Evidence has shown that differential transcriptomic profiles among human populations from diverse ancestries,supporting the role of genetic architecture in regulating gene expression alongside environmental stimuli.Ge...Evidence has shown that differential transcriptomic profiles among human populations from diverse ancestries,supporting the role of genetic architecture in regulating gene expression alongside environmental stimuli.Genetic variants that regulate gene expression,known as expression quantitative trait loci(eQTL),are primarily shaped by human migration history and evolutionary forces,likewise,regulation of gene expression in principle could have been influenced by these events.Therefore,a comprehensive understanding of how human evolution impacts eQTL offers important insights into how phenotypic diversity is shaped.Recent studies,however,suggest that eQTL is enriched in genes that are selectively constrained.Whether eQTL is minimally affected by selective pressures remains an open question and requires comprehensive investigations.In addition,such studies are primarily dominated by the major populations of European ancestry,leaving many marginalized populations underrepresented.These observations indicate there exists a fundamental knowledge gap in the role of genomics variation on phenotypic diversity,which potentially hinders precision medicine.This article aims to revisit the abundance of eQTL across diverse populations and provide an overview of their impact from the population and evolutionary genetics perspective,subsequently discuss their influence on phenomics,as well as challenges and opportunities in the applications to precision medicine.展开更多
The integration of artificial intelligence(AI)into medical robotics has emerged as a cornerstone of modern healthcare,driving transformative advancements in precision,adaptability and patient outcomes.Although computa...The integration of artificial intelligence(AI)into medical robotics has emerged as a cornerstone of modern healthcare,driving transformative advancements in precision,adaptability and patient outcomes.Although computational tools have long supported diagnostic processes,their role is evolving beyond passive assistance to become active collaborators in therapeutic decision-making.In this paradigm,knowledge-driven deep learning systems are redefining possibilities-enabling robots to interpret complex data,adapt to dynamic clinical environments and execute tasks with human-like contextual awareness.展开更多
Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ...Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.展开更多
Breast cancer stands as the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality,posing a persistent challenge to global public health1.In recent decades,the landscape of bre...Breast cancer stands as the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality,posing a persistent challenge to global public health1.In recent decades,the landscape of breast cancer care has been profoundly reshaped by the rapid development of precision medicine,targeted therapy,immunotherapy,and clinical translational research.展开更多
基金supported by the Ministry of Higher Education(MOHE)Malaysia through Fundamental Research Grant Scheme(FRGS)with project code:FRGS/1/2021/STG01/UCSI/01/.SX was funded by the National Natural Science Foundation of China(NSFC)grants 32030020 and 32288101funded by the NSFC grant 32270665.
文摘Evidence has shown that differential transcriptomic profiles among human populations from diverse ancestries,supporting the role of genetic architecture in regulating gene expression alongside environmental stimuli.Genetic variants that regulate gene expression,known as expression quantitative trait loci(eQTL),are primarily shaped by human migration history and evolutionary forces,likewise,regulation of gene expression in principle could have been influenced by these events.Therefore,a comprehensive understanding of how human evolution impacts eQTL offers important insights into how phenotypic diversity is shaped.Recent studies,however,suggest that eQTL is enriched in genes that are selectively constrained.Whether eQTL is minimally affected by selective pressures remains an open question and requires comprehensive investigations.In addition,such studies are primarily dominated by the major populations of European ancestry,leaving many marginalized populations underrepresented.These observations indicate there exists a fundamental knowledge gap in the role of genomics variation on phenotypic diversity,which potentially hinders precision medicine.This article aims to revisit the abundance of eQTL across diverse populations and provide an overview of their impact from the population and evolutionary genetics perspective,subsequently discuss their influence on phenomics,as well as challenges and opportunities in the applications to precision medicine.
文摘The integration of artificial intelligence(AI)into medical robotics has emerged as a cornerstone of modern healthcare,driving transformative advancements in precision,adaptability and patient outcomes.Although computational tools have long supported diagnostic processes,their role is evolving beyond passive assistance to become active collaborators in therapeutic decision-making.In this paradigm,knowledge-driven deep learning systems are redefining possibilities-enabling robots to interpret complex data,adapt to dynamic clinical environments and execute tasks with human-like contextual awareness.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number PNURSP2024R333,Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.
文摘Breast cancer stands as the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality,posing a persistent challenge to global public health1.In recent decades,the landscape of breast cancer care has been profoundly reshaped by the rapid development of precision medicine,targeted therapy,immunotherapy,and clinical translational research.
