuticals and biologics on life support, the debate on the protection of clinical trial data will continue and will emerge in future bilateral, regional, and plurilateral trade negotiations, including the renegotiations on the North American Free Trade Agreement (NAFTA). Part I of this Article reviews the protection of clinical trial data under Article 39.3 of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement) of the World Trade Organization (WTO). Even though the provision covers both pharmaceutical and agricultural chemical products, this Article focuses only on the former. Part II examines the additional protection clinical trial data have received through TRIPS-plus bilateral, regional, and plurilateral trade agreements. Part III outlines five specific recommendations to help advance the debate on such protection in the age of big data, biologics, and plurilateral trade agreements.
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Saikia, Surovi, and Manobjyoti Bordoloi. "Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective." Current Drug Targets 20, no. 5 (2019): 501–21. http://dx.doi.org/10.2174/1389450119666181022153016.

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Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don’t always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.
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D'URSO, PIERPAOLO. "FUZZY C-MEANS CLUSTERING MODELS FOR MULTIVARIATE TIME-VARYING DATA: DIFFERENT APPROACHES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 03 (2004): 287–326. http://dx.doi.org/10.1142/s0218488504002849.

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The classification of multivariate time-varying data finds application in several fields, such as economics, finance, marketing research, psychometrics, bioinformatics, medicine, signal processing, pattern recognition, etc. In this paper, by considering an exploratory formalization, we propose different unsupervised clustering models for multivariate data time arrays (objects×quantitative variables×times). These models can be classified in two different approaches: the cross sectional and the longitudinal approach. In the first case, after the objects, observed at each time, have been classified, comparison among the classifications made in different time instants will be done. In the second approach, we cluster the time trajectories of the objects; then, we obtain only one classification by comparing the instantaneous and evolutive features of the trajectories of the objects. In particular, in this work, the second approach is analyzed in detail, with reference to the so-called single and double step procedures. Geometric, correlative, instantaneous, evolutive and trend characteristics of the multivariate time arrays are taken into account in the different proposed clustering models. Furthermore, the fuzzy approach, that is particularly suitable in the dynamic classification problem, has been considered. Extensions of a cluster-validity criterion for the proposed fuzzy dynamic clustering models are also suggested. A socio-economic example concludes the paper.
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Dissertations / Theses on the topic "Marketing Bioinformatics"

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Leung, Ho-yin. "Stochastic models for optimal control problems with applications." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42841781.

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Leung, Ho-yin, and 梁浩賢. "Stochastic models for optimal control problems with applications." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841781.

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Allen, Anthony W. Jr. "Young Adult Perception and Acceptance of Biometrics." Youngstown State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1363891122.

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Books on the topic "Marketing Bioinformatics"

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Probabilistic methods for bionformatics: With an introduction to Bayesian networks. Morgan Kaufmann Publishers, 2009.

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Book chapters on the topic "Marketing Bioinformatics"

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Bose, Subhas Chandra, and Ravi Kiran. "Digital Marketing: A Sustainable Way to Thrive in Competition of Agriculture Marketing." In Bioinformatics for agriculture: High-throughput approaches. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4791-5_8.

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Kohli, Shruti, and Sonia Saini. "Mining of Medical Trends Using Social Networks." In Advances in Bioinformatics and Biomedical Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8811-7.ch008.

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Recent work in machine learning and natural language processing has studied the content of health related information in tweets and demonstrated the potential for extracting useful public health information from their aggregation. Social intelligence derived from health content has become of significant importance for various applications, including post-marketing drug surveillance, competitive intelligence, medicine reviews and to assess health-related opinions and sentiments. Further, the quantity of medical information in the media such as tweets on Twitter, Facebook or medical blogs is growing at an exponential rate. Medical data such as health records, drug data, etc. has become major candidates for Big Data analysis and thus exploring this content has become a necessity for organizations. However, the volume, velocity, variety, and quality of online health information present challenges, necessitating enhanced facilitation mechanisms for medical social computing. The objective of this chapter is to discuss the possibility of mining medical trends using Social Networks.
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Gupta, Anamika, Shikha Gupta, and Naveen Kumar. "Mining Frequent Closed Itemsets for Association Rules." In Handbook of Research on Innovations in Database Technologies and Applications. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-242-8.ch057.

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Association refers to correlations that exist among data. Association Rule Mining (ARM) is an important data-mining task. It refers to discovery of rules between different sets of attributes/items in very large databases (Agrawal R. & Srikant R. 1994). The discovered rules help in strategic decision making in both commercial and scientific domains. A classical application of ARM is market basket analysis, an application of data mining in retail sales where associations between the different items are discovered to analyze the customer’s buying habits in order to develop better marketing strategies. ARM has been extensively used in other applications like spatial-temporal, health care, bioinformatics, web data etc (Han J., Cheng H., Xin D., & Yan X. 2007).
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Conference papers on the topic "Marketing Bioinformatics"

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Guo, Xuebing, and Kexin Yuan. "Promotion of Marketing Efficiency of SMEs Based on Big Data." In BIC 2021: 2021 International Conference on Bioinformatics and Intelligent Computing. ACM, 2021. http://dx.doi.org/10.1145/3448748.3448787.

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