標題: Titlebook: Bioinformatics Research and Applications; 20th International S Wei Peng,Zhipeng Cai,Pavel Skums Conference proceedings 2024 The Editor(s) ( [打印本頁] 作者: FLAW 時間: 2025-3-21 18:02
書目名稱Bioinformatics Research and Applications影響因子(影響力)
書目名稱Bioinformatics Research and Applications影響因子(影響力)學科排名
書目名稱Bioinformatics Research and Applications網絡公開度
書目名稱Bioinformatics Research and Applications網絡公開度學科排名
書目名稱Bioinformatics Research and Applications被引頻次
書目名稱Bioinformatics Research and Applications被引頻次學科排名
書目名稱Bioinformatics Research and Applications年度引用
書目名稱Bioinformatics Research and Applications年度引用學科排名
書目名稱Bioinformatics Research and Applications讀者反饋
書目名稱Bioinformatics Research and Applications讀者反饋學科排名
作者: 險代理人 時間: 2025-3-21 22:15 作者: 骨 時間: 2025-3-22 04:07 作者: 清楚說話 時間: 2025-3-22 08:36 作者: 改進 時間: 2025-3-22 09:19
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.作者: Oversee 時間: 2025-3-22 14:24
,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.作者: debacle 時間: 2025-3-22 18:34
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.作者: Indent 時間: 2025-3-22 21:48
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.作者: Irrepressible 時間: 2025-3-23 02:43
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.作者: Indurate 時間: 2025-3-23 05:56
,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.作者: 情愛 時間: 2025-3-23 10:14
,scCoRR: A Data-Driven Self-correction Framework for?Labeled scRNA-Seq Data,ised 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.作者: Spirometry 時間: 2025-3-23 17:11 作者: CRUDE 時間: 2025-3-23 18:27
Conference proceedings 2024unming, China, in July 19–21, 2024...The 93 full papers? included in this book were carefully reviewed and selected from 236 submissions. The symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and作者: 喚起 時間: 2025-3-23 23:57 作者: FOVEA 時間: 2025-3-24 04:51 作者: 個人長篇演說 時間: 2025-3-24 08:59
Exploring Hierarchical Structures of Cell Types in scRNA-seq Data,al structures that perform functions differently. Constructing a hierarchical structure of cell types is crucial for revealing relationships between cell types. Existing hierarchical methods construct cell type hierarchy with fixed branches which cannot reflect actual cell type hierarchy, and cannot作者: 該得 時間: 2025-3-24 11:45 作者: recession 時間: 2025-3-24 17:55 作者: 客觀 時間: 2025-3-24 21:36
,A Hybrid Feature Fusion Network for?Predicting HER2 Status on?H &E-Stained Histopathology Images, Breast cancer is the most common and lethal cancer among women worldwide, and about 25% of breast cancer patients have HER2 overexpression/amplification. At present, the commonly evaluating HER2 status methods are tissue-consuming and prone to analysis or interpretation errors. Therefore, this stud作者: 一回合 時間: 2025-3-25 00:09
,scCoRR: A Data-Driven Self-correction Framework for?Labeled scRNA-Seq Data, evaluation metrics within single-cell research are intertwined with cell labels. While annotating cell labels often requires prior biological knowledge for clustering, this is frequently approached from a clustering perspective rather than considering the heterogeneity of individual cells. Building作者: Cuisine 時間: 2025-3-25 03:20 作者: Nerve-Block 時間: 2025-3-25 09:03 作者: TEM 時間: 2025-3-25 12:24 作者: 翅膀拍動 時間: 2025-3-25 19:47
,FedKD-DTI: Drug-Target Interaction Prediction Based on?Federated Knowledge Distillation,and time required for traditional high-throughput screening. However, DTI-related data are usually distributed across different institutions and their sharing is restricted because of data privacy and intellectual property rights. It is essential to construct a scheme that enhances multi-institution作者: 人類的發(fā)源 時間: 2025-3-25 23:24 作者: 舊石器時代 時間: 2025-3-26 02:11
,Synthesis of?Boolean Networks with?Weak and?Strong Regulators,and understand the dynamic nature of cell signaling events. Previously, automatic synthesis methods have been developed that consider a large set of Boolean networks and identify solutions that are consistent with all experimental data. A method and tool termed the Reasoning Engine (RE:IN) allows sy作者: 柏樹 時間: 2025-3-26 07:58
,Patch-Based Coupled Attention Network to?Predict MSI Status in?Colon Cancer,rker-related features from huge scale WSI images is a challenge faced by AI models. In this study, we propose a patch-based coupled attention neural network (CovAttnNet) designed to predict Microsatellite Instability (MSI) status from WSI images. CovAttnNet consists of a transformer based backbone n作者: BADGE 時間: 2025-3-26 11:04 作者: 傾聽 時間: 2025-3-26 15:11 作者: 手工藝品 時間: 2025-3-26 20:51
,DMSDR: Drug Molecule Synergy-Enhanced Network for?Drug Recommendation with?Multi-source Domain Knowing drugs by modeling patient electronic health records and drug side effects knowledge. However, they overlook the importance of drug synergy in drug recommendation, despite the fact that synergistic drug combinations can enhance therapeutic efficacy. To address this gap, we propose the Drug Molecu作者: Collision 時間: 2025-3-26 22:26 作者: 乏味 時間: 2025-3-27 03:26 作者: 可耕種 時間: 2025-3-27 07:24
Integrated Analysis of Autophagy-Related Genes Identifies Diagnostic Biomarkers and Immune Correlat the involvement of autophagy and cellular immunity in PE pathogenesis; however, research on autophagy-related genes in PE and their clinical significance is limited. This study aimed to identify autophagy-related genes with diagnostic potential for PE and investigate their correlation with immune i作者: ALT 時間: 2025-3-27 11:25 作者: surrogate 時間: 2025-3-27 14:50 作者: 廢墟 時間: 2025-3-27 20:51 作者: 偽造 時間: 2025-3-28 01:48
https://doi.org/10.1007/978-3-663-02976-2ontrolled clinical trials before, while it is time consuming and expensive. Recently, more and more computational models for predicting frequencies of drug side effects are put forward. In this work, we propose a novel method for predicting the frequencies of drug side effects, by using a Graph Atte作者: 易于 時間: 2025-3-28 06:09 作者: 飛行員 時間: 2025-3-28 06:44
Die Theorie des Metallspritzens, Breast cancer is the most common and lethal cancer among women worldwide, and about 25% of breast cancer patients have HER2 overexpression/amplification. At present, the commonly evaluating HER2 status methods are tissue-consuming and prone to analysis or interpretation errors. Therefore, this stud作者: Curmudgeon 時間: 2025-3-28 10:54 作者: myalgia 時間: 2025-3-28 16:26
https://doi.org/10.1007/978-3-319-93755-7to become a favorable alternative to antibiotics. The development of machine learning and deep learning-based AMP classification methods has been driven by the need to overcome the time-consuming and expensive aspects of wet-lab discriminatory techniques. Recently, several pre-trained protein langua作者: 眨眼 時間: 2025-3-28 19:38
Metallurgical Design and Industrymbersome and costly, requiring an overnight stay in a sleep laboratory with multiple sensors attached to the subjects, which limits widespread adoption. Conversely, electrocardiogram (ECG) signal offers a convenient and affordable alternative due to its ease of integration into portable devices. How作者: BAN 時間: 2025-3-28 23:50
The Role of Dislocation Drag in Shock Wavesd-and-extend strategy, where seeding usually involves a large number of random memory accesses, and extension of seeds relies on computationally expensive alignment algorithms, resulting in huge time consumption. Recently, Strobealign has reached state-of-the-art alignment speed while maintaining hi作者: Introduction 時間: 2025-3-29 04:34
https://doi.org/10.1007/978-3-642-13956-7and time required for traditional high-throughput screening. However, DTI-related data are usually distributed across different institutions and their sharing is restricted because of data privacy and intellectual property rights. It is essential to construct a scheme that enhances multi-institution作者: 平躺 時間: 2025-3-29 07:27
,Steel— the “Material of Choice”,ds are limited in performance due to that they are mainly designed for scRNA-seq data without accounting for spatial coordinate information. More importantly, they have been struggling to identify novel cell types. Here, we introduce SPOTAnno, a novel method that allows for the simultaneous and accu作者: 聲音刺耳 時間: 2025-3-29 11:56 作者: 縱火 時間: 2025-3-29 15:45 作者: Ligament 時間: 2025-3-29 22:22
Metallurgical Process Engineeringimizing CNS drug efficacy. Since traditional experimental approaches are expensive and time-consuming, some computational methods based on Graph Neural Networks (GNNs) have emerged for predicting BBB permeability of small molecule compounds. While these methods have demonstrated considerable success作者: 不持續(xù)就爆 時間: 2025-3-30 00:58
Steel Plants and the Environment Issues,ark datasets, their prediction accuracy drops significantly in cold-start scenarios - i.e., when confront with drugs or proteins that have not appeared in the training set. Limited by training dataset scale, features learned by end-to-end DTA models have less generalization. To this end, we propose 作者: myopia 時間: 2025-3-30 04:16 作者: left-ventricle 時間: 2025-3-30 10:01
,Steel— the “Material of Choice”,n lncRNAs and various pathological conditions holds significant promise for unraveling the intricate mechanisms that underlie disease onset and progression. Due to traditional biological experimentation for probing lncRNA-disease associations is often hampered by substantial financial constraints an作者: 無意 時間: 2025-3-30 15:32 作者: 破譯密碼 時間: 2025-3-30 18:23 作者: instructive 時間: 2025-3-30 22:49
Steel Plants and the Environment Issues,’ conditions and accurately predict their diseases is an important research issue in the medical field. However, most fusion approaches employed in existing multimodal learning studies are excessively simplistic and often neglect the hierarchical nature of intermodal interactions. In this paper, we 作者: 漂亮 時間: 2025-3-31 04:28 作者: mortuary 時間: 2025-3-31 05:31
https://doi.org/10.1007/978-981-97-5131-0Computer Science; Informatics; Conference Proceedings; Research; Applications; Applied computing; Life and作者: enmesh 時間: 2025-3-31 10:36
978-981-97-5130-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: vasospasm 時間: 2025-3-31 16:57 作者: 預防注射 時間: 2025-3-31 19:55 作者: 祝賀 時間: 2025-3-31 21:45 作者: 空中 時間: 2025-4-1 03:23 作者: 辮子帶來幫助 時間: 2025-4-1 07:15 作者: dissent 時間: 2025-4-1 10:42
,FedKD-DTI: Drug-Target Interaction Prediction Based on?Federated Knowledge Distillation,aggregation, which can effectively utilize the knowledge contained in public and private data. We evaluate FedKD-DTI on three benchmark datasets and compare it with four baselines. The results show that FedKD-DTI is very close to centralized learning and significantly better than localized learning.
