標題: Titlebook: Intelligent Computing Theories and Application; 16th International C De-Shuang Huang,Kang-Hyun Jo Conference proceedings 2020 Springer Natu [打印本頁] 作者: CHORD 時間: 2025-3-21 18:46
書目名稱Intelligent Computing Theories and Application影響因子(影響力)
書目名稱Intelligent Computing Theories and Application影響因子(影響力)學科排名
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書目名稱Intelligent Computing Theories and Application網(wǎng)絡公開度學科排名
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書目名稱Intelligent Computing Theories and Application被引頻次學科排名
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書目名稱Intelligent Computing Theories and Application讀者反饋
書目名稱Intelligent Computing Theories and Application讀者反饋學科排名
作者: Rejuvenate 時間: 2025-3-21 22:34 作者: DEI 時間: 2025-3-22 02:43
Na Gao,Chen Qiao,Shun Qi,Kai Ren,Jian Chen,Hanfeng Fangmuliert, hat sich der Ausdruck in der neueren Entwicklungsphysiologie auf zoologischer Seite fest eingebürgert; er wird auch von Winkler in seiner Zusammenfassung der Ergebnisse der pflanzlichen Entwicklungsphysiologie (1913) in diesem Sinne verwendet, nachdem bisher in der botanischen Literatur ein作者: 不愿 時間: 2025-3-22 06:34 作者: FLACK 時間: 2025-3-22 11:17 作者: CBC471 時間: 2025-3-22 15:23
Tumor Gene Selection and Prediction via Supervised Correlation Analysis Based F-Score Methodposed. Firstly, all the genes are sorted as their descending F-scores, and then supervised correlation analysis is also recommended to reduce the redundancy from those selected genes. At last SVM is introduced to classify those gene subsets. Some experiments are conducted on benchmark tumor gene exp作者: single 時間: 2025-3-22 18:46 作者: 斷言 時間: 2025-3-23 01:07
Multi-omics Classification on Kidney Samples Exploiting Uncertainty-Aware Modelsconsists of creating a classifier for each omic and subsequently making a consensus among the classifiers that assigns to each sample the most voted class among the outputs on the individual omics..However, this approach does not consider the confidence in the prediction ignoring that a biological i作者: Eclampsia 時間: 2025-3-23 02:28 作者: dermatomyositis 時間: 2025-3-23 07:26
Exploring lncRNA-MRNA Regulatory Modules Based on lncRNA Similarity in Breast Cancerincluding breast cancer. The development of computational methods to identify lncRNA-related modules is a powerful tool to reveal the role of lncRNA in diseases. Here, we proposed a novel computational method called BCLMM to identify lncRNA-mRNA modules of breast cancer based on lncRNA similarity. F作者: reception 時間: 2025-3-23 11:40 作者: 歡笑 時間: 2025-3-23 16:27 作者: poliosis 時間: 2025-3-23 21:27
Predicting ,-, Transcription Factor Binding Sites with Deep Embedding Convolution Networkf discovery, In this paper, we propose a novel neural network based architecture i.e. eDeepCNN, combining multi-layer convolution network and embedding layer for predicting in-vitro DNA protein binding sequence. Our model fully utilize fitting capacity of deep convolution neural network and is well 作者: amenity 時間: 2025-3-24 00:00
Prediction of Membrane Protein Interaction Based on Deep Residual Learninger predict its spatial structure. Therefore, it is of great significance to study the interaction of membrane proteins. Currently, there is no contact method specifically for membrane protein prediction. In this paper, a membrane protein prediction tool based on deep residual learning is established作者: 效果 時間: 2025-3-24 04:15
GCNSP: A Novel Prediction Method of Self-Interacting Proteins Based on Graph Convolutional Networksf great significance for the exploration of new gene functions, protein function research and proteomics research. Although a large number of SIPs have been confirmed with the rapid development of high-throughput technology, the biological experimental method is still limited by blindness and high c作者: bibliophile 時間: 2025-3-24 06:34
Predicting Protein-Protein Interactions from Protein Sequence Using Locality Preserving Projections ut technologies have been proposed to detect the PPIs in past decades. However, they have some drawbacks such as time-consuming and high cost, and at the same time, a high rate of false positive is also unavoidable. Hence, developing an efficient computational method for predicting PPIs is very nece作者: 小歌劇 時間: 2025-3-24 12:11 作者: Mammal 時間: 2025-3-24 15:55 作者: 者變 時間: 2025-3-24 22:11
Yan Cui,Huacheng Gao,Rui Zhang,Yuanyuan Lu,Yuan Xue,Chun-Hou Zheng作者: delusion 時間: 2025-3-24 23:39 作者: 不透氣 時間: 2025-3-25 04:03
Ying He,Zhen Shen,Qinhu Zhang,Siguo Wang,Changan Yuan,Xiao Qin,Hongjie Wu,Xingming Zhao作者: BOOR 時間: 2025-3-25 08:54
Siguo Wang,Zhen Shen,Ying He,Qinhu Zhang,Changan Yuan,Xiao Qin,Hongjie Wu,Xingming Zhao作者: 口訣 時間: 2025-3-25 15:39 作者: Compatriot 時間: 2025-3-25 16:07 作者: Mutter 時間: 2025-3-25 20:09
Lei Wang,Zhu-Hong You,Xin Yan,Kai Zheng,Zheng-Wei Li作者: Rankle 時間: 2025-3-26 02:15 作者: Tinea-Capitis 時間: 2025-3-26 04:48 作者: Morose 時間: 2025-3-26 11:56 作者: addict 時間: 2025-3-26 16:11
Lei Tian,Shu-Lin Wang,Xing Zhonguswirkungen, die sich aus dem Wandel des industriellen Systems der Massenproduktion ergeben. Gerade weil in diesen Arbeiten die Frage nach der hier im Mittelpunkt stehenden Dynamik staatlicher Prozesse meist vernachl?ssigt wird, erscheint die Forderung nach einer Integration derjenigen Teiltheorien 作者: 放肆的你 時間: 2025-3-26 17:51
Na Gao,Chen Qiao,Shun Qi,Kai Ren,Jian Chen,Hanfeng Fangung der Begriffe h?ngt das Urteil über die Bedeutung der ?Regeneration“ für das Pflanzenreich wie die Eingliederung eines bestimmten Einzelvorgangs ab. Wer die Wiederherstellung gest?rter Formganzheit als ?Regeneration“ bezeichnet, wird dieselbe Fülle von Beispielen für dieses Regulations-geschehen 作者: Deject 時間: 2025-3-26 22:03 作者: 一個姐姐 時間: 2025-3-27 04:06 作者: 愉快么 時間: 2025-3-27 06:50
Inference Method for Reconstructing Regulatory Networks Using Statistical Path-Consistency Algorithmroteins is determined by the statistical measures based on the generated networks using different variable orders. Our proposed algorithm is evaluated by using the golden standard networks in Dream challenges and a cell signalling transduction pathway by using experimental data. Inference results su作者: 死貓他燒焦 時間: 2025-3-27 10:38 作者: 總 時間: 2025-3-27 15:16 作者: CRACY 時間: 2025-3-27 19:16 作者: 蜈蚣 時間: 2025-3-27 23:09 作者: inferno 時間: 2025-3-28 03:05
Biomarkers Selection of Abnormal Functional Connections in Schizophrenia with ,-Norm Based Sparse Reethod. The .-norm sparse regularization feature selection method can be used to select features that are crucial for discrimination, and then explore the abnormal functional connections of schizophrenia with fMRI data. Our results showed that the abnormal functional connections are related to superi作者: 魔鬼在游行 時間: 2025-3-28 07:38 作者: Compatriot 時間: 2025-3-28 10:41 作者: Agnosia 時間: 2025-3-28 15:41 作者: 品牌 時間: 2025-3-28 21:01
Conference proceedings 2020s conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, addressing theories, methodologies, and applications in science and technology..作者: Nefarious 時間: 2025-3-28 23:38
0302-9743 me for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, addressing theories, methodologies, and applications in science and technology..978-3-030-60801-9978-3-030-60802-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 容易生皺紋 時間: 2025-3-29 05:58
A Machine Learning Based Method to Identify Differentially Expressed Geneswell as other methods. A validation on the Platinum Spike dataset indicates that the proposed approach is more reliable with high confidence in identifying DEGs. An analysis of the biological function of the identified genes illustrates that the designed ensemble technique is powerful for identifying biologically relevant expression changes.作者: Pepsin 時間: 2025-3-29 08:18
A New Method Combining DNA Shape Features to Improve the Prediction Accuracy of Transcription Factorch filter to improve the prediction accuracy of TFBSs. We conduct a series of experiments on 66 . datasets and experimental results show that proposed model DCDS is superior to some state-of-the-art methods.作者: 沖擊力 時間: 2025-3-29 14:05 作者: Lumbar-Spine 時間: 2025-3-29 17:08 作者: 大氣層 時間: 2025-3-29 22:56
A Novel Clustering-Framework of Gene Expression Data Based on the Combination Between Deep Learning but also reduced the dimensionality of raw data effectively without any prior knowledge. The clustering results obtained from this method based on four gene datasets exhibited an impressive performance in efficiency and accuracy.作者: 窗簾等 時間: 2025-3-30 01:15
Tumor Gene Selection and Prediction via Supervised Correlation Analysis Based F-Score Methodndancy from those selected genes. At last SVM is introduced to classify those gene subsets. Some experiments are conducted on benchmark tumor gene expressive data sets and results show the performance of the proposed method.作者: Incorporate 時間: 2025-3-30 05:37 作者: 厚顏無恥 時間: 2025-3-30 08:44 作者: cochlea 時間: 2025-3-30 13:11
