Relevant bibliographies by topics / Personalised Search Agents

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

Academic literature on the topic 'Personalised Search Agents'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Personalised Search Agents"

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Cosway, B., V. Paleri, and J. Wilson. "Biomarkers predicting chemotherapy response in head and neck squamous cell carcinoma: a review." Journal of Laryngology & Otology 129, no. 11 (October 2, 2015): 1046–52. http://dx.doi.org/10.1017/s0022215115002479.

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AbstractBackground:Biomarkers are increasingly being used in many cancers to select patients for oncological treatment paradigms based on their inherent genetic properties. However, in head and neck cancers, there are no personalised therapies available outside the context of a clinical trial. A number of studies suggest there are intrinsic tumour properties of head and neck cancers that affect their response to chemotherapeutic agents. This paper aimed to review their evidence base.Method:A narrative review was conducted following a search of the PubMed database.Results and conclusion:The review identified a number of biomarkers predicting response to chemotherapy in head and neck cancers. The paper discusses these in detail, and explores where future research could be directed in order to deliver personalised therapies for patients with head and neck cancers.
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Gisbert, Javier P., and María Chaparro. "Predictors of Primary Response to Biologic Treatment [Anti-TNF, Vedolizumab, and Ustekinumab] in Patients With Inflammatory Bowel Disease: From Basic Science to Clinical Practice." Journal of Crohn's and Colitis 14, no. 5 (November 28, 2019): 694–709. http://dx.doi.org/10.1093/ecco-jcc/jjz195.

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Abstract Background Inflammatory bowel diseases [IBD]―ulcerative colitis and Crohn’s disease―are commonly treated with biologic drugs. However, only approximately two-thirds of patients have an initial response to these therapies. Personalised medicine has the potential to optimise efficacy, decrease the risk of adverse drug events, and reduce costs by establishing the most suitable therapy for a selected patient. Aim The present study reviews the potential predictors of short-term primary response to biologic treatment, including not only anti-tumour necrosis factor [TNF] agents [such as infliximab, adalimumab, certolizumab, and golimumab] but also vedolizumab and ustekinumab. Methods We performed a systematic bibliographical search to identify studies investigating predictive factors of response to biologic therapy. Results For anti-TNF agents, most of the evaluated factors have not demonstrated usefulness, and many others are still controversial. Thus, only a few factors may have a potential role in the prediction of the response, including disease behaviour/phenotype, disease severity, C-reactive protein, albumin, cytokine expression in serum, previous anti-TNF therapy, some proteomic markers, and some colorectal mucosa markers. For vedolizumab, the availability of useful predictive markers seems to be even lower, with only some factors showing a limited value, such as the expression of α4β7 integrin in blood, the faecal microbiota, some proteomic markers, and some colorectal mucosa markers. Finally, in the case of ustekinumab, no predictive factor has been reported yet to be helpful in clinical practice. Conclusion In summary, currently no single marker fulfils all criteria for being an appropriate prognostic indicator of response to any biologic treatment in IBD.
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Wang, Rui, Wentao Li, Esmée M. Bordewijk, Richard S. Legro, Heping Zhang, Xiaoke Wu, Jingshu Gao, et al. "First-line ovulation induction for polycystic ovary syndrome: an individual participant data meta-analysis." Human Reproduction Update 25, no. 6 (October 23, 2019): 717–32. http://dx.doi.org/10.1093/humupd/dmz029.

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Abstract BACKGROUND Polycystic ovary syndrome (PCOS) is the most frequent cause of anovulatory infertility. In women with PCOS, effective ovulation induction serves as an important first-line treatment for anovulatory infertility. Individual participant data (IPD) meta-analysis is considered as the gold standard for evidence synthesis which provides accurate assessments of outcomes from primary randomised controlled trials (RCTs) and allows additional analyses for time-to-event outcomes. It also facilitates treatment–covariate interaction analyses and therefore offers an opportunity for personalised medicine. OBJECTIVE AND RATIONALE We aimed to evaluate the effectiveness of different ovulation induction agents, in particular letrozole alone and clomiphene citrate (CC) plus metformin, as compared to CC alone, as the first-line choice for ovulation induction in women with PCOS and infertility, and to explore interactions between treatment and participant-level baseline characteristics. SEARCH METHODS We searched electronic databases including MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials up to 20 December 2018. We included RCTs comparing the following interventions with each other or placebo/no treatment in women with PCOS and infertility: CC, metformin, CC plus metformin, letrozole, gonadotrophin and tamoxifen. We excluded studies on treatment-resistant women. The primary outcome was live birth. We contacted the investigators of eligible RCTs to share the IPD and performed IPD meta-analyses. We assessed the risk of bias by using the Cochrane risk of bias tool for RCTs. OUTCOMES IPD of 20 RCTs including 3962 women with PCOS were obtained. Six RCTs compared letrozole and CC in 1284 women. Compared with CC, letrozole improved live birth rates (3 RCTs, 1043 women, risk ratio [RR] 1.43, 95% confidence interval [CI] 1.17–1.75, moderate-certainty evidence) and clinical pregnancy rates (6 RCTs, 1284 women, RR 1.45, 95% CI 1.23–1.70, moderate-certainty evidence) and reduced time-to-pregnancy (6 RCTs, 1235 women, hazard ratio [HR] 1.72, 95% CI 1.38–2.15, moderate-certainty evidence). Meta-analyses of effect modifications showed a positive interaction between baseline serum total testosterone levels and treatment effects on live birth (interaction RR 1.29, 95% CI 1.01–1.65). Eight RCTs compared CC plus metformin to CC alone in 1039 women. Compared with CC alone, CC plus metformin might improve clinical pregnancy rates (8 RCTs, 1039 women, RR 1.18, 95% CI 1.00–1.39, low-certainty evidence) and might reduce time-to-pregnancy (7 RCTs, 898 women, HR 1.25, 95% CI 1.00–1.57, low-certainty evidence), but there was insufficient evidence of a difference on live birth rates (5 RCTs, 907 women, RR 1.08, 95% CI 0.87–1.35, low-certainty evidence). Meta-analyses of effect modifications showed a positive interaction between baseline insulin levels and treatment effects on live birth in the comparison between CC plus metformin and CC (interaction RR 1.03, 95% CI 1.01–1.06). WIDER IMPLICATIONS In women with PCOS, letrozole improves live birth and clinical pregnancy rates and reduces time-to-pregnancy compared to CC and therefore can be recommended as the preferred first-line treatment for women with PCOS and infertility. CC plus metformin may increase clinical pregnancy and may reduce time-to-pregnancy compared to CC alone, while there is insufficient evidence of a difference on live birth. Treatment effects of letrozole are influenced by baseline serum levels of total testosterone, while those of CC plus metformin are affected by baseline serum levels of insulin. These interactions between treatments and biomarkers on hyperandrogenaemia and insulin resistance provide further insights into a personalised approach for the management of anovulatory infertility related to PCOS.
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GODOY, DANIELA, and ANALÍA AMANDI. "COLLABORATIVE WEB SEARCH BASED ON USER INTEREST SIMILARITY." International Journal of Cooperative Information Systems 17, no. 04 (December 2008): 495–521. http://dx.doi.org/10.1142/s0218843008001907.

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The motivation behind personal information agents resides in the enormous amount of information available on the Web, which has created a pressing need for effective personalized techniques. In order to assists Web search these agents rely on user profiles modeling information preferences, interests and habits that help to contextualize user queries. In communities of people with similar interests, collaboration among agents fosters knowledge sharing and, consequently, potentially improves the results of individual agents by taking advantage of the knowledge acquired by other agents. In this paper, we propose an agent-based recommender system for supporting collaborative Web search in groups of users with partial similarity of interests. Empirical evaluation showed that the interaction among personal agents increases the performance of the overall recommender system, demonstrating the potential of the approach to reduce the burden of finding information on the Web.
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Rattrout, Amjad, Rasha Assaf, and Ali Al-Dahoud. "Personalizing the dynamic information resources in a self organized web system using CAS and MAS." Computer Science and Information Systems 7, no. 4 (2010): 883–905. http://dx.doi.org/10.2298/csis090608023r.

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The Web is a constantly growing dynamic environment where the components are changed in non-linear ways. These components represent the targets to researches in order to better understand the behavior of the Web, where the owners and the users in this environment exist as out factors. Web page Usage information is the term which describes ways and methods of using the Web. Various factors affect the use of the diversity of the resources in the Web, The non-linear way of its growth, and the evolution in the methods for how we build the Web pages which eventually leads to reflecting the users? interests. Personalizing the results of search engines are created to meet the users need for information on the Web. Generally, the researchers seek user?s satisfaction through utilizing these search engines to serve the user. One of the most efficient methods in this domain is the use of semantic measure algorithms to personalize and recognize the outputs of the information resources according to the users' needs. The Web is represented as three aspects: Content, Structure, and Usage. Three components can lead to a personalized Web in order to reinforce the semantic value. This paper will present a model that uses new Web Usage information to see the effects on the semantic values, and how it will help us achieve a robust well personalized and organized Web. It will consider the Usage space as the field of our research as we will simulate this environment in the MAS ?Multi Agents System? and CAS ?Complex Adaptive System ?paradigm.
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Kocaballi, Ahmet Baki, Shlomo Berkovsky, Juan C. Quiroz, Liliana Laranjo, Huong Ly Tong, Dana Rezazadegan, Agustina Briatore, and Enrico Coiera. "The Personalization of Conversational Agents in Health Care: Systematic Review." Journal of Medical Internet Research 21, no. 11 (November 7, 2019): e15360. http://dx.doi.org/10.2196/15360.

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Background The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. Methods We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. Results The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. Conclusions Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.
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Savinova, A. R., and I. G. Gataullin. "Personalized approach to the ovarian cancer treatment." Kazan medical journal 97, no. 3 (June 15, 2016): 388–93. http://dx.doi.org/10.17750/kmj2016-388.

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The aim of the review is to summarize the modern views on etiopathogenesis and treatment of ovarian cancer, as well as to search for ways for a more personalized approach to the management of patients with this oncological disease. Despite the limited number of patients participating in clinical trials of ovarian cancer, there are obvious results of gradual evolution in its diagnosis and treatment. Studies of etiopathogenesis led to a better understanding of the ovarian cancer genesis mechanisms, whereas evolution in treatment was marked by combination of secondary cytoreductive surgery with the most effective chemotherapeutic drugs and biological agents. The introduction of biomarkers, particularly CA125 (cancer antigen 125), in the diagnostic algorithm for both primary and recurrent ovarian cancer has opened up new horizons for the application of effective methods of treatment at the disease earliest stages. However, in the landmark study of G.J. Rustin et al. there was no a statistically significant difference in disease-free and overall survival among female patients with immediate treatment of recurrent ovarian cancer based only on elevated CA125 and female patients in whom chemotherapy was initiated after the clinical symptoms manifestation. Conducting clinical trials in small cohorts of patients with certain ovarian cancer histotype will help to select one or another effective combination of chemotherapy and/or biological agents administered not only intravenously, but also intraperitoneally, and thereby provide the personalized approach to the treatment of this disease.
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Snoch, Wojciech, Dawid Wnuk, Tomasz Witko, Jakub Staroń, Andrzej J. Bojarski, Ewelina Jarek, Francisco J. Plou, and Maciej Guzik. "In Search of Effective Anticancer Agents—Novel Sugar Esters Based on Polyhydroxyalkanoate Monomers." International Journal of Molecular Sciences 22, no. 13 (July 5, 2021): 7238. http://dx.doi.org/10.3390/ijms22137238.

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Cancer is one of the deadliest illness globally. Searching for new solutions in cancer treatments is essential because commonly used mixed, targeted and personalized therapies are sometimes not sufficient or are too expensive for common patients. Sugar fatty acid esters (SFAEs) are already well-known as promising candidates for an alternative medical tool. The manuscript brings the reader closer to methods of obtaining various SFAEs using combined biological, chemical and enzymatic methods. It presents how modification of SFAE’s hydrophobic chains can influence their cytotoxicity against human skin melanoma and prostate cancer cell lines. The compound’s cytotoxicity was determined by an MTT assay, which followed an assessment of SFAEs’ potential metastatic properties in concentrations below IC50 values. Despite relatively high IC50 values (63.3–1737.6 μM) of the newly synthesized SFAE, they can compete with other sugar esters already described in the literature. The chosen bioactives caused low polymerization of microtubules and the depolymerization of actin filaments in nontoxic levels, which suggest an apoptotic rather than metastatic process. Altogether, cancer cells showed no propensity for metastasis after treating them with SFAE. They confirmed that lactose-based compounds seem the most promising surfactants among tested sugar esters. This manuscript creates a benchmark for creation of novel anticancer agents based on 3-hydroxylated fatty acids of bacterial origin.
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Abdulrahman, Amal, and Deborah Richards. "In Search of Embodied Conversational and Explainable Agents for Health Behaviour Change and Adherence." Multimodal Technologies and Interaction 5, no. 9 (September 18, 2021): 56. http://dx.doi.org/10.3390/mti5090056.

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Conversational agents offer promise to provide an alternative to costly and scarce access to human health providers. Particularly in the context of adherence to treatment advice and health behavior change, they can provide an ongoing coaching role to motivate and keep the health consumer on track. Due to the recognized importance of face-to-face communication and establishment of a therapist-patient working alliance as the biggest single predictor of adherence, our review focuses on embodied conversational agents (ECAs) and their use in health and well-being interventions. The article also introduces ECAs who provide explanations of their recommendations, known as explainable agents (XAs), as a way to build trust and enhance the working alliance towards improved behavior change. Of particular promise, is work in which XAs are able to engage in conversation to learn about their user and personalize their recommendations based on their knowledge of the user and then tailor their explanations to the beliefs and goals of the user to increase relevancy and motivation and address possible barriers to increase intention to perform the healthy behavior.
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Nebbioso, Marcella, Alessandro Lambiase, Alberto Cerini, Paolo Giuseppe Limoli, Maurizio La Cava, and Antonio Greco. "Therapeutic Approaches with Intravitreal Injections in Geographic Atrophy Secondary to Age-Related Macular Degeneration: Current Drugs and Potential Molecules." International Journal of Molecular Sciences 20, no. 7 (April 4, 2019): 1693. http://dx.doi.org/10.3390/ijms20071693.

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The present review focuses on recent clinical trials that analyze the efficacy of intravitreal therapeutic agents for the treatment of dry age-related macular degeneration (AMD), such as neuroprotective drugs, and complement inhibitors, also called immunomodulatory or anti-inflammatory agents. A systematic literature search was performed to identify randomized controlled trials published prior to January 2019. Patients affected by dry AMD treated with intravitreal therapeutic agents were included. Changes in the correct visual acuity and reduction in geographic atrophy progression were evaluated. Several new drugs have shown promising results, including those targeting the complement cascade and neuroprotective agents. The potential action of the two groups of drugs is to block complement cascade upregulation of immunomodulating agents, and to prevent the degeneration and apoptosis of ganglion cells for the neuroprotectors, respectively. Our analysis indicates that finding treatments for dry AMD will require continued collaboration among researchers to identify additional molecular targets and to fully interrogate the utility of pluripotent stem cells for personalized therapy.
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Dissertations / Theses on the topic "Personalised Search Agents"

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Gopinathan-Leela, Ligon, and n/a. "Personalisation of web information search: an agent based approach." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20060728.120849.

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The main purpose of this research is to find an effective way to personalise information searching on the Internet using middleware search agents, namely, Personalised Search Agents (PSA). The PSA acts between users and search engines, and applies new and existing techniques to mine and exploit relevant and personalised information for users. Much research has already been done in developing personalising filters, as a middleware technique which can act between user and search engines to deliver more personalised results. These personalising filters, apply one or more of the popular techniques for search result personalisation, such as the category concept, learning from user actions and using metasearch engines. By developing the PSA, these techniques have been investigated and incorporated to create an effective middleware agent for web search personalisation. In this thesis, a conceptual model for the Personalised Search Agent is developed, implemented by developing a prototype and benchmarked the prototype against existing web search practices. System development methodology which has flexible and iterative procedures that switch between conceptual design and prototype development was adopted as the research methodology. In the conceptual model of the PSA, a multi-layer client server architecture is used by applying generalisation-specialisation features. The client and the server are structurally the same, but differ in the level of generalisation and interface. The client handles personalising information regarding one user whereas the server effectively combines the personalising information of all the clients (i.e. its users) to generate a global profile. Both client and server apply the category concept where user selected URLs are mapped against categories. The PSA learns the user relevant URLs both by requesting explicit feedback and by implicitly capturing user actions (for instance the active time spent by the user on a URL). The PSA also employs a keyword-generating algorithm, and tries different combinations of words in a user search string by effectively combining them with the relevant category values. The core functionalities of the conceptual model for the PSA, were implemented in a prototype, used to test the ideas in the real word. The result was benchmarked with the results from existing search engines to determine the efficiency of the PSA over conventional searching. A comparison of the test results revealed that the PSA is more effective and efficient in finding relevant and personalised results for individual users and possesses a unique user sense rather than the general user sense of traditional search engines. The PSA, is a novel architecture and contributes to the domain of knowledge web information searching, by delivering new ideas such as active time based user relevancy calculations, automatic generation of sensible search keyword combinations and the implementation of a multi-layer agent architecture. Moreover, the PSA has high potential for future extensions as well. Because it captures highly personalised data, data mining techniques which employ case-based reasoning make the PSA a more responsive, more accurate and more effective tool for personalised information searching.
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Lin, Den-Yuh, and 林典諭. "Personalized Paper Search Agent." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/27184850534736828333.

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碩士
國立交通大學
資訊科學系
88
Paper search service is an important tool on World Wide Web which is very handy for scholars, students, and researchers when they need to search for papers . Currently there are plenty of engines in Internet, and each of them has different interface and characteristic. Since single paper search engine can only provide us partial results, we need to integrate several paper search engines that are in different domains and areas to provide more results for users. Accordingly, in this thesis we propose an Internet Paper Search Service (IPSS) based on the Common Object Request Broker Architecture (CORBA) - an industrial standard of distributed object technology announced by OMG. We follow the style of the Common Object Service Specification (COSS) to define the interface of IPSS, so that we can easily integrate a new paper search engine into paper meta-search services. Not only other application programs can easily request the search service via IPSS, but IPSS can be applied to different search services. Besides, we also provide personalized search service for users so that they can have more choices and functions on paper search. Finally, we integrate two paper search engines into our system; one for ACM, and the other for IDEAL.
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Oosthuizen, Ockmer Louren. "A multi-agent collaborative personalized web mining system model." Thesis, 2008. http://hdl.handle.net/10210/508.

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The Internet and world wide web (WWW) have in recent years, grown exponentially in size and in terms of the volume of information that is available on it. In order to effectively deal with the huge amount of information on the web, so called web search engines have been developed for the task of retrieving useful and relevant information for its users. Unfortunately, these web search engines have not kept pace with the boom growth and commercialization of the web. The main goal of this dissertation is the development of a model for a collaborative personalized meta-search agent (COPEMSA) system for the WWW. This model will enable the personalization of web search for users. Furthermore, the model aims to leverage on current search engines on the web as well as enable collaboration between users of the search system for the purposes of sharing useful resources between them. The model also employs the use of multiple intelligent agents and web content mining techniques. This enables the model to autonomously retrieve useful information for it’s user(s) and present this information in an effective manner. In order to achieve the above stated, the COPEMSA model employs the use of multiple intelligent agents. COPEMSA consists of five core components: a user agent, a query agent, a community agent, a content mining agent and a directed web spider. The user agent learns about the user in order to introduce personal preference into user queries. The query agent is a scaled down meta-search engine with the task of submitting the personalized queries it receives from the user agent to multiple search services on theWWW. The community agent enables the search system to communicate and leverage on the search experiences of a community of searchers. The content mining agent is responsible for analysis of the retrieved results from theWWWand the presentation of these results to the system user. Finally, a directed web spider is used by the content mining agent to retrieve the actual web pages it analyzes from the WWW. In this dissertation an additional model is also presented to deal with a specific problem all web spidering software must deal with namely content and link encapsulation.
Prof. E.M. Ehlers
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Book chapters on the topic "Personalised Search Agents"

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Verma, Disha, Barjesh Kochar, and Y. S. Shishodia. "Optimized Multi-agent Personalized Search Engine." In Advances in Intelligent Systems and Computing, 451–64. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1951-8_41.

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De Pauw, Ines, Carolien Boeckx, and An Wouters. "Mechanisms of Cetuximab Resistance and How to Overcome It." In Critical Issues in Head and Neck Oncology, 21–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63234-2_3.

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AbstractDeregulated or increased signalling of the epidermal growth factor receptor (EGFR) plays an integral role in the development of various cancer types, including head and neck squamous cell carcinoma (HNSCC), making it a compelling drug target. However, after initially promising results of EGFR-targeted therapies, such as the monoclonal antibody cetuximab, it became clear that both intrinsic and acquired therapeutic resistance are major roadblocks in the field of personalised cancer treatments.In order to unravel and overcome resistance to cetuximab, at least two strategies can be adopted.Firstly, therapeutic resistance to anti-EGFR therapy may arise from mechanisms that can compensate for reduced EGFR signalling and/or mechanisms that can modulate EGFR-dependent signalling. In this chapter, we discuss which mechanisms of cetuximab resistance are already known and which ones deserve further investigation. This enhanced knowledge will guide us to rationally design and test novel combination therapies that overcome resistance to EGFR-targeting agents in cancer treatment.Secondly, an urgent need remains to develop novel targeted treatments for single-agent or combined therapy use. In this view, due to the particular mode of activation of the EGFR receptor, involving ligand-induced homo- and heterodimerization of the four HER receptors, an increased inhibition scope of HER receptors most likely results in a more potent blockade of the HER network, preventing premature emergence of resistance and leading to a more pronounced therapeutic benefit. We discuss two multitargeted compounds, being MEHD7945A (duligotuzumab) and afatinib, in this chapter.Despite the huge efforts to unravel the molecular landscape of HNSCC, the main clinically validated target remains EGFR. However, immune checkpoints, like programmed cell death protein 1 (PD-1), are gaining clinical approvals as well. We underscore the importance of adopting rational drug combinations to enhance the therapeutic effect of the EGFR-inhibitor cetuximab and highlight the ongoing search for predictive biomarkers, with the ultimate goal of delivering individualized cancer therapy to HNSCC patients.
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Tang, Yu, Yan-Qing Zhang, Abraham Kandel, T. Y. Lin, and Y. Y. Yao. "Personalized Search Agents Using Data Mining and Granular Fuzzy Techniques." In Enhancing the Power of the Internet, 207–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45218-8_9.

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Ho, Min-Huang, Yue-Shan Chang, Shyan-Ming Yuan, and Winston Lo. "An Agent-Based Personalized Search on a Multi-search Engine Based on Internet Search Service." In Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents, 404–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44491-2_59.

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Semeraro, Giovanni, Marco Degemmis, Pasquale Lops, Ulrich Thiel, and Marcello L’Abbate. "A Personalized Information Search Process Based on Dialoguing Agents and User Profiling." In Lecture Notes in Computer Science, 613–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36618-0_49.

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Casali, Ana, Valeria Gerling, Claudia Deco, and Cristina Bender. "A Recommender System for Learning Objects Personalized Retrieval." In Educational Recommender Systems and Technologies, 182–210. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-489-5.ch008.

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This chapter describes the development of a recommender system of learning objects. This system helps a user to find educational resources that are most appropriate to his/her needs and preferences. The search is performed in different repositories of learning objects, where each object has descriptive metadata. Metadata is used to retrieve objects that satisfy not only the subject of the query, but also the user profile, taking into account his/her characteristics and preferences. A multi-agent architecture that includes several types of agents with different functionalities is used. In this chapter, we describe the modelization of the Personalized Search Agent (PS-Agent) as a graded BDI (Belief-Desire-Intention) agent. This agent is responsible for making a flexible content-based retrieval and provides an ordered list of the resources that better meet the user profile data. A prototype was implemented, and experimentation results are presented.
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Helmy, Tarek, and Ahmed Al-Nazer. "Personalized Web Services Selection." In Developing Advanced Web Services through P2P Computing and Autonomous Agents, 114–32. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-973-6.ch007.

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Web services have gained an increasing popularity over the Internet. Because of today’s wide variety of services offered to perform a specific task. The task of finding selected Web services to perform a specific task becomes very hard, and it is essential that users are supported in the eventual selection of appropriate services. Web services are a great application area for agent techniques and a great substrate for developing serious autonomous agent-based systems to support a personalized Web services selection. In this chapter, we present a Collaborative Autonomous Interface Agent (CAIA) that collaborates with the Internet search engines and supports the user in finding exactly the Web services consistent with his/her needs. CAIA system has been designed, fully implemented and tested. As a case study, the testing results show a big improvement in the relevancy of the retrieved results and of the user’s satisfaction by using CAIA+Google compared to using Google only.
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Singh, Aarti, and Anu Sharma. "Web Semantics for Personalized Information Retrieval." In Web Semantics for Textual and Visual Information Retrieval, 166–86. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2483-0.ch008.

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This chapter explores the synergy between Semantic Web (SW) technologies and Web Personalization (WP) for demonstrating an intelligent interface for Personalized Information Retrieval (PIR) on web. Benefits of adding semantics to WP through ontologies and Software Agents (SA) has already been realized. These approaches are expected to prove useful in handling the information overload problem encountered in web search. A brief introduction to PIR process is given, followed by description of SW, ontologies and SA. A comprehensive review of existing web technologies for PIR has been presented. Although, a huge contribution by various researchers has been seen and analyzed but still there exist some gap areas where the benefits of these technologies are still to be realized in future personalized web search.
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Singh, Aarti, and Anu Sharma. "Web Semantics for Personalized Information Retrieval." In Information Retrieval and Management, 795–810. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch034.

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This chapter explores the synergy between Semantic Web (SW) technologies and Web Personalization (WP) for demonstrating an intelligent interface for Personalized Information Retrieval (PIR) on web. Benefits of adding semantics to WP through ontologies and Software Agents (SA) has already been realized. These approaches are expected to prove useful in handling the information overload problem encountered in web search. A brief introduction to PIR process is given, followed by description of SW, ontologies and SA. A comprehensive review of existing web technologies for PIR has been presented. Although, a huge contribution by various researchers has been seen and analyzed but still there exist some gap areas where the benefits of these technologies are still to be realized in future personalized web search.
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Ovalle, Demetrio, Oscar Salazar, and Néstor Duque. "Ubiquitous Multi-Agent Context-Aware System for Enhancing Teaching-Learning Processes Adapted to Student Profile." In Technology Platform Innovations and Forthcoming Trends in Ubiquitous Learning, 237–53. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4542-4.ch013.

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The need for ubiquitous systems that allow access to computer systems from anywhere at anytime and the massive use of the Internet has prompted the creation of e-learning systems that can be accessed from mobile smart phones, PDA, or tablets, taking advantage of the current growth of mobile technologies. The aim of this chapter is to present the advantages brought by the integration of ubiquitous computing-oriented along with distributed artificial intelligence techniques in order to build student-centered context-aware learning systems. Based on this model, the authors propose a multi-agent context-aware u-learning system that offers several functionalities such as context-aware learning planning, personalized course evaluation, selection of learning objects according to student’s profile, search of learning objects in repository federations, search of thematic learning assistants, and access of current context-aware collaborative learning activities involved. Finally, the authors present some solutions considering the functionalities that a u-learning multi-agent context-aware system should exhibit.
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Conference papers on the topic "Personalised Search Agents"

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Marcialis, Ivan, and Emanuela De Vita. "SEARCHY: An Agent to Personalize Search Results." In 2008 3rd International Conference on Intenet and Web Applications and Services. IEEE, 2008. http://dx.doi.org/10.1109/iciw.2008.86.

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Liu, Qianlong, Baoliang Cui, Zhongyu Wei, Baolin Peng, Haikuan Huang, Hongbo Deng, Jianye Hao, Xuanjing Huang, and Kam-Fai Wong. "Building Personalized Simulator for Interactive Search." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/710.

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Interactive search, where a set of tags is recommended to users together with search results at each turn, is an effective way to guide users to identify their information need. It is a classical sequential decision problem and the reinforcement learning based agent can be introduced as a solution. The training of the agent can be divided into two stages, i.e., offline and online. Existing reinforcement learning based systems tend to perform the offline training in a supervised way based on historical labeled data while the online training is performed via reinforcement learning algorithms based on interactions with real users. The mis-match between online and offline training leads to a cold-start problem for the online usage of the agent. To address this issue, we propose to employ a simulator to mimic the environment for the offline training of the agent. Users' profiles are considered to build a personalized simulator, besides, model-based approach is used to train the simulator and is able to use the data efficiently. Experimental results based on real-world dataset demonstrate the effectiveness of our agent and personalized simulator.
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Xiao, Mengqiang, and Fengqing Zhang. "A Multi-Agent Based Personalized Search Engine." In 2010 2nd International Conference on Information Technology and Computer Science (ITCS 2010). IEEE, 2010. http://dx.doi.org/10.1109/itcs.2010.130.

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Qing Wang and Fei Wang. "Personalized semantic search agent interaction mode research." In 2010 IEEE 11th International Conference on Computer-Aided Industrial Design & Conceptual Design 1. IEEE, 2010. http://dx.doi.org/10.1109/caidcd.2010.5681995.

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Palleti, Pallavi, Harish Karnick, and Pabitra Mitra. "Personalized Web Search Using Probabilistic Query Expansion." In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wiiatw.2007.4427545.

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Palleti, Pallavi, Harish Karnick, and Pabitra Mitra. "Personalized Web Search Using Probabilistic Query Expansion." In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wi-iatw.2007.44.

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Zhuhadar, Leyla, and Olfa Nasraoui. "Personalized Search Based on a User-Centered Recommender Engine." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.296.

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Yan Chen, HaiLong Hou, and Yan-Qing Zhang. "A personalized context-dependent Web search agent using Semantic Trees." In NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2008. http://dx.doi.org/10.1109/nafips.2008.4531347.

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Hoeber, Orland, and Hanze Liu. "Comparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.42.

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Liu, Hanze, and Orland Hoeber. "A Luhn-Inspired Vector Re-weighting Approach for Improving Personalized Web Search." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.130.

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