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Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks
In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respe...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Hamed Ghazikhani Gregory Butler Source Type: research

Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks
In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respe...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Hamed Ghazikhani Gregory Butler Source Type: research

Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks
In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respe...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Hamed Ghazikhani Gregory Butler Source Type: research

Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks
In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respe...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Hamed Ghazikhani Gregory Butler Source Type: research

Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks
In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respe...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Hamed Ghazikhani Gregory Butler Source Type: research

Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks
In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respe...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Hamed Ghazikhani Gregory Butler Source Type: research

Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks
In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respe...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Hamed Ghazikhani Gregory Butler Source Type: research

Discovering NDM-1 inhibitors using molecular substructure embeddings representations
In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison t...
Source: Journal of integrative bioinformatics - July 27, 2023 Category: Bioinformatics Authors: Thomas Papastergiou J érôme Azé Sandra Bringay Maxime Louet Pascal Poncelet Miyanou Rosales-Hurtado Yen Vo-Hoang Patricia Licznar-Fajardo Jean-Denis Docquier Laurent Gavara Source Type: research