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Relevant bibliographies by topics / Personalized predictive medicine

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Academic literature on the topic 'Personalized predictive medicine'

Author: Grafiati

Published: 2 November 2024

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Journal articles on the topic "Personalized predictive medicine"

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Nassimbwa, Kabanda D. "The Role of Biomarkers in Personalized Cancer Treatment." RESEARCH INVENTION JOURNAL OF PUBLIC HEALTH AND PHARMACY 3, no. 2 (2024): 3–33. http://dx.doi.org/10.59298/rijpp/2024/323033.

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Biomarkers are vital instruments in modern medicine, providing information about biological processes and disease progression. Biomarkers in oncology have had a substantial impact on the development of personalised cancer treatments by predicting therapy responses and outcomes. The study investigates the different types of biomarkers, such as diagnostic, prognostic, and predictive biomarkers, and their function in precision medicine. Personalised cancer treatment, which is guided by biomarker testing, improves patient outcomes by adapting medicines to individual genetic profiles. However, obst

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Giglia, Giuseppe, Giuditta Gambino, and Pierangelo Sardo. "Through Predictive Personalized Medicine." Brain Sciences 10, no. 9 (2020): 594. http://dx.doi.org/10.3390/brainsci10090594.

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Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validat

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Reena Dhan, Archana, and Binod Kumar. "Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1307–13. http://dx.doi.org/10.21275/mr24608013906.

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Farooq, Faisal, Balaji Krishnapuram, Romer Rosales, Shipeng Yu, Jude Shavlik, and Raju Kucherlapati. "Predictive Models in Personalized Medicine." ACM SIGHIT Record 1, no. 1 (2011): 23–25. http://dx.doi.org/10.1145/1971706.1971714.

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DOSAY-AKBULUT, Mine. "A Review on Determination and Future of the Predictive and Personalized Medicine." International Journal of Biology 8, no. 1 (2015): 32. http://dx.doi.org/10.5539/ijb.v8n1p32.

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<p class="1Body">Medicine contents’ have extended to predictive, personalized, preventive and participatory medicine (P4). ‘Personalized medicine' focuses on the prediction of potential benefits or risks for individuals as possible as in detailed. Biomarker discovery, biocomputing and nanotechnology have opened a new horizon on ‘personalized medicine’ (including disease detection, diagnosis and therapy by using individual's molecular profile) and ‘predictive medicine’ (to predict disease development, progression and clinical outcome, by using the genetic and molecular information).</p

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Nie, Shuming. "Nanotechnology for personalized and predictive medicine." Nanomedicine: Nanotechnology, Biology and Medicine 2, no. 4 (2006): 305. http://dx.doi.org/10.1016/j.nano.2006.10.115.

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Workman, Paul, Paul A. Clarke, and Bissan Al-Lazikani. "Personalized Medicine: Patient-Predictive Panel Power." Cancer Cell 21, no. 4 (2012): 455–58. http://dx.doi.org/10.1016/j.ccr.2012.03.030.

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Rizvi, S. Mohd Shiraz, Farzana Mahdi, Abbas Ali Mahdi, Tabrez Jafar, and Saliha Rizvi. "PERSONALIZED MEDICINE: ROLE OF ASYMMETRIC DIMETHYLARGININE AS A PREDICTIVE MARKER OF CAD." Era's Journal of Medical Research 7, no. 1 (2020): 86–91. http://dx.doi.org/10.24041/ejmr2020.15.

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Mathieu, Thierry, Laurent Bermont, Jean-Christophe Boyer, et al. "Lexical fields of predictive and personalized medicine." Annales de biologie clinique 70, no. 6 (2012): 651–58. http://dx.doi.org/10.1684/abc.2012.0767.

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Martin, Greg, and Dean Jones. "The Road to Personalized and Predictive Medicine." American Journal of Respiratory and Critical Care Medicine 188, no. 2 (2013): 257. http://dx.doi.org/10.1164/rccm.201212-2248le.

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Dissertations / Theses on the topic "Personalized predictive medicine"

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Bragazzi, Nicola Luigi [Verfasser], and Norbert [Akademischer Betreuer] Hampp. "Nanogenomics and Nanoproteomics Enabling Personalized, Predictive and Preventive Medicine / Nicola Luigi Bragazzi. Betreuer: Norbert Hampp." Marburg : Philipps-Universität Marburg, 2014. http://d-nb.info/1051935334/34.

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Park, Keon-Young. "Predicting patient-to-patient variability in proteolytic activity and breast cancer progression." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53479.

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About one in eight women in the United States will develop breast cancer over the course of her lifetime. Moreover, patient-to-patient variability in disease progression continues to complicate clinical decisions in diagnosis and treatment for breast cancer patients. Early detection of tumors is a key factor influencing patient survival, and advancements in diagnostic and imaging techniques has allowed clinicians to spot smaller sized lesions. There has also been an increase in premature treatments of non-malignant lesions because there is no clear way to predict whether these lesions will bec

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Cheng, Chih-Wen. "Development of integrated informatics analytics for improved evidence-based, personalized, and predictive health." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54872.

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Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data

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IANZA, ANNA. "VALIDATION OF PREDICTIVE AND PROGNOSTIC BIOMARKERS AS A GUIDE FOR A PERSONALIZED APPROACH IN SOLID TUMOURS." Doctoral thesis, Università degli Studi di Trieste, 2020. http://hdl.handle.net/11368/2973745.

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Breast cancer (BC), Colorectal Cancer (CRC) and Non-Small Cell Lung Cancer (NSCLC) are among the most commonly diagnosed solid tumors, and occupy the first places in the mortality rankings. Compared to an old fashioned one-size-fits-all approach, precision medicine offers the possibility to accurately choose the most appropriate therapeutic strategy, that fits the patients not only from the clinical (age, comorbidities) but also from a molecular point of view. A genetic and biological understanding of the tumor, integrated with a weighted analysis of results can help the clinician in designing

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Wang, Hao. "Screening multi-omics biomarkers for suboptimal health status." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2431.

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Background Suboptimal health status (SHS) is an intermediate health status between ideal health and diseases. It is characterized by chronic fatigue, perception of health complaints and a cluster of physical symptoms lasting for more than three months. SHS is recognised as a subclinical, reversible stage of chronic diseases. Objectives Study I. To investigate the prevalence of SHS in a cross-sectional study. Study II. To screen transcriptomic biomarkers for SHS in a case-control study. Study III. To screen metabolomics biomarkers for SHS in a case-control study. Materials and Methods Study

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Shen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.

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In personalized medicine, two important tasks are predicting disease risk and selecting appropriate treatments for individuals based on their baseline information. The dissertation focuses on providing improved risk prediction for ordinal outcome data and proposing score-based test to identify informative markers for treatment selection. In Chapter 1, we take up the first problem and propose a disease risk prediction model for ordinal outcomes. Traditional ordinal outcome models leave out intermediate models which may lead to suboptimal prediction performance; they also don't allow for non-lin

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Reggiani, Francesco. "Development and assessment of bioinformatics methods for personalized medicine." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424693.

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The human genome is a source of information for researchers that study complex diseases with the perspective of a better understanding of the pathologies and the development of new therapeutic strategies. Starting from the beginning of the current century, a growing number of technologies devoted to DNA sequencing have emerged, generally referred to as Next Generation Sequencing (NGS) technologies. NGS gradually decreased the cost of sequencing a human genome to around US$1000, enabling the use of these technologies for clinical and research purposes, such as Genome-wide association studies (G

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Alderdice, Matthew. "Personalised medicine in rectal cancer : understanding and predicting response to neoadjuvant chemoradiotherapy." Thesis, Queen's University Belfast, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725327.

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Around 12-15% of patients with locally advanced rectal cancer (LARC) undergo a pathologically complete response (Tumour Regression Grade 4 - TRG4) to neoadjuvant chemoradiotherapy; the remainder exhibit a spectrum of tumour regression (TRG1-3). Understanding therapy-related genomic alterations may help us better predict response, progression-free and overall survival, and also identify both novel and repurposed treatment strategies based on the underlying biology of the disease. The Northern Ireland Biobank provided 48 formalin fixed paraffin embedded (FFPE) rectal cancer biopsies and matched

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Alcenat, Stéphane. "Assurance maladie et tests génétiques." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCB002.

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Cette thèse apporte trois contributions majeures. Le premier chapitre, un article paru dans la Revue Française d’Économie n°2/vol XXXIV, propose une revue de la littérature sur les implications de la régulation des tests génétiques de prédisposition sur le marché de l'assurance santé. Nous montrons que les formes actuelles de régulation réalisent un arbitrage entre la maximisation du bien-être social ex ante et l’incitation à la prévention. Cet arbitrage est conditionné par la façon dont l’acquisition de l’information affecte les comportements de prévention et de révélation des agents, la disc

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Cornec-Le, Gall Emilie. "Polykystose rénale autosomique dominante : de la génétique moléculaire au développement d'outils pronostiques." Thesis, Brest, 2015. http://www.theses.fr/2015BRES0030.

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La Polykystose Rénale Autosomique Dominante (PKRAD) est une des pathologies héréditaires les plus fréquentes et affecte environ un individu sur 1000. Elle se caractérise par une importante variabilité clinique, notamment dans l’âge de survenue de l’insuffisance rénale terminale. Deux gènes sont en cause : le gène PKD1 situé sur le chromosome 16 (85% des cas) et le gène PKD2 situé sur le chromosome 4 (15% des cas). Les progrès majeurs dans la compréhension des mécanismes moléculaires impliqués ont permis le développement de stratégies thérapeutiques spécifiques, et de nouvelles questions surgis

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Books on the topic "Personalized predictive medicine"

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Olga, Golubnitschaja, ed. Predictive diagnostics and personalized treatment: Dream or reality. Nova Science Publishers, 2009.

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Grech, Godfrey, and Iris Grossman, eds. Preventive and Predictive Genetics: Towards Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15344-5.

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Podbielska, Halina, and Marko Kapalla, eds. Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6.

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Chaari, Lotfi, ed. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11800-6.

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Chaari, Lotfi, ed. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3.

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Berliner, Leonard, and Heinz U. Lemke, eds. An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6.

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Hood, Leroy, and Nathan Price. Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

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Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

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Age of Scientific Wellness - Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

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Mansnérus, Juli, Raimo Lahti, and Amanda Blick, eds. Personalized medicine: Legal and ethical challenges. University of Helsinki, Faculty of Law, 2020. http://dx.doi.org/10.31885/9789515169419.

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This anthology deals with the legal and ethical challenges regarding personalized (precision) medicine and healthcare. It can also be regarded as the final report of a research project on the legal and ethical aspects of personalized medicine. It complements the reported results of the consortium ‘Personalised medicine to predict and prevent Type 1 Diabetes (P4 Diabetes)’ which were briefly presented in the booklet entitled ‘Better, Smarter, Now: Personalised Health – From Genes to Society (pHealth)’, Academy of Finland, Helsinki 2019. The articles of this anthology are not limited to the aspe

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Book chapters on the topic "Personalized predictive medicine"

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Richter, Kneginja, Nikola Gjorgov, and Stojan Bajraktarov. "Predictive, Preventive, and Personalized Approach in Sleep Medicine." In Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6_14.

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Regierer, Babette, Valeria Zazzu, Ralf Sudbrak, Alexander Kühn, and Hans Lehrach. "Future of Medicine: Models in Predictive Diagnostics and Personalized Medicine." In Molecular Diagnostics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/10_2012_176.

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Hazin, Hesham, and David Dosik. "Personalized Chemotherapy for Hepatocellular Carcinoma." In An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6_6.

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Andrews, Russell J. "Wearable Revolution: Predictive, Preventive, Personalized Medicine (PPPM) Par Excellence." In Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6_19.

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Kapalla, Marko, Dagmar Kapallová, and Ladislav Turecký. "Healthcare Overview in the Slovak Republic and Implementation of Predictive, Preventive and Personalized Medicine." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_5.

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Brown, Paul M. "Effectiveness, Cost Effectiveness, and Financial Viability of Personalized Medicine: A Role for Comparative Effectiveness Research?" In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_21.

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Marcus-Kalish, Mira, and Hamutal Meiri. "Simultaneous Systematic Approach to Enable Predictive, Preventive and Personalized Medicine – Women Healthcare as a Case Study." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_17.

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Trovato, Guglielmo M., and Francesco Basile. "Italian Healthcare System in the Global Context: The Cultural Challenge of Predictive, Preventive and Personalized Medicine." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_2.

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Kugler, Andrea, Chiara Kertu, and Kurt Krapfenbauer. "The Economic Challenge of Predictive, Preventive and Personalized Medicine: The Case Study of Lung, Head and Neck Cancer." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_22.

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Kinkorová, Judita, and Ondřej Topolčan. "An Overview of the Healthcare System in the Czech Republic with Respect to Predictive, Preventive and Personalized Medicine." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_6.

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Conference papers on the topic "Personalized predictive medicine"

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Lella, Luigi, Ignazio Licata, Gianfranco Minati, Christian Pristipino, Antonio De Belvis, and Roberta Pastorino. "Predictive AI Models for the Personalized Medicine." In 12th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007472203960401.

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Hood, Lee. "Systems medicine, transformational technologies and the emergence of predictive, personalized, preventive and participatory (P4) medicine." In the Conference. ACM Press, 2009. http://dx.doi.org/10.1145/1654059.1657026.

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Hood, Leroy. "“Systems biology and systems medicine: From reactive to predictive, personalized, preventive and participatory (P4) medicine”." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649061.

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Affan, Affan, Jacek M. Zurada, and Tamer Inane. "Patient-Specific Modeling and Model Predictive Control Approach to Personalized Optimal Anemia Management." In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2023. http://dx.doi.org/10.1109/embc40787.2023.10340171.

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Zdereva, E. A., M. Tsyganov, N. V. Litvyakov, and M. K. Ibragimova. "PREDICTIVE AND PROGNOSTIC SIGNIFICANCE OF EXPRESSION AND ABERRATIONS OF THE DNA COPY NUMBER OF CHEMOSENSITIVITY GENES IN PATIENTS WITH BREAST CANCER." In I International Congress “The Latest Achievements of Medicine, Healthcare, and Health-Saving Technologies”. Kemerovo State University, 2023. http://dx.doi.org/10.21603/-i-ic-42.

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Increasingly, researchers are focusing on the susceptibility of breast tumors (BC) to certain chemotherapy drugs and personalizing studies based on an assessment of this susceptibility. One such personalized approach is the assessment of chemotherapy gene expression and aberrations in the number of DNA copies — deletions and amplifications that affect gene activity. Comprehensive assessment of gene expression of chemotherapy drugs is important not only for understanding the heterogeneity and molecular biology of breast cancer, but also for obtaining a more accurate prognosis of the disease. Th

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Cyganov, M. M., M. K. Ibragimova, and A. A. Hozyainova. "PREDICTIVE AND PROGNOSTIC SIGNIFICANCE OF PALB2 GENE MUTATIONS IN BREAST TUMORS." In I International Congress “The Latest Achievements of Medicine, Healthcare, and Health-Saving Technologies”. Kemerovo State University, 2023. http://dx.doi.org/10.21603/-i-ic-146.

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It has been shown that the loss of PALB2 function, due to mutations or chromosomal aberrations, may have an impact on the effectiveness of chemotherapy treatment and disease prognosis in patients with various oncological diseases. Thus, the aim of this work was to evaluate the predictive and prognostic potential of DNA copy number aberrations and PALB2 gene mutations in breast tumors. Materials and methods. The study included 66 patients with breast cancer (BC). To evaluate the presence of DNA copy number aberrations (CNA), microarray analysis was used

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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.

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Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictio

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Nguyen, Giang T. T., and Duc-Hau Le. "A matrix completion method for drug response prediction in personalized medicine." In the Ninth International Symposium. ACM Press, 2018. http://dx.doi.org/10.1145/3287921.3287974.

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Cojbasic, Zarko. "Machine Learning for Personalized Medicine: Clinical Outcome Prediction and Diagnosis : Plenary Talk." In 2019 IEEE 13th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2019. http://dx.doi.org/10.1109/saci46893.2019.9111519.

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Biswas, Sougatamoy, Vinod Kumar, and Smritilekha Das. "Multiclass classification models for Personalized Medicine prediction based on patients Genetic Variants." In 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES). IEEE, 2021. http://dx.doi.org/10.1109/tribes52498.2021.9751631.

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Reports on the topic "Personalized predictive medicine"

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Manski, Charles. Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29358.

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Zhang, Yu, Chaoliang Sun, Hengxi Xu, et al. Connectivity-Based Subtyping of De Novo Parkinson Disease: Biomarkers, Medication Effects and Longitudinal Progression. Progress in Neurobiology, 2024. http://dx.doi.org/10.60124/j.pneuro.2024.10.04.

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Parkinson's disease (PD) is characterized by divergent clinical symptoms and prognosis, suggesting the presence of distinct subtypes. Identifying these subtypes is crucial for understanding the underlying pathophysiology, predicting disease progression, and developing personalized treatments. In this study, we propose a connectivity-based subtyping approach, which measures each patient's deviation from the reference structural covariance networks established in healthy controls. Using data from the Parkinson's Progression Markers Initiative, we identified two distinct subtypes of de novo PD pa

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