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Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene
J Bioinform Comput Biol. 2024 Feb;22(1):2450003. doi: 10.1142/S0219720024500033.ABSTRACTIn this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated phar...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Seloua Hadiby Yamina Mohamed Ben Ali Source Type: research

Analyzing omics data based on sample network
J Bioinform Comput Biol. 2024 Feb;22(1):2450002. doi: 10.1142/S0219720024500021. Epub 2024 Mar 25.ABSTRACTIdentifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a str...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Meizhen Sheng Yanpeng Qi Zhenbo Gao Xiaohui Lin Source Type: research

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling
In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming o...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Min Gao Shaohua Jiang Weibin Ding Ting Xu Zhijian Lyu Source Type: research

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene
J Bioinform Comput Biol. 2024 Feb;22(1):2450003. doi: 10.1142/S0219720024500033.ABSTRACTIn this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated phar...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Seloua Hadiby Yamina Mohamed Ben Ali Source Type: research

Analyzing omics data based on sample network
J Bioinform Comput Biol. 2024 Feb;22(1):2450002. doi: 10.1142/S0219720024500021. Epub 2024 Mar 25.ABSTRACTIdentifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a str...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Meizhen Sheng Yanpeng Qi Zhenbo Gao Xiaohui Lin Source Type: research

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling
In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming o...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Min Gao Shaohua Jiang Weibin Ding Ting Xu Zhijian Lyu Source Type: research

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene
J Bioinform Comput Biol. 2024 Feb;22(1):2450003. doi: 10.1142/S0219720024500033.ABSTRACTIn this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated phar...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Seloua Hadiby Yamina Mohamed Ben Ali Source Type: research

Analyzing omics data based on sample network
J Bioinform Comput Biol. 2024 Feb;22(1):2450002. doi: 10.1142/S0219720024500021. Epub 2024 Mar 25.ABSTRACTIdentifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a str...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Meizhen Sheng Yanpeng Qi Zhenbo Gao Xiaohui Lin Source Type: research

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling
In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming o...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Min Gao Shaohua Jiang Weibin Ding Ting Xu Zhijian Lyu Source Type: research

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene
J Bioinform Comput Biol. 2024 Feb;22(1):2450003. doi: 10.1142/S0219720024500033.ABSTRACTIn this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated phar...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Seloua Hadiby Yamina Mohamed Ben Ali Source Type: research

Analyzing omics data based on sample network
J Bioinform Comput Biol. 2024 Feb;22(1):2450002. doi: 10.1142/S0219720024500021. Epub 2024 Mar 25.ABSTRACTIdentifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a str...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Meizhen Sheng Yanpeng Qi Zhenbo Gao Xiaohui Lin Source Type: research

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling
In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming o...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Min Gao Shaohua Jiang Weibin Ding Ting Xu Zhijian Lyu Source Type: research

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene
J Bioinform Comput Biol. 2024 Feb;22(1):2450003. doi: 10.1142/S0219720024500033.ABSTRACTIn this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated phar...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Seloua Hadiby Yamina Mohamed Ben Ali Source Type: research

Analyzing omics data based on sample network
J Bioinform Comput Biol. 2024 Feb;22(1):2450002. doi: 10.1142/S0219720024500021. Epub 2024 Mar 25.ABSTRACTIdentifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a str...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Meizhen Sheng Yanpeng Qi Zhenbo Gao Xiaohui Lin Source Type: research

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling
In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming o...
Source: Journal of Bioinformatics and Computational Biology - April 3, 2024 Category: Bioinformatics Authors: Min Gao Shaohua Jiang Weibin Ding Ting Xu Zhijian Lyu Source Type: research