首页|Cyprus Institute of Neurology and Genetics Reports Findings in Personalized Medi cine (Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach)
Cyprus Institute of Neurology and Genetics Reports Findings in Personalized Medi cine (Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach)
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New research on Drugs and Therapies -Personalized Medicine is the subject of a report. According to news reporting ou t of Nicosia, Cyprus, by NewsRx editors, research stated, "The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individ ual patients and a lack of targeted agents for most mutational events, implement ing individualized treatment for AML has proven difficult." Our news journalists obtained a quote from the research from the Cyprus Institut e of Neurology and Genetics, "We reanalysed the BeatAML dataset employing Machin e Learning algorithms. The BeatAML project entails patients extensively characte rized at the molecular and clinical levels and linked to drug sensitivity output s. Our approach capitalizes on the molecular and clinical data provided by the B eatAML dataset to predict the ex vivo drug sensitivity for the 122 drugs evaluat ed by the project. We utilized ElasticNet, which produces fully interpretable mo dels, in combination with a two-step training protocol that allowed us to narrow down computations. We automated the genes' filtering step by employing two metr ics, and we evaluated all possible data combinations to identify the best traini ng configuration settings per drug. We report a Pearson correlation across all d rugs of 0.36 when clinical and RNA sequencing data were combined, with the best-performing models reaching a Pearson correlation of 0.67. When we trained using the datasets in isolation, we noted that RNA Sequencing data (Pearson: 0.36) att ained three times the predictive power of whole exome sequencing data (Pearson: 0.11), with clinical data falling somewhere in between (Pearson 0.26). Lastly, w e present a paradigm of clinical significance. We used our models' prediction as a drug sensitivity score to rank an individual's expected response to treatment . We identified 78 patients out of 89 (88 %) that the proposed drug was more potent than the administered one based on their ex vivo drug sensitivi ty data."
NicosiaCyprusEuropeAcute Myeloid L eukemiaCancerCyborgsDrugs and TherapiesEmerging TechnologiesHealth and MedicineHematologyLeukemiaMachine LearningMyeloid LeukemiaOncologyP ersonalized MedicinePersonalized Therapy
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