Titlebook: Bioinformatics Research and Applications; 20th International S Wei Peng,Zhipeng Cai,Pavel Skums Conference proceedings 2024 The Editor(s) (

FLAW

書(shū)目名稱(chēng)Bioinformatics Research and Applications影響因子(影響力)




書(shū)目名稱(chēng)Bioinformatics Research and Applications影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Bioinformatics Research and Applications網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Bioinformatics Research and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Bioinformatics Research and Applications被引頻次




書(shū)目名稱(chēng)Bioinformatics Research and Applications被引頻次學(xué)科排名




書(shū)目名稱(chēng)Bioinformatics Research and Applications年度引用




書(shū)目名稱(chēng)Bioinformatics Research and Applications年度引用學(xué)科排名




書(shū)目名稱(chēng)Bioinformatics Research and Applications讀者反饋




書(shū)目名稱(chēng)Bioinformatics Research and Applications讀者反饋學(xué)科排名

險(xiǎn)代理人

骨

清楚說(shuō)話(huà)

改進(jìn)

Metallurgical Process Engineeringact and fuse key features. We validated the performance of the model on the publicly available dataset TCGA-COAD, and the experimental results demonstrated the superior ability of CovAttnNet in predicting the status of colon cancer MSI status. This study provides a new method for deep learning in marker prediction research.

Oversee

,Patch-Based Coupled Attention Network to?Predict MSI Status in?Colon Cancer,act and fuse key features. We validated the performance of the model on the publicly available dataset TCGA-COAD, and the experimental results demonstrated the superior ability of CovAttnNet in predicting the status of colon cancer MSI status. This study provides a new method for deep learning in marker prediction research.

debacle

Die Theorie des Metallspritzens,ation module (LRM) for global and local features extraction, respectively, and then fuses global and local features for final classification. The experimental results show the effectiveness and potential of the proposed HM-HER2 model in the field of H &E-stained whole slide images (WSIs) classification of breast cancer.

Indent

Metallurgical Design and Industryised deep neural network is trained with cross-entropy loss and a contrastive regularization term to predict the types of the remaining cells. During this process, the labels of some cells are corrected from one cell type to another, a phenomenon that can also be elucidated from various biological perspectives.

Irrepressible

https://doi.org/10.1007/978-3-642-13956-7ients and healthy controls. Our findings demonstrate higher classification accuracy using time-varying features compared to static brain network topology features. This study enhances our understanding of the dynamic brain network mechanisms in ASD and suggests reliable methods for early diagnosis.

Indurate

,A Hybrid Feature Fusion Network for?Predicting HER2 Status on?H &E-Stained Histopathology Images,ation module (LRM) for global and local features extraction, respectively, and then fuses global and local features for final classification. The experimental results show the effectiveness and potential of the proposed HM-HER2 model in the field of H &E-stained whole slide images (WSIs) classification of breast cancer.