About this Research Topic
In the rapidly evolving landscape of personalized medicine, notable progress, especially in cancer research, has markedly transformed our approach to health care. Over recent years, leveraged by genetic breakthroughs and robust statistical models, our capacity to discern and address individual cancer profiles has advanced. Despite these advancements, challenges persist in refining these methods to achieve precise risk prediction and treatment adjustments for each patient. As traditional models falter in detail and accuracy, the integration of modern genetic tools and analytical sophistication proposes an unmatched promise for personalized therapeutic regimens.
This Research Topic aims to spotlight transformative strides in applying contemporary genetic and statistical techniques within personalized medicine, focusing on the exactitude of cancer risk predictions and individual treatment strategies. It sets out to extend the bounds of conventional risk models by embedding cutting-edge genetic analyses and statistical frameworks, enhancing the precision in forecasting cancer susceptibility and tailoring patient-specific treatments. Efforts to reshape clinical decision-making frameworks that capitalize on these advancements, thereby offering enhanced, tailored care to cancer patients, are particularly emphasized.
To gather further insights within the boundaries of tailored genetic analysis and statistical innovation, we welcome articles addressing, but not limited to, the following themes:
Integration of genomic data and advanced statistical models for individualized cancer risk assessment.
Development and validation of predictive biomarkers for cancer risk stratification.
Application of machine learning and artificial intelligence algorithms in cancer risk prediction and therapeutic decision-making.
Exploration of novel genetic variants, their functional role by novel approaches as CRISPR/cas9 and their impact on cancer susceptibility and treatment response.
Implementation of personalized medicine approaches in clinical practice, including challenges and opportunities for adoption and implementation.
Ethical considerations and implications of utilizing genetic and statistical approaches in personalized cancer care.
In aiming for a comprehensive encapsulation of contemporary research, this issue calls for a collaboration across diverse scientific disciplines aimed at refining and implementing practical solutions for predictive medicine in cancer care, promoting a unified push towards optimal patient-specific outcomes.
Please note manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases that are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of the scope of this Research Topic.
This Research Topic aims to spotlight transformative strides in applying contemporary genetic and statistical techniques within personalized medicine, focusing on the exactitude of cancer risk predictions and individual treatment strategies. It sets out to extend the bounds of conventional risk models by embedding cutting-edge genetic analyses and statistical frameworks, enhancing the precision in forecasting cancer susceptibility and tailoring patient-specific treatments. Efforts to reshape clinical decision-making frameworks that capitalize on these advancements, thereby offering enhanced, tailored care to cancer patients, are particularly emphasized.
To gather further insights within the boundaries of tailored genetic analysis and statistical innovation, we welcome articles addressing, but not limited to, the following themes:
Integration of genomic data and advanced statistical models for individualized cancer risk assessment.
Development and validation of predictive biomarkers for cancer risk stratification.
Application of machine learning and artificial intelligence algorithms in cancer risk prediction and therapeutic decision-making.
Exploration of novel genetic variants, their functional role by novel approaches as CRISPR/cas9 and their impact on cancer susceptibility and treatment response.
Implementation of personalized medicine approaches in clinical practice, including challenges and opportunities for adoption and implementation.
Ethical considerations and implications of utilizing genetic and statistical approaches in personalized cancer care.
In aiming for a comprehensive encapsulation of contemporary research, this issue calls for a collaboration across diverse scientific disciplines aimed at refining and implementing practical solutions for predictive medicine in cancer care, promoting a unified push towards optimal patient-specific outcomes.
Please note manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases that are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of the scope of this Research Topic.
Keywords: personalized medicine, cancer risk estimation, therapeutic decision-making, genomic data, statistical approaches, biomarkers, machine learning, artificial intelligence, genetic variants, clinical practice, ethical considerations.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
