Academic literature on the topic 'Breast cancer prediction'
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Journal articles on the topic "Breast cancer prediction"
Khubchandani, Pratham, Harshith Deeti, and Beeram Harsh. "Breast Cancer Prediction in Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 274–77. http://dx.doi.org/10.22214/ijraset.2022.47870.
Full textAssegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.
Full textKumari, Madhu, and Vijendra Singh. "Breast Cancer Prediction system." Procedia Computer Science 132 (2018): 371–76. http://dx.doi.org/10.1016/j.procs.2018.05.197.
Full textDas, Akhil Kumar, Saroj Kumar Biswas, and Ardhendu Mandal. "An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree." Indian Journal Of Science And Technology 15, no. 45 (December 5, 2022): 2441–50. http://dx.doi.org/10.17485/ijst/v15i45.756.
Full textKumar, Mukesh, Saurabh Singhal, Shashi Shekhar, Bhisham Sharma, and Gautam Srivastava. "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning." Sustainability 14, no. 21 (October 27, 2022): 13998. http://dx.doi.org/10.3390/su142113998.
Full textXiong, Wei, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, and Jiebo Luo. "Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders." BME Frontiers 2022 (April 7, 2022): 1–10. http://dx.doi.org/10.34133/2022/9763284.
Full textPankratz, V. Shane, Amy C. Degnim, Ryan D. Frank, Marlene H. Frost, Daniel W. Visscher, Robert A. Vierkant, Tina J. Hieken, et al. "Model for Individualized Prediction of Breast Cancer Risk After a Benign Breast Biopsy." Journal of Clinical Oncology 33, no. 8 (March 10, 2015): 923–29. http://dx.doi.org/10.1200/jco.2014.55.4865.
Full textDerouane, Françoise, Cédric van Marcke, Martine Berlière, Amandine Gerday, Latifa Fellah, Isabelle Leconte, Mieke R. Van Bockstal, Christine Galant, Cyril Corbet, and Francois P. Duhoux. "Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine." Cancers 14, no. 16 (August 11, 2022): 3876. http://dx.doi.org/10.3390/cancers14163876.
Full textYe, Guolin, Suqun He, Ruilin Pan, Lewei Zhu, Dan Zhou, and RuiLiang Lu. "Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction." Journal of Healthcare Engineering 2022 (February 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/4477099.
Full textMcCarthy, Anne Marie, Zoe Guan, Michaela Welch, Molly E. Griffin, Dorothy A. Sippo, Zhengyi Deng, Suzanne B. Coopey, et al. "Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort." JNCI: Journal of the National Cancer Institute 112, no. 5 (September 26, 2019): 489–97. http://dx.doi.org/10.1093/jnci/djz177.
Full textDissertations / Theses on the topic "Breast cancer prediction"
Pawluczyk, Olga. "Volumetric estimation of breast density for breast cancer risk prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58694.pdf.
Full textRipley, Ruth Mary. "Neural network models for breast cancer prognosis." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244721.
Full textJerevall, Piiha-Lotta. "Homeobox B13 in breast cancer : Prediction of tamoxifen benefit." Doctoral thesis, Linköpings universitet, Onkologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68137.
Full textOh, Daniel S. Perou Charles M. "Prediction of outcome in breast cancer patients using gene expression profiling." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,501.
Full textTitle from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum in Genetics and Molecular Biology." Discipline: Genetics and Molecular Biology; Department/School: Medicine.
Ahlgren, Johan. "Studies on Prediction of Axillary Lymph Node Status in Invasive Breast Cancer." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5221-3/.
Full textMojarad, Ameiryan Shirin. "A reliable neural network-based decision support system for breast cancer prediction." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1738.
Full textLouro, Aldamiz-Echevarría Javier. "Individualized breast cancer risk prediction models applied to population-based screening mammography." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673964.
Full textIntroducción: Se ha demostrado que el cribado mamográfico reduce la mortalidad por cáncer de mama. Siguiendo las recomendaciones de la Comisión Europea, los países europeos han establecido programas poblacionales de cribado que ofrecen mamografías bienales a mujeres de entre 50 y 69 años de edad. Sin embargo, el cribado de cáncer de mama no está libre de controversia ya que existe un debate en cuanto al equilibrio entre la reducción de la mortalidad y los efectos adversos. Para mejorar este equilibrio, la evidencia científica actual apoya el cribado personalizado. Los estudios de modelización han demostrado que modificar el intervalo de cribado, la prueba de cribado o el rango de edad de la población objetivo en función del riesgo individual de las mujeres produce un mayor beneficio que las estrategias convencionales. Por lo tanto, es necesario ampliar la información actual sobre los factores de riesgo de esta enfermedad y crear modelos de predicción del riesgo individual mediante el análisis de grandes bases de datos poblacionales. Objetivo: El objetivo general de esta tesis es profundizar en el análisis del cribado poblacional del cáncer de mama. En concreto, esta tesis pretende evaluar diferentes factores de riesgo de cáncer de mama para desarrollar y validar un modelo de predicción de riesgo individual de esta enfermedad. Se analizó cómo la densidad mamaria afecta a los distintos indicadores de cribado en el contexto de la mamografía digital. A continuación, se evaluaron las diferencias en el riesgo de cáncer de mama en función de si una lesión benigna de mama se diagnosticó en un cribado prevalente o un cribado incidente. También se analizó la interacción entre la densidad mamaria y las lesiones benignas en el riesgo de cáncer de mama. Posteriormente, se realizó una revisión sistemática para actualizar la evidencia existente, llevar a cabo una valoración crítica y una evaluación del riesgo de sesgo y resumir los resultados de los modelos de riesgo individualizados que se utilizan para estimar el riesgo de cáncer de mama en las mujeres de la población general. Por último, se diseñó un modelo de predicción individual del riesgo de cáncer de mama y se validó internamente, basado en información fácilmente accesible en un episodio de cribado. Conclusiones: i) Los distintos indicadores de cribado se ven afectados negativamente por la densidad mamaria, disminuyendo la sensibilidad y el valor predictivo positivo de la prueba a medida que aumenta la densidad mamaria. ii) El riesgo de cáncer de mama conferido por una lesión benigna difiere según el tipo de cribado (prevalente o incidente). Hasta donde sabemos, este es el primer estudio que analiza el impacto del tipo de cribado en el pronóstico de la lesión benigna. iii) El riesgo de cáncer de mama aumenta de forma independiente con la presencia de una lesión benigna y con una mayor densidad mamaria y se mantiene elevado durante más de 15 años. iv) Los modelos de predicción son herramientas prometedoras para implementar políticas de cribado basadas en el riesgo individualizado. Sin embargo, es un reto recomendar cualquiera de ellos para la personalización del cribado ya que necesitan mejorar su calidad y capacidad discriminatoria. v) Diseñamos y validamos internamente un modelo de predicción de riesgo capaz de estimar el riesgo de cáncer de mama a corto y largo plazo utilizando la información recogida de forma rutinaria en el cribado mamográfico. El modelo incluye edad, antecedentes familiares de cáncer de mama, antecedentes de lesión benigna y patrones mamográficos previos, que resultaron estar relacionados con un aumento del riesgo de cáncer de mama. El modelo debe ser validado externamente y actualizado con nuevas variables.
Background: Mammographic screening has been shown to reduce mortality from breast cancer. Following the recommendations of the European Council, European countries have started population-based screening programs that offer biennial mammograms to women aged between 50 and 69 years. The results of the effectiveness of population-based screening are controversial in terms of the balance between mortality reduction and adverse effects. To improve this balance, current evidence supports personalized screening. Modeling studies have shown that modifying the screening interval, screening modality, or age range of the target population based on women's individual risk yields a greater benefit than conventional standard strategies. Several risk models have been designed to estimate women's individual breast cancer risk based on their personal characteristics. However, most of these models have not been specifically developed to estimate the risk of women targeted for breast cancer screening. There is therefore a need to broaden current information on risk factors for breast cancer and the estimation of individual risk prediction models through the analysis of large population-based databases. Aims: The general objective of the thesis is to deepen the analysis of population-based breast cancer screening. Specifically, the aim of this thesis is to assess different breast cancer risk factors in order to develop and validate an individualized breast cancer risk prediction model. We evaluated how breast density affects screening performance indicators in a digital mammography context. Then, we assessed differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens. To our knowledge, this is the first time that such an approach has been used. We also evaluated the interaction between breast density and benign breast disease. Subsequently, we performed a systematic review to update the existing evidence, conduct a critical appraisal and risk of bias assessment and summarize the results of the individualized risk models that are used to estimate the risk of breast cancer in women in the general population. Finally, a breast cancer risk prediction model was designed and internally validated, based on information easily accessible at screening. Conclusions: i) Performance screening measures are negatively affected by breast density, with sensitivity and positive predictive value decreasing as breast density increases. ii) The risk of breast cancer conferred by benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyze the impact of screening type on the prognosis of benign breast disease. iii) The risk of breast cancer independently increased with the presence of benign breast disease and with greater breast density and remained elevated for over 15 years. iv) Individualized risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity. v) We designed and internally validated a risk prediction model able to estimate short- and long-term breast cancer risk using information routinely reported at screening participation. The model included age, family history of breast cancer, benign breast disease and previous mammographic findings, which were found to be related to an increase in breast cancer risk. The model should be externally validated and updated with new variables.
Universitat Autònoma de Barcelona. Programa de Doctorat en Metodologia de la Recerca Biomèdica i Salut Pública
Abdull, Mohamed A. Salem. "Data mining techniques and breast cancer prediction : a case study of Libya." Thesis, Sheffield Hallam University, 2011. http://shura.shu.ac.uk/20611/.
Full textKadra, Gais. "Prediction of therapeutic response to paclitaxel, docetaxel and ixabepilone in breast cancer." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX20702.
Full textThe aim of this thesis is to study the sensitivity of breast cancer cell lines BTCL to microtubule-stabilizing agents (Taxanes and ixabepilone) in order to:1- identify pharmaco-genomic predictor of response (resistance /sensitivity) as a molecular signature, and to validate this signature on others studies of which the genomic data are available on line, so gene expression set GES predictors for Tax-sensitivity (333 genes) and Ixa-sensitivity (79 genes) were defined, and the Taxanes GES predictors has significantly predicted Pac-sensitivity in BTCL, and pathological complete response to Pac-based neo-adjuvant chemotherapy in BC patients.2- study the role of cancer stem cell (ALDH+) on the therapeutic response to Taxanes and their we identify 4 BTCLs which present a significant enrichment in the percentage and the absolute numbers of ALDELFUOR-positive cells found in each of these 4 BTCLs after 5 days of treatment by Paclitaxel , In contrast to the previous results we found that in others 3 BTCLs these phenomenon is inversed with the significant decrease of the percentage, and the absolute numbers of ALDELFUOR-positive cells found in each of these 3 BTCLs after 5 days of treatment by Paclitaxel.A molecular signature of CSC resistant /sensitive of 243 pb with 179 genes of which 152 genes are up- regulated and 27 genes down-regulated in CSC resistant to Paclitaxel, so a genomic predictors for Tax-sensitivity in CSC resistant to Paclitaxel can be derived from BTCL and may be helpful for better understanding the mechanisms of resistance to Taxanes and the implication of CSC in this resistance in order to better select of cytotoxic treatment in breast cancer patients and identification of others potential markers for targeted therapies in the future .3- we tested the impact of the alteration of genomic and proteic parameters or the mutations of some genes like tau (MAPT),Tubulin K- ALPHA (TUB A1B) Tubulin alpha-6 (TUB A1C) Tubulin beta 3 (TUBB3) and Stathmin (STMN1), unfortunately we did'nt identify a mutations to be correlated to BTCL response to Taxanes .4- we try to study at the level of proteins by immunohistochemistry on the tissue micro- array and cyto-micro-array for some parameters which have been already proved (in vitro) to be correlated with response to Taxanes , ( this part is actually ongoing)
Arif, Km Taufiqul. "Functional association of Micrornas with molecular subtypes of breast cancer." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213110/1/Km%20Taufiqul_Arif_Thesis.pdf.
Full textBooks on the topic "Breast cancer prediction"
Pawluczyk, Olga. Volumetric estimation of breast density for breast cancer risk prediction. Ottawa: National Library of Canada, 2001.
Find full textM, Thompson Alastair, ed. Prognostic and predictive factors in breast cancer. 2nd ed. London: Informa Healthcare, 2008.
Find full textPrognostic and predictive factors in breast cancer. London: Martin Dunitz, 2003.
Find full textNoorani, Hussein Zafer. Predictive genetic testing for breast and prostate cancer. Ottawa, Ont: Canadian Coordinating Office for Health Technology Assessment, 1999.
Find full textGiampietro, Gasparini, and Hayes Daniel 1951-, eds. Biomarkers in breast cancer: Molecular diagnostics for predicting and monitoring therapeutic effect. Totowa, N.J: Humana Press, 2006.
Find full textHenderson, Mary G. Predicting costs of hospitalization for cancer care: Final report to HCFA : a DRG-based casemix for cancer care. [Waltham, Mass., etc.?: Bigel Institute for Health Policy, etc.?], 1990.
Find full textJames, Paul, and Alison H. Trainer. Constitutional Genomic Variation in Disease Prediction and Prevention: Breast Cancer. Academic Press, 2022.
Find full textJames, Paul, and Alison H. Trainer. Constitutional Genomic Variation in Disease Prediction and Prevention: Breast Cancer. Elsevier Science & Technology Books, 2022.
Find full textAhlgren, Johan. Studies on Prediction of Axillary Lymph Node Status in Invasive Breast Cancer. Uppsala Universitet, 2002.
Find full textCassidy, Jim, Donald Bissett, Roy A. J. Spence OBE, Roy A. J. Spence OBE, Miranda Payne, and Gareth Morris-Stiff. Biomarkers and cancer. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199689842.003.0040.
Full textBook chapters on the topic "Breast cancer prediction"
Singh, Sonam Jawahar, Ramanathan Rajaraman, and Tanmay Tulsidas Verlekar. "Breast Cancer Prediction Using Auto-Encoders." In Data Management, Analytics and Innovation, 121–32. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2600-6_9.
Full textNguyen, Anvy, Jennifer K. Plichta, Jessica Cintolo-Gonzalez, Kinyas Kartal, Molly Elizabeth Griffin, and Kevin Hughes. "Genetic Risk Prediction in Breast Cancer." In Changing Paradigms in the Management of Breast Cancer, 217–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60336-0_15.
Full textHarbeck, Nadia. "Breast Cancer Molecular Testing for Prognosis and Prediction." In Management of Breast Diseases, 195–202. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46356-8_11.
Full textCarley, Helena, and Anju Kulkarni. "Hereditary Breast Cancer Genetics and Risk Prediction Techniques." In Breast Cancer Management for Surgeons, 43–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56673-3_5.
Full textKeller, Brad M., Emily F. Conant, Huen Oh, and Despina Kontos. "Breast Cancer Risk Prediction via Area and Volumetric Estimates of Breast Density." In Breast Imaging, 236–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31271-7_31.
Full textBaum, M. "Prognosis and Prediction for Early Breast Cancer." In Adjuvant Therapy of Breast Cancer IV, 89–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84745-5_12.
Full textCoopey, Suzanne B., and Kevin S. Hughes. "Breast Cancer Risk Prediction in Women with Atypical Breast Lesions." In Atypical Breast Proliferative Lesions and Benign Breast Disease, 103–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92657-5_8.
Full textPrateek. "Breast Cancer Prediction: Importance of Feature Selection." In Advances in Intelligent Systems and Computing, 733–42. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6861-5_62.
Full textValencia-Moreno, José Manuel, Everardo Gutiérrez López, José Felipe Ramírez Pérez, Juan Pedro Febles Rodríguez, and Omar Álvarez Xochihua. "Exploring Breast Cancer Prediction for Cuban Women." In Advances in Intelligent Systems and Computing, 480–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40690-5_47.
Full textJantre, Shrutika, and Prakash M. Mainkar. "Breast Cancer Prediction Using Machine Learning Techniques." In Smart Intelligent Computing and Applications, Volume 2, 355–68. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9705-0_36.
Full textConference papers on the topic "Breast cancer prediction"
Nafa, Fatema, Enoc Gonzalez, and Gurpreet Kaur. "An Approach using Machine Learning Model for Breast Cancer Prediction." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121815.
Full textSontrop, H., W. Verhaegh, Rene van den Ham, M. Reinders, and P. Moerland. "Subtype specific breast cancer event prediction." In 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2010. http://dx.doi.org/10.1109/gensips.2010.5719684.
Full textOctaviani, T. L., and Z. Rustam. "Random forest for breast cancer prediction." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132477.
Full textYarabarla, Mamatha Sai, Lakshmi Kavya Ravi, and A. Sivasangari. "Breast Cancer Prediction via Machine Learning." In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019. http://dx.doi.org/10.1109/icoei.2019.8862533.
Full textTee, Ir Cath, and Ali H. Gazala. "A novel breast cancer prediction system." In 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2011. http://dx.doi.org/10.1109/inista.2011.5946170.
Full textShanmugasundaram, G., S. Balaji, R. Saravanan, V. Malarselvam, and S. Yazhini. "SYSTEMATIC ANALYSIS ON BREAST CANCER PREDICTION." In 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2018. http://dx.doi.org/10.1109/icscan.2018.8541239.
Full textZhang, Aili, Lisa X. Xu, George A. Sandison, and Jiayao Zhang. "A Microscale Model for Prediction of Breast Cancer Cell Damage During Cryosurgery." In ASME 2003 Heat Transfer Summer Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/ht2003-47110.
Full textGioia, Sandra, Renata Galdino, Lucia Brigagão, Cristiane Torres, Sandra San Miguel, Lindsay Krush, and Paul Goss. "PREDICTION OF ATTENDANCE TO THE “60 DAYS LAW” WITHIN THE PATIENT NAVIGATION PROGRAM TO BREAST CANCER IN RIO DE JANEIRO." In Brazilian Breast Cancer Symposium. v29s1, 2019. http://dx.doi.org/10.29289/259453942019v29s1ep04.
Full textPawluczyk, Olga, Martin J. Yaffe, Norman F. Boyd, and Roberta A. Jong. "Estimation of volumetric breast density for breast cancer risk prediction." In Medical Imaging 2000, edited by James T. Dobbins III and John M. Boone. SPIE, 2000. http://dx.doi.org/10.1117/12.384495.
Full textUyar, Kaan, Umit Ilhan, Ahmet Ilhan, and Erkut Inan Iseri. "Breast Cancer Prediction Using Neuro-Fuzzy Systems." In 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2020. http://dx.doi.org/10.1109/iceee49618.2020.9102476.
Full textReports on the topic "Breast cancer prediction"
Hartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada442889.
Full textHartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada427975.
Full textHartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada418667.
Full textHartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2009. http://dx.doi.org/10.21236/ada552165.
Full textRohan, Thomas E. Proteomic Prediction of Breast Cancer Risk: A Cohort Study. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada506647.
Full textEuhus, David M., Sara Milchgrub, and Raheela Ashfraq. Prediction of Breast Cancer Risk by Aberrant Methylation in Mammary Duct Lavage. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada428491.
Full textWang, Ying yuan, Zechang Chen, Luxin Zhang, Shuangyi Chen, Zhuomiao Ye, Tingting Xu, and Yingying Zhang c. A systematic review and network meta-analysis: Role of SNPs in predicting breast carcinoma risk. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0092.
Full textChang, Jenny. To Ascertain Distinctive Gene Expression Patterns for the Prediction of Docetaxel (Taxotere Chemosensitivity or Chemoresistance in Human Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada430707.
Full textBuckley, Jonathan D. Predicting Time-to-Relapse in Breast Cancer Using Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada300396.
Full textHudachek, Susan F. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574076.
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