Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly di...Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.展开更多
The rapid advances in the understanding of oncogenic process and the maturation of affordable precision diagnostic tools have enabled the development of targeted therapeutic agents,such as those targeting BCR-ABL,epit...The rapid advances in the understanding of oncogenic process and the maturation of affordable precision diagnostic tools have enabled the development of targeted therapeutic agents,such as those targeting BCR-ABL,epithelial growth factor receptor L858R,EML4-anaplastic lymphoma kinase,and BRAF V600E,to treat cancers that harbor specific molecular alterations.Traditionally,each targeted drug has been developed for a particular tumor type where such alteration is most frequently found.Recently,the widespread adoption of next generation sequencing has led to an increase in the identification of rare and ultra-rare alterations,and,in some cases,the same rare alterations are found across multiple tumor types.The rarity of these alterations makes clinical trials traditionally designed for specific tumor types infeasible.As a result,tissue-agnostic trials have been developed to study the efficacy of these treatments and increase patient access.This review summarizes current successful cases of tissue-agnostic development,such as drugs targeting tropomyosin receptor kinase fusions,and proposes the next wave of potential tissue-agnostic targets,including fusions of ROS1,anaplastic lymphoma kinase,fibroblast growth factor receptor,and rearranged during transfection.In addition,the advantages and the challenges of such approach are discussed in the context of clinical development and approval.展开更多
基金Researchers Supporting Project Number(RSPD2024R 553),King Saud University,Riyadh,Saudi Arabia.
文摘Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.
基金Li IW and Krishnamurthy N were supported by the Emperor Science Awards,an initiative of Stand Up To Cancer(SU2C),made possible by support from Genentech,Bristol-Myers Squibb Company,and Novartis.
文摘The rapid advances in the understanding of oncogenic process and the maturation of affordable precision diagnostic tools have enabled the development of targeted therapeutic agents,such as those targeting BCR-ABL,epithelial growth factor receptor L858R,EML4-anaplastic lymphoma kinase,and BRAF V600E,to treat cancers that harbor specific molecular alterations.Traditionally,each targeted drug has been developed for a particular tumor type where such alteration is most frequently found.Recently,the widespread adoption of next generation sequencing has led to an increase in the identification of rare and ultra-rare alterations,and,in some cases,the same rare alterations are found across multiple tumor types.The rarity of these alterations makes clinical trials traditionally designed for specific tumor types infeasible.As a result,tissue-agnostic trials have been developed to study the efficacy of these treatments and increase patient access.This review summarizes current successful cases of tissue-agnostic development,such as drugs targeting tropomyosin receptor kinase fusions,and proposes the next wave of potential tissue-agnostic targets,including fusions of ROS1,anaplastic lymphoma kinase,fibroblast growth factor receptor,and rearranged during transfection.In addition,the advantages and the challenges of such approach are discussed in the context of clinical development and approval.
