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] 作者: 使委屈 時間: 2025-3-21 16:52
書目名稱Bioinformatics Research and Applications影響因子(影響力)
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書目名稱Bioinformatics Research and Applications網(wǎng)絡(luò)公開度
書目名稱Bioinformatics Research and Applications網(wǎng)絡(luò)公開度學(xué)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(xué)科排名
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書目名稱Bioinformatics Research and Applications年度引用學(xué)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(xué)科排名
作者: 寄生蟲 時間: 2025-3-21 21:22
Metallothioneins in Normal and Cancer Cellsn deep learning models, which can often reach millions, requires learning from large and diverse medical datasets to achieve the accuracy required for clinical applications. The challenges of cross-domain, decentralization, and data privacy in medical data have constrained the development of this fi作者: 哎呦 時間: 2025-3-22 01:26 作者: pacific 時間: 2025-3-22 08:25 作者: athlete’s-foot 時間: 2025-3-22 10:57
Vorl?ufiges über den Metallischen Zustandrised of diverse cell types, exhibit considerable cellular heterogeneity. Leveraging single-cell data offers a promising avenue for deciphering this complexity and enhancing the accuracy of drug response prediction. In this study, we propose the Single-Cell Deconvolution Guided method for Patient An作者: 靦腆 時間: 2025-3-22 14:36 作者: 樹木中 時間: 2025-3-22 17:36
Diffusionsartige Platzwechselvorg?nges with neurodegenerative diseases (NDDs). In theory, computational methods have the potential to facilitate the interpretation of genetic variants in NDDs on a large scale. However, individual tools often exhibit disagreements, biases, and variations in quality. As a result, the predictions derived 作者: Obscure 時間: 2025-3-22 21:17
https://doi.org/10.1007/978-3-7091-3275-3sed of contigs which are matched to fix positions on the reference genome, as well as some unmatched fragments, the purpose is to insert the unmatched fragments between the contigs in the scaffold, such that the resulting genome is similar to the reference genome. Let . be a one-to-one matching betw作者: cardiac-arrest 時間: 2025-3-23 04:29
Diffusionsartige Platzwechselvorg?nget tool for diagnosing and monitoring heart health. However, existing ECG diagnostic methods suffer from incomplete capture of spatio-temporal features and poor recognition ability for rare categories. In this paper, we introduce few-shot learning for ECG signals and propose a Residual Spatio-Tempora作者: 極力證明 時間: 2025-3-23 08:05 作者: obscurity 時間: 2025-3-23 11:45
Das magnetische Verhalten der Metalle,oteoforms. Consequently, the accuracy of identification results is crucial. Proteins with multiple primary structure alterations generate various proteoforms, leading to a combinatorial explosion due to their vast numbers. Furthermore, there is no gold set as a reference. So, enhancing the accuracy 作者: Folklore 時間: 2025-3-23 14:06 作者: 線 時間: 2025-3-23 18:02 作者: 膠狀 時間: 2025-3-24 00:00
https://doi.org/10.1007/978-981-97-5087-0Computer Science; Informatics; Conference Proceedings; Research; Applications; Applied computing; Life and作者: GLOSS 時間: 2025-3-24 04:40
978-981-97-5086-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: Vo2-Max 時間: 2025-3-24 09:14
Bioinformatics Research and Applications978-981-97-5087-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Obvious 時間: 2025-3-24 10:50
Feddaw: Dual Adaptive Weighted Federated Learning for Non-IID Medical Data,n deep learning models, which can often reach millions, requires learning from large and diverse medical datasets to achieve the accuracy required for clinical applications. The challenges of cross-domain, decentralization, and data privacy in medical data have constrained the development of this fi作者: accordance 時間: 2025-3-24 18:47
,LoopNetica: Predicting Chromatin Loops Using Convolutional Neural Networks and?Attention Mechanismsgene regulation. These loop structures facilitate interactions among enhancers, promoters, and other regulatory elements, fundamentally influencing gene expression patterns. With the advent of high-throughput technologies such as Hi-C and ChIA-PET, researchers have begun to peel back the layers of t作者: 馬籠頭 時間: 2025-3-24 19:40 作者: recession 時間: 2025-3-25 00:53 作者: 大看臺 時間: 2025-3-25 06:09 作者: FIG 時間: 2025-3-25 10:04 作者: ASTER 時間: 2025-3-25 12:27
,Improved Inapproximability Gap and?Approximation Algorithm for?Scaffold Filling to?Maximize Increassed of contigs which are matched to fix positions on the reference genome, as well as some unmatched fragments, the purpose is to insert the unmatched fragments between the contigs in the scaffold, such that the resulting genome is similar to the reference genome. Let . be a one-to-one matching betw作者: 音樂等 時間: 2025-3-25 17:30 作者: Habituate 時間: 2025-3-25 20:12
,SEMQuant: Extending Sipros-Ensemble with?Match-Between-Runs for?Comprehensive Quantitative Metaprots this understanding by measuring relative protein abundance and revealing dynamic changes under different conditions. However, the challenge of missing peptide quantification persists in metaproteomics analysis, particularly in data-dependent acquisition mode, where high-intensity precursors for MS作者: HAUNT 時間: 2025-3-26 02:38
PrSMBooster: Improving the Accuracy of Top-Down Proteoform Characterization Using Deep Learning Resoteoforms. Consequently, the accuracy of identification results is crucial. Proteins with multiple primary structure alterations generate various proteoforms, leading to a combinatorial explosion due to their vast numbers. Furthermore, there is no gold set as a reference. So, enhancing the accuracy 作者: sparse 時間: 2025-3-26 05:19
,FCMEDriver: Identifying Cancer Driver Gene by?Combining Mutual Exclusivity of?Embedded Features andtion approaches mainly exploit mutual exclusivity of mutated driver genes and integrate multi-omics data with gene function networks. Some of them identify driver genes based on the gene features learned by network embedding algorithms. However, these methods are limited to using the mutual exclusiv作者: 暫時別動 時間: 2025-3-26 11:09 作者: 人充滿活力 時間: 2025-3-26 15:50 作者: 四溢 時間: 2025-3-26 20:26
Der Thermodynamische Gleichgewichtszustandstic algorithm to predict the missing amino acids in the gaps. The experimental results on both real and simulation data show that our proposed algorithms show promising results of 100% and close to 100% accuracy.作者: 郊外 時間: 2025-3-26 21:46
Vorl?ufiges über den Metallischen Zustandtions of medium-resolution cryo-EM maps in the EMDB could benefit from potentially more reliable AlphaFold models derived later after more structural templates become available in the PDB. To study the utility of AlphaFold-predicted models, we conducted systematic mapping between the PDB and AlphaFo作者: 悠然 時間: 2025-3-27 01:44 作者: 說不出 時間: 2025-3-27 09:02
https://doi.org/10.1007/978-3-7091-3275-3ffold to maximize the number of increased duo-preservations between the filled scaffold and the reference genome. In [.], this problem was shown to be MAX-SNP-complete and can not be approximated within .. In this paper, we firstly improve the inapproximability gap to ., then we devise a new approxi作者: INCUR 時間: 2025-3-27 12:17 作者: Ibd810 時間: 2025-3-27 16:47 作者: concubine 時間: 2025-3-27 18:00 作者: 譏笑 時間: 2025-3-28 01:57
Schallschwingungen in Metallen,modules, our extensive experiments show that the Euclidean distances between learned features are highly related with the mutual exclusivity defined on the original data, and they can reveal more information compared to mutual exclusivity. Thus, we apply the Euclidean distances of learned gene featu作者: 疏忽 時間: 2025-3-28 03:49 作者: QUAIL 時間: 2025-3-28 09:44
,LoopNetica: Predicting Chromatin Loops Using Convolutional Neural Networks and?Attention Mechanismstic data, which are not always available. To overcome this problem, we propose a new deep learning computational tool called LoopNetica by utilizing a combination of one-dimensional convolutional neural networks and a multi-head attention mechanism. It can accurately predict the formation of chromat作者: EVICT 時間: 2025-3-28 12:53
,Probabilistic and?Machine Learning Models for?the?Protein Scaffold Gap Filling Problem,stic algorithm to predict the missing amino acids in the gaps. The experimental results on both real and simulation data show that our proposed algorithms show promising results of 100% and close to 100% accuracy.作者: 休戰(zhàn) 時間: 2025-3-28 15:04 作者: 占卜者 時間: 2025-3-28 19:08 作者: 領(lǐng)帶 時間: 2025-3-28 23:41
,Improved Inapproximability Gap and?Approximation Algorithm for?Scaffold Filling to?Maximize Increasffold to maximize the number of increased duo-preservations between the filled scaffold and the reference genome. In [.], this problem was shown to be MAX-SNP-complete and can not be approximated within .. In this paper, we firstly improve the inapproximability gap to ., then we devise a new approxi作者: 吹氣 時間: 2025-3-29 05:15
,Residual Spatio-Temporal Attention Based Prototypical Network for?Rare Arrhythmia Classification,work can learn useful features for classifying rare categories even with extremely limited samples of rare diseases. We evaluate our method on a large public ECG dataset, and the N-way K-shot experimental results demonstrate that RSTA-ProtoNet outperforms the state-of-the-art approaches in rare arrh作者: 使害怕 時間: 2025-3-29 09:57 作者: 初次登臺 時間: 2025-3-29 14:48
PrSMBooster: Improving the Accuracy of Top-Down Proteoform Characterization Using Deep Learning Res. Our comparison with the identification algorithm TopPIC demonstrates that PrSMBooster scores more accurately. In the vast majority of datasets, PrSM increases were observed at 1% FDR. Our findings indicate that PrSMBooster enhances scoring accuracy, reveals more identification results, and exhibit作者: 脫落 時間: 2025-3-29 16:50 作者: 退潮 時間: 2025-3-29 20:50
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: 新字 時間: 2025-3-30 01:59 作者: orthodox 時間: 2025-3-30 06:11
