標(biāo)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(yè)] 作者: Bunion 時(shí)間: 2025-3-21 19:24
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
作者: indenture 時(shí)間: 2025-3-21 22:50
Metallurgie der Ferrolegierungen effectively identify disease-related genes. By a series of experiments, we study the effect of the fusion strategies and kernel sparsification, and demonstrate that our MSMK methods outperform the state-of-art network-based algorithms. These results confirm that the multiscale module kernel is part作者: Common-Migraine 時(shí)間: 2025-3-22 03:33 作者: 弄污 時(shí)間: 2025-3-22 04:59 作者: FICE 時(shí)間: 2025-3-22 09:11
https://doi.org/10.1007/978-3-7091-4449-7 matrix based on this distance allows for implicit mapping of the data into a higher-dimensional feature space, enabling the capture of intricate nonlinear relationships. This is especially advantageous when dealing with hierarchical or tree-like structures commonly found in biological sequences. Ou作者: Ordeal 時(shí)間: 2025-3-22 15:19
https://doi.org/10.1007/978-3-7091-4449-7erior performance in enhancing spatial resolution and predicting gene expression in unmeasured areas compared to other deep learning and traditional interpolation methods. Additionally, stEnTrans can also help the discovery of spatial patterns in spatial transcriptomics and enrich to more biological作者: 動(dòng)機(jī) 時(shí)間: 2025-3-22 18:54
Joseph W. Richards A. C., Ph. D. of the stCMGAE method on three spatial transcriptomics datasets, achieving the highest ARI indices in all cases. Additionally, we obtain clearer boundaries in spatial recognition. Our source code is available at ..作者: 喚起 時(shí)間: 2025-3-22 23:59
Joseph W. Richards A. C., Ph. D.n. We utilized spatial transcriptomics data from two different tumors generated by the 10 times Genomics platform: human HER2 positive breast cancer (HER2+) and human cutaneous squamous cell carcinoma (cSCC) datasets. The experimental results demonstrate the superiority of STco compared to other met作者: 閑蕩 時(shí)間: 2025-3-23 02:55 作者: CIS 時(shí)間: 2025-3-23 08:09
https://doi.org/10.1007/978-3-642-91434-8those models, our model attains a classification accuracy of 91.6%, marking an advancement of 2.7% over SE-ResNet. Additionally, our model demonstrates an F1-score of 92.4%, exhibiting an improvement of 4.4% compared to SE-ResNet.作者: Malaise 時(shí)間: 2025-3-23 10:20
https://doi.org/10.1007/978-3-642-91434-8r better prediction. Experiments on the Cancer Drug Sensitivity Database (GDSC) show that GSDRP can effectively combine multi-omics information such as genome aberration (MUT_CNA) and gene expression (GE) with the 1-D and 2-D features of SMILES to significantly improve the accuracy of drug response 作者: 異端邪說(shuō)下 時(shí)間: 2025-3-23 17:25 作者: 我要沮喪 時(shí)間: 2025-3-23 20:08
Influence of Convection on Phase Selectiontabolite interactions. First, we build a heterogeneous network, which consists of microbe-metabolite pairs, microbial internal interaction network, and metabolite internal interaction network. Then, we utilize paired embeddings obtained from an autoencoder to extract fine-grained pairwise informatio作者: Increment 時(shí)間: 2025-3-24 01:37 作者: Pageant 時(shí)間: 2025-3-24 03:47
Theory of Nucleation and Glass Formation diseases, we applied nonnegative matrix decomposition and singular value decomposition to the adjacency matrix of the heterogeneous network. Subsequently, an unsupervised embedding model enhanced local and global information exchange between nodes using a graph convolutional network encoder within 作者: Arrhythmia 時(shí)間: 2025-3-24 06:34 作者: 玷污 時(shí)間: 2025-3-24 11:29 作者: AVOW 時(shí)間: 2025-3-24 18:24 作者: 寵愛(ài) 時(shí)間: 2025-3-24 21:47
Flat and Nested Protein Name Recognition Based on BioBERT and Biaffine Decoder, to achieve the representation of nested entities. Moreover, a smoothing strategy is applied to improve the biaffine decoder’s selection of entity boundaries, which can improve the accuracy of entity recognition. Experiments on flat and nested named entity recognition datasets containing a large num作者: PALMY 時(shí)間: 2025-3-25 03:03 作者: 擦試不掉 時(shí)間: 2025-3-25 07:13 作者: 拋物線 時(shí)間: 2025-3-25 07:35 作者: confide 時(shí)間: 2025-3-25 13:02 作者: 自然環(huán)境 時(shí)間: 2025-3-25 18:16
,Spatial Gene Expression Prediction from?Histology Images with?STco,n. We utilized spatial transcriptomics data from two different tumors generated by the 10 times Genomics platform: human HER2 positive breast cancer (HER2+) and human cutaneous squamous cell carcinoma (cSCC) datasets. The experimental results demonstrate the superiority of STco compared to other met作者: 過(guò)時(shí) 時(shí)間: 2025-3-25 19:58 作者: critic 時(shí)間: 2025-3-26 02:53
,Dendritic SE-ResNet Learning for?Bioinformatic Classification,those models, our model attains a classification accuracy of 91.6%, marking an advancement of 2.7% over SE-ResNet. Additionally, our model demonstrates an F1-score of 92.4%, exhibiting an improvement of 4.4% compared to SE-ResNet.作者: fatuity 時(shí)間: 2025-3-26 07:16 作者: vocation 時(shí)間: 2025-3-26 09:20
,CircMAN: Multi-channel Attention Networks Based on?Feature Fusion for?CircRNA-Binding Protein Site ing bidirectional gated recurrent units and self-attention. The CircMAN method achieved an AUC of 0.923 on 37 circRNA datasets. Its effectiveness was confirmed through comparisons with five different methods and multiple ablation experiments. The source code can be available at ..作者: 軍火 時(shí)間: 2025-3-26 15:50
,A Novel Combined Embedding Model Based on?Heterogeneous Network for?Inferring Microbe-Metabolite Intabolite interactions. First, we build a heterogeneous network, which consists of microbe-metabolite pairs, microbial internal interaction network, and metabolite internal interaction network. Then, we utilize paired embeddings obtained from an autoencoder to extract fine-grained pairwise informatio作者: CANON 時(shí)間: 2025-3-26 17:57 作者: 狗窩 時(shí)間: 2025-3-26 21:51 作者: flourish 時(shí)間: 2025-3-27 02:38 作者: DECRY 時(shí)間: 2025-3-27 08:45
Predicting Drug-Target Affinity Using Protein Pocket and Graph Convolution Network,a vital role in DTA due to its direct interaction with drug. With the emergence of numerous computational methods, deep learning holds great promise in this field. Currently, most deep learning methods for DTA prediction are based on sequence or two-dimensional graph data, overlooking the structural作者: ungainly 時(shí)間: 2025-3-27 10:46 作者: 總 時(shí)間: 2025-3-27 14:22 作者: IST 時(shí)間: 2025-3-27 20:10
,RFIR: A Lightweight Network for?Retinal Fundus Image Restoration, obtain high-quality retinal images. Low resolution or poor quality significantly hinders medical diagnosis, adversely affecting clinical or downstream tasks. Furthermore, medical datasets lack the vast quantity of data characteristic of natural images. Transformers, with their numerous parameters, 作者: 廚師 時(shí)間: 2025-3-28 01:09
,Gaussian Beltrami-Klein Model for?Protein Sequence Classification: A Hyperbolic Approach,ical processes. Several machine-learning approaches have been proposed to address the problems in the area. However, conventional machine learning approaches encounter limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences due to the high-dimen作者: Keratectomy 時(shí)間: 2025-3-28 04:53 作者: 是突襲 時(shí)間: 2025-3-28 06:26
,Contrastive Masked Graph Autoencoders for?Spatial Transcriptomics Data Analysis,vel. This technique enables the acquisition of gene expression profiles for each spot, constructing a spatial gene expression map. While numerous methods have been developed to integrate expression profiles and spatial information for spatial domain detection, accurate identification remains a chall作者: voluble 時(shí)間: 2025-3-28 13:38
,Spatial Gene Expression Prediction from?Histology Images with?STco,on within complex biological systems. However, the widespread application of spatial transcriptome technology in large-scale studies is hindered by its high cost and complexity. An economical alternative involves utilizing artificial intelligence to predict gene expression information from entire sl作者: Fabric 時(shí)間: 2025-3-28 18:17
,Exploration and?Visualization Methods for?Chromatin Interaction Data,nterpret a biological dataset, particularly when the data in question is not well-standardized or fully understood, such as in the case of high-throughput chromatin conformation capture or Hi-C. Using Hi-C contact lists from publicly available databases as well as supplemental data, we demonstrate t作者: fructose 時(shí)間: 2025-3-28 22:02 作者: 騎師 時(shí)間: 2025-3-29 01:14 作者: carbohydrate 時(shí)間: 2025-3-29 04:01 作者: 認(rèn)為 時(shí)間: 2025-3-29 08:24
GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug Response,ver, they failed to make full use of the drug complementary information of sequence features and graphical features when considering the SMILES(Simplified molecular input line entry system) features. In view of this, we propose a deep learning model GSDRP that effectively integrates omics data and d作者: insidious 時(shí)間: 2025-3-29 12:09
,CircMAN: Multi-channel Attention Networks Based on?Feature Fusion for?CircRNA-Binding Protein Site pathogenesis of various diseases, especially neurodegenerative diseases and cancers. Given that traditional biological experiments are often time-consuming and costly, developing computational methods for predicting circRNA-binding protein sites is crucial. Current computational methods for extract作者: 豪華 時(shí)間: 2025-3-29 16:51 作者: 桉樹(shù) 時(shí)間: 2025-3-29 21:26
,A Novel Combined Embedding Model Based on?Heterogeneous Network for?Inferring Microbe-Metabolite Inous research has established connections between various microbiomes and metabolomes through correlation and association analyses. Although traditional statistical analysis methods have been used to quantify microbe-metabolite correlations, they do not fully elucidate the biological connections betw作者: 笨拙處理 時(shí)間: 2025-3-30 03:09 作者: climax 時(shí)間: 2025-3-30 06:57 作者: 修剪過(guò)的樹(shù)籬 時(shí)間: 2025-3-30 09:21 作者: Armada 時(shí)間: 2025-3-30 15:14 作者: 技術(shù) 時(shí)間: 2025-3-30 19:44
0302-9743 held in?Kunming, 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 bioinfor作者: hair-bulb 時(shí)間: 2025-3-30 23:49 作者: Forage飼料 時(shí)間: 2025-3-31 01:17 作者: xanthelasma 時(shí)間: 2025-3-31 08:48 作者: GRILL 時(shí)間: 2025-3-31 10:00
Joseph W. Richards A. C., Ph. D.s to identify previously indistinguishable features in our large datasets and comprehensively validate their functionality. We suggest how this approach can be generalized to other visualizations of genomics data.作者: 煩人 時(shí)間: 2025-3-31 17:18
https://doi.org/10.1007/978-3-663-07302-4. These data-driven patient stratifications hold promise for personalized assessments and targeted interventions, contingent upon clinical validation. This research highlights the synergy of machine learning and statistical analysis in charting a course toward more effective QNBC management strategies.作者: Canary 時(shí)間: 2025-3-31 18:11
,Exploration and?Visualization Methods for?Chromatin Interaction Data,s to identify previously indistinguishable features in our large datasets and comprehensively validate their functionality. We suggest how this approach can be generalized to other visualizations of genomics data.作者: 拍下盜公款 時(shí)間: 2025-4-1 00:18 作者: LINES 時(shí)間: 2025-4-1 05:38 作者: 打折 時(shí)間: 2025-4-1 09:01 作者: 愛(ài)花花兒憤怒 時(shí)間: 2025-4-1 11:53 作者: mortgage 時(shí)間: 2025-4-1 17:26
P. E. Queneau B.A., B.S., E.M,H. J. Roordaan automated manner. Protein name recognition is of great research importance as an important pre-task for the automatic extraction of protein-protein interactions. Many existing recognition methods focus on flat protein name entities and have difficulty in handling nested entities. Existing nested
