標(biāo)題: Titlebook: Advanced Intelligent Computing in Bioinformatics; 20th International C De-Shuang Huang,Yijie Pan,Qinhu Zhang Conference proceedings 2024 Th [打印本頁] 作者: 中產(chǎn)階級 時間: 2025-3-21 18:41
書目名稱Advanced Intelligent Computing in Bioinformatics影響因子(影響力)
書目名稱Advanced Intelligent Computing in Bioinformatics影響因子(影響力)學(xué)科排名
書目名稱Advanced Intelligent Computing in Bioinformatics網(wǎng)絡(luò)公開度
書目名稱Advanced Intelligent Computing in Bioinformatics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advanced Intelligent Computing in Bioinformatics被引頻次
書目名稱Advanced Intelligent Computing in Bioinformatics被引頻次學(xué)科排名
書目名稱Advanced Intelligent Computing in Bioinformatics年度引用
書目名稱Advanced Intelligent Computing in Bioinformatics年度引用學(xué)科排名
書目名稱Advanced Intelligent Computing in Bioinformatics讀者反饋
書目名稱Advanced Intelligent Computing in Bioinformatics讀者反饋學(xué)科排名
作者: 教義 時間: 2025-3-21 20:39
https://doi.org/10.1007/978-981-97-5692-6Gene Regulation Modeling and Analysis; Protein Structure and Function Prediction; Biomedical Data Mode作者: Ventricle 時間: 2025-3-22 02:33
978-981-97-5691-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: coalition 時間: 2025-3-22 04:34 作者: 猛擊 時間: 2025-3-22 12:22
,überblick über das Gebiet der Metallkunde,ingle cells, cell type annotation is the most common computational task in the downstream specific task. Different cell types differ in morphology, function, or biochemical properties, and these differences determine the specific function and role of cells in the organism. Traditional methods for ce作者: LINE 時間: 2025-3-22 16:30 作者: 腫塊 時間: 2025-3-22 19:30
,Mikrobielle ?kologie und Biogeochemie,stive measurements of transcriptional perturbation responses become challenging. There are some computational methods to predict drug responses, but the mapping between the drug responses of different cell lines is largely overlooked. We propose CDDTR, a cross-domain autoencoders based method, that 作者: Innocence 時間: 2025-3-22 23:27
https://doi.org/10.1007/978-3-642-05096-1However, the high cost of sequencing techniques limits the identification of chromatin interactions across diverse samples. Considering its significance, quite a few deep learning-based methods have recently emerged for computationally detecting chromatin interactions. In this study, we propose ChiM作者: 消極詞匯 時間: 2025-3-23 01:30
https://doi.org/10.1007/978-3-642-05096-1 level. In scRNA-seq data analysis, cell clustering is a key step in downstream analysis as it can identify cell types and discover new cell subtypes. However, the high dimensionality, sparsity, and high noise characteristics of scRNA-seq datasets present significant challenges for clustering analys作者: 偶像 時間: 2025-3-23 05:58 作者: 漂亮 時間: 2025-3-23 12:01
Biotechnologie und Umweltmikrobiologie,gh dynamic programming. MSA is a crucial tool for temporal analyses such as classification, aggregation, and speech recognition. The process uses a penalty score to assess the similarity among the sequences, with or without gaps. In bioinformatics, MSA is widely used to identify conserved regions an作者: Deject 時間: 2025-3-23 17:23
,Mikrobielle ?kologie und Biogeochemie,ositioning can reduce experimental costs and expedite drug development. In this paper, a knowledge graph is employed to integrate biological data from various database sources. Subsequently, a graph embedding algorithm based on random walks is utilized to obtain feature vectors of entities in the gr作者: 揮舞 時間: 2025-3-23 21:27
,Mikrobielle ?kologie und Biogeochemie,e performance, they are seldom applied in medical recommendations due to their lack of interpretability. On the other hand, traditional statistical methods are easily interpretable but often limit in performance. In this study, we propose a novel framework called Diagnosis Neural Collaborative Filte作者: 在前面 時間: 2025-3-24 01:49
,Mikrobielle ?kologie und Biogeochemie,get is unavailable. This paper proposed a drug design model based on the protein sequence features. The model utilizes Transformer and a graph neural network (GNN) algorithm based on the weighted protein graph to obtain protein sequence features. This approach allows model to effectively understand 作者: 牽索 時間: 2025-3-24 06:26
Biotechnologie und Umweltmikrobiologie,drug development. Various sequence-based and graph-based deep learning models have achieved good performance in drug target affinity (DTA) prediction, but most of them extract features at a single scale, and this approach is deficient in global topological feature extraction. In this paper, we propo作者: 來就得意 時間: 2025-3-24 08:16
https://doi.org/10.1007/978-3-642-05096-1nsuming and expensive. Therefore, deep learning-based methods have been widely applied in the field of DTIs prediction. In recent years, methods utilizing graph convolutional neural networks to learn the features of drug-protein pairs (DPPs) and thus achieve DTI prediction have achieved certain succ作者: 闖入 時間: 2025-3-24 13:22
,Mikrobielle ?kologie und Biogeochemie,cRNAs regulate gene expression by adsorbing miRNAs and acting as ‘sponges’. Dysregulation of miRNAs has been observed in various cancer tissues, and co-expression of circRNAs with miRNAs has been noted in many cancer tissues. The co-expression of miRNAs with circRNAs may play an important role in re作者: GROSS 時間: 2025-3-24 17:44 作者: 排斥 時間: 2025-3-24 22:42 作者: Flatter 時間: 2025-3-25 01:19
Biotechnologie und Umweltmikrobiologie,owever, existing deep learning approaches face a challenge due to the lack of representations for non-pairwise relations and substructures in compounds, leading to limited performance and poor generalization ability. To address this challenge, a novel method named HyperCPI is proposed in this study.作者: 戰(zhàn)勝 時間: 2025-3-25 06:59
Spezielle Morphologie von Prokaryoten,ts arise from non-biological variations such as different sequencing batches, sequencing protocols, sequencing depths, and so on. Batch effects introduce systematic biases and confound biological variations of interest, which have a detrimental impact on the validity of study findings. Eliminating b作者: mutineer 時間: 2025-3-25 08:39
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/167166.jpg作者: Arthr- 時間: 2025-3-25 15:17 作者: 大量殺死 時間: 2025-3-25 18:42 作者: Sputum 時間: 2025-3-25 21:35
https://doi.org/10.1007/978-3-662-25696-1uences, thereby extracting essential features to construct an efficient predictive model. Experimental results demonstrate the method’s efficacy in predicting the binding probability between antigens, MHC molecules and TCR, showcasing its potential for application.作者: 性滿足 時間: 2025-3-26 04:12
,Mikrobielle ?kologie und Biogeochemie,uts. Experimental results demonstrate that the node feature vectors obtained using the Monte Carlo Random Walk based on Metropolis-Hastings algorithm (MHRW) based graph embedding algorithm are superior, and the GRU neural network model incorporating multi-head attention mechanism outperforms others.作者: 結(jié)束 時間: 2025-3-26 05:22 作者: 松軟無力 時間: 2025-3-26 11:09
https://doi.org/10.1007/978-3-642-05096-1ion and graph-level attention mechanism to learn features of DPPs. Experimental results indicate that compared to other state-of-the-art methods, the proposed approach demonstrates higher accuracy and generalization capability.作者: SLAY 時間: 2025-3-26 15:47
,Mikrobielle ?kologie und Biogeochemie,d in the reconstructed network to predict novel DPIs. The results demonstrated GSDPI could gain better prediction performance than several state-of-the-art models, achieving prediction accuracies of 0.9840, 0.9846, 0.9767, and 0.9878 on four public datasets, respectively.作者: MAZE 時間: 2025-3-26 19:25
BiLETCR: An Efficient PMHC-TCR Combined Forecasting Methoduences, thereby extracting essential features to construct an efficient predictive model. Experimental results demonstrate the method’s efficacy in predicting the binding probability between antigens, MHC molecules and TCR, showcasing its potential for application.作者: 微生物 時間: 2025-3-26 22:42
DeepMHAttGRU-DTI: Prediction of Drug-Target Interactions Based on Knowledge Graph Random Walk Embedduts. Experimental results demonstrate that the node feature vectors obtained using the Monte Carlo Random Walk based on Metropolis-Hastings algorithm (MHRW) based graph embedding algorithm are superior, and the GRU neural network model incorporating multi-head attention mechanism outperforms others.作者: 大方一點 時間: 2025-3-27 02:28
DiagNCF: Diagnosis Neural Collaborative Filtering for Accurate Medical Recommendationelationships between diseases and laboratory test results. We conducted experimental results on several medical datasets, specifically the MIMIC3 dataset, demonstrate that DiagNCF effectively provides accurate and efficient recommendations.作者: Ergots 時間: 2025-3-27 05:34 作者: 后天習(xí)得 時間: 2025-3-27 11:47
GSDPI: An Integrated Feature Extraction Framework for Predicting Novel Drug-Protein Interactiond in the reconstructed network to predict novel DPIs. The results demonstrated GSDPI could gain better prediction performance than several state-of-the-art models, achieving prediction accuracies of 0.9840, 0.9846, 0.9767, and 0.9878 on four public datasets, respectively.作者: Prophylaxis 時間: 2025-3-27 16:49 作者: 喧鬧 時間: 2025-3-27 19:01
0302-9743 4880 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024...The total of 863 regular papers were carefully reviewed and selected from 2189 submissions...The intelligent computing annual conference primari作者: COMA 時間: 2025-3-28 01:04 作者: MELD 時間: 2025-3-28 03:01
AAHLDMA: Predicting Drug-Microbe Associations Based on Bridge Graph Learningd toxicity. Utilizing microbes in antibacterial development is a new focus, yet understanding their complex interactions with drugs remains a challenge. Identifying microbe-drug associations enhances understanding and accelerates drug development, benefiting research and screening efforts. Given the作者: Jacket 時間: 2025-3-28 09:23
Adaptive Weight Sampling and Graph Transformer Neural Network Framework for Cell Type Annotation of ingle cells, cell type annotation is the most common computational task in the downstream specific task. Different cell types differ in morphology, function, or biochemical properties, and these differences determine the specific function and role of cells in the organism. Traditional methods for ce作者: Circumscribe 時間: 2025-3-28 11:18
BiLETCR: An Efficient PMHC-TCR Combined Forecasting Method at predicting the binding probability between peptides and major histocompatibility complex (pMHC) with T-cell receptors (TCR), a critical aspect of cancer immunotherapy. The method specifically targets the prediction of binding specificity between neoantigens and TCR within Class I MHC complexes. 作者: AGGER 時間: 2025-3-28 17:39 作者: 鞭子 時間: 2025-3-28 21:41
ChiMamba: Predicting Chromatin Interactions Based on MambaHowever, the high cost of sequencing techniques limits the identification of chromatin interactions across diverse samples. Considering its significance, quite a few deep learning-based methods have recently emerged for computationally detecting chromatin interactions. In this study, we propose ChiM作者: 友好 時間: 2025-3-28 23:47 作者: 雄辯 時間: 2025-3-29 06:10 作者: Allure 時間: 2025-3-29 08:38
CUK-Band: A CUDA-Based Multiple Genomic Sequence Alignment on GPUgh dynamic programming. MSA is a crucial tool for temporal analyses such as classification, aggregation, and speech recognition. The process uses a penalty score to assess the similarity among the sequences, with or without gaps. In bioinformatics, MSA is widely used to identify conserved regions an作者: chiropractor 時間: 2025-3-29 12:23 作者: Collision 時間: 2025-3-29 15:43
DiagNCF: Diagnosis Neural Collaborative Filtering for Accurate Medical Recommendatione performance, they are seldom applied in medical recommendations due to their lack of interpretability. On the other hand, traditional statistical methods are easily interpretable but often limit in performance. In this study, we propose a novel framework called Diagnosis Neural Collaborative Filte作者: Suppository 時間: 2025-3-29 21:59 作者: Gum-Disease 時間: 2025-3-30 01:48
Drug Target Affinity Prediction Based on Graph Structural Enhancement and Multi-scale Topological Fedrug development. Various sequence-based and graph-based deep learning models have achieved good performance in drug target affinity (DTA) prediction, but most of them extract features at a single scale, and this approach is deficient in global topological feature extraction. In this paper, we propo作者: 擦掉 時間: 2025-3-30 05:50 作者: WAIL 時間: 2025-3-30 11:28
Fully Convolutional Neural Network for Predicting Cancer-Specific CircRNA-MiRNA Interaction SitescRNAs regulate gene expression by adsorbing miRNAs and acting as ‘sponges’. Dysregulation of miRNAs has been observed in various cancer tissues, and co-expression of circRNAs with miRNAs has been noted in many cancer tissues. The co-expression of miRNAs with circRNAs may play an important role in re作者: jagged 時間: 2025-3-30 14:32
GSDPI: An Integrated Feature Extraction Framework for Predicting Novel Drug-Protein Interactionelopment processes. However, existing DPIs prediction models still encounter challenges in efficiently extracting node features from complex networks. This paper proposed a novel DPIs prediction framework named GSDPI, in which graph neural networks (GNN) were employed to aggregate neighborhood infor作者: companion 時間: 2025-3-30 19:55 作者: 情感脆弱 時間: 2025-3-31 00:02
HyperCPI: A Novel Method Based on Hypergraph for Compound Protein Interaction Prediction with Good Gowever, existing deep learning approaches face a challenge due to the lack of representations for non-pairwise relations and substructures in compounds, leading to limited performance and poor generalization ability. To address this challenge, a novel method named HyperCPI is proposed in this study.作者: 致敬 時間: 2025-3-31 00:56
iEMNN: An Iterative Integration Method for Single-Cell Transcriptomic Data Based on Network Similarits arise from non-biological variations such as different sequencing batches, sequencing protocols, sequencing depths, and so on. Batch effects introduce systematic biases and confound biological variations of interest, which have a detrimental impact on the validity of study findings. Eliminating b作者: forbid 時間: 2025-3-31 05:34 作者: 不自然 時間: 2025-3-31 11:30 作者: JAUNT 時間: 2025-3-31 16:51 作者: 河潭 時間: 2025-3-31 17:37
CDDTR: Cross-Domain Autoencoders for Predicting Cell Type Specific Drug-Induced Transcriptional Respextracted by 10-fold cross-validation have a 0.663 PCC, revealing the competence of CDDTR to predict the cross-cell type responses. By integrating perturbations from multiple cell lines and incorporating pre-training, the predictive performance of CDDTR can be further improved. Source code is availa作者: 不朽中國 時間: 2025-4-1 00:33 作者: MODE 時間: 2025-4-1 05:28 作者: 扔掉掐死你 時間: 2025-4-1 07:35 作者: 異端 時間: 2025-4-1 12:06
