About this Research Topic
Over the past few decades, genome sequencing methods have rapidly evolved, enabling the collection of vast amounts of genomic data providing human genetic variants, disease-related mutations, and associations between genotype and phenotype.
Precision medicine is an approach to healthcare that uses genomic data, clinical data, and other information to tailor treatments to individual patients or groups based on their genetic makeup, environment, and lifestyle.
Computational genomics plays a critical role in precision medicine by providing researchers and clinicians with the tools and techniques to analyze and interpret large genomic datasets. These datasets can be used to identify genetic variants and other genomic features that are associated with diseases and drug responses. This information can then be used to develop personalized treatment plans tailored to individual patients.
One of the most important contributions of computational genomics to precision medicine is the development of predictive models that can identify patients who are most likely to benefit from specific treatments. These models incorporate genomic, clinical, and other data to predict drug responses, disease outcomes, and other important clinical factors. Using these models, clinicians can develop treatment plans tailored to individual patients, leading to better results and improved patient care.
This Research Topic aims to gather the most up-to-date knowledge on recent advances in analytical approaches and deep-learning models for identifying disease-associated rare variants and predicting the best treatment outcomes.
Precision medicine is an approach to healthcare that uses genomic data, clinical data, and other information to tailor treatments to individual patients or groups based on their genetic makeup, environment, and lifestyle.
Computational genomics plays a critical role in precision medicine by providing researchers and clinicians with the tools and techniques to analyze and interpret large genomic datasets. These datasets can be used to identify genetic variants and other genomic features that are associated with diseases and drug responses. This information can then be used to develop personalized treatment plans tailored to individual patients.
One of the most important contributions of computational genomics to precision medicine is the development of predictive models that can identify patients who are most likely to benefit from specific treatments. These models incorporate genomic, clinical, and other data to predict drug responses, disease outcomes, and other important clinical factors. Using these models, clinicians can develop treatment plans tailored to individual patients, leading to better results and improved patient care.
This Research Topic aims to gather the most up-to-date knowledge on recent advances in analytical approaches and deep-learning models for identifying disease-associated rare variants and predicting the best treatment outcomes.
Keywords: Statistical genetics, genomics, bioinformatics, predictive genomics, precision medicine
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