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Full-Text Articles in Life Sciences
Accurate Cytogenetic Biodosimetry Through Automated Dicentric Chromosome Curation And Metaphase Cell Selection, Jin Liu, Yanxin Li, Ruth Wilkins, Canadian Nuclear Laboratories, Joan H. Knoll, Peter Rogan
Accurate Cytogenetic Biodosimetry Through Automated Dicentric Chromosome Curation And Metaphase Cell Selection, Jin Liu, Yanxin Li, Ruth Wilkins, Canadian Nuclear Laboratories, Joan H. Knoll, Peter Rogan
Biochemistry Publications
Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs …
Predicting Outcomes Of Hormone And Chemotherapy In The Molecular Taxonomy Of Breast Cancer International Consortium (Metabric) Study By Biochemically-Inspired Machine Learning, Peter Rogan, Eliseos J. Mucaki, Katherina Baranova, Huy Q. Pham, Iman Rezaeian, Dimo Angelov, Alioune Ngom, Luis Rueda
Predicting Outcomes Of Hormone And Chemotherapy In The Molecular Taxonomy Of Breast Cancer International Consortium (Metabric) Study By Biochemically-Inspired Machine Learning, Peter Rogan, Eliseos J. Mucaki, Katherina Baranova, Huy Q. Pham, Iman Rezaeian, Dimo Angelov, Alioune Ngom, Luis Rueda
Biochemistry Publications
Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, …