Karim Abbasi | Sharif University of Technology (original) (raw)

Papers by Karim Abbasi

Research paper thumbnail of A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data

Research paper thumbnail of TripletMultiDTI: Multimodal representation learning in drug-target interaction prediction with triplet loss function

Expert Systems with Applications

Research paper thumbnail of DeepTraSynergy: drug combinations using multimodal deep learning with transformers

Bioinformatics

Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidiscip... more Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synerg...

Research paper thumbnail of DeepTraSynergy: drug combinations using multimodal deep learning with transformers

Bioinformatics

Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidiscip... more Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synerg...

Research paper thumbnail of DeepTraSynergy: drug combinations using multimodal deep learning with transformers

Bioinformatics

Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidiscip... more Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synerg...

Research paper thumbnail of Multivariate pattern recognition by machine learning methods

Research paper thumbnail of Multimodal brain tumor detection using multimodal deep transfer learning

Research paper thumbnail of TripletMultiDTI: Multimodal Representation Learning in Drug-Target Interaction Prediction

BackgroundIn drug discovery, drug-target interaction (DTI) plays a crucial role. Identifying DTI ... more BackgroundIn drug discovery, drug-target interaction (DTI) plays a crucial role. Identifying DTI in a wet-lab experiment is time-consuming, labor-intensive, and costly. Using reliable computational methods to predict DTI mitigates the enormous costs and time of drug discovery. Deep learning-based methods for predicting DTI have recently gained more attention. ResultsIn this paper, a new multimodal approach to DTI is proposed. It is shown that a discriminative feature representation of the drug-target pair plays the main role in multimodal DTI prediction. To achieve this goal, we propose a new multimodal approach that utilizes triplet loss jointly with the prediction loss. The proposed approach is abbreviately called TripletMultiDTI. The proposed approach has two main contributions: a new architecture that fuses the multimodal knowledge to predict interaction affinity labels and a new loss function that utilizes the triplet loss. Triplet loss encourages clustering of feature space su...

Research paper thumbnail of BACE-1 Design of a bioinformatics model to predict drug compound properties and its application in inhibition of HIV replication and BACE-1 میرک ،یسابع یلع

In this paper, a new method for the problem of predicting the compound molecule properties in the... more In this paper, a new method for the problem of predicting the compound molecule properties in the lead optimization step in drug design is presented. In the lead optimization step, the amount of available biological data on small molecule compounds is low. In recent years, this challenge has been considered and transfer learning and deep learning techniques have been used to solve it. For this purpose, similar data sets have been used as auxiliary data to learn a reliable model. In this method, compound feature extraction plays an essential role in transferring knowledge from similar (auxiliary) data sets to the target data set. In this paper, the effect of using Edge weighted Graph Convolutional Network (EGCN) is assessed which able to consider the feature vector of the compound bond as well as the atom feature vector. To evaluate the method, we have applied the proposed approach on BACE and HIV datasets. The obtained results show that the proposed method is able to extract more ef...

Research paper thumbnail of AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders

BMC Bioinformatics, 2021

Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–t... more Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. Results This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matri...

Research paper thumbnail of Incorporating part-whole hierarchies into fully convolutional network for scene parsing

Expert Systems with Applications, 2020

Research paper thumbnail of Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives

Current Medicinal Chemistry, 2021

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational ... more Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a...

Research paper thumbnail of DeepCDA: deep cross-domain compound–protein affinity prediction through LSTM and convolutional neural networks

Bioinformatics, 2020

Motivation An essential part of drug discovery is the accurate prediction of the binding affinity... more Motivation An essential part of drug discovery is the accurate prediction of the binding affinity of new compound–protein pairs. Most of the standard computational methods assume that compounds or proteins of the test data are observed during the training phase. However, in real-world situations, the test and training data are sampled from different domains with different distributions. To cope with this challenge, we propose a deep learning-based approach that consists of three steps. In the first step, the training encoder network learns a novel representation of compounds and proteins. To this end, we combine convolutional layers and long-short-term memory layers so that the occurrence patterns of local substructures through a protein and a compound sequence are learned. Also, to encode the interaction strength of the protein and compound substructures, we propose a two-sided attention mechanism. In the second phase, to deal with the different distributions of the training and te...

Research paper thumbnail of Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery

Journal of Chemical Information and Modeling, 2019

Research paper thumbnail of Modality adaptation in multimodal data

Expert Systems with Applications, 2021

Abstract Recently, multimodal data has received much attention. In classical machine learning, it... more Abstract Recently, multimodal data has received much attention. In classical machine learning, it is assumed that all data comes from one modality while in multimodal machine learning, the information comes from different modalities. In multimodal machine learning, transiting, or fusing knowledge from different modalities is an important step. Hence, in these steps, the different marginal distributions between different modalities should be taken into account. However, in recent years, modality adaptation has not gotten enough attention. The motivation of this work is to consider modality adaptation to effectively encode the shared common or complementary knowledge in multimodal data. To reduce the modality shift, we present a new perspective on the modality adaptation algorithm. In multimodal data, by applying the existing domain adaptation techniques to reduce the modality shift, a problem arises because of the insufficient capability of those techniques in preserving complementary knowledge. Our proposed modality adaptation is designed such that it simultaneously considers both the shared and complementary knowledge of each modality while preserving the discriminative ability of each modality in the label space. To evaluate the proposed approach, we have applied it to two different multimodal applications: multi-view object detection and RGBD image semantic segmentation. Our results show that the proposed modality adaptation technique is successful in transferring and fusing knowledge.

Research paper thumbnail of Transfer subspace learning via low-rank and discriminative reconstruction matrix

Knowledge-Based Systems, 2019

Abstract In this paper, we investigate the unsupervised domain transfer learning in which there i... more Abstract In this paper, we investigate the unsupervised domain transfer learning in which there is no label in the target samples whereas the source samples are all labeled. We use the transformation matrix to transfer both target and source samples to a common subspace where they have the same distribution and each target sample in the transformed space is constructed of a linear combination of the source samples. To preserve the local and global structure of the samples in the transferred domain, the low-rank and sparse constraints are imposed on the reconstruction coefficient matrix. In this paper, in order to consider the discriminative ability of the target and source samples, the information content of the reconstruction coefficient matrix is utilized. To capture the discriminative ability of the target samples, it is assumed that the class labels of the source samples which are linearly incorporated in constructing a target sample should be the same. Based on this assumption, it is assured that the target samples are well distributed over the transferred domain. To handle this, we utilize the linear entropy to measure the discriminant power of the target domain. This term considers the discriminative ability of the target samples without using their hidden labels. Also, to assess the discriminative ability of source samples, we use max-margin classifier where the kernel matrix is defined by using the reconstruction coefficient matrix. To evaluate the proposed approach, it is applied on MSRC, VOC 2007, CMU PIE, Office, Caltech-256, Extended Yale B and two imbalanced datasets. The experimental results show that our proposed approach outperforms its competitors.

Research paper thumbnail of Learning Spatial Hierarchies of High-level Features in Deep Neural Network

Journal of Visual Communication and Image Representation

Abstract This paper addresses a new approach to learn perceptual grouping of the extracted featur... more Abstract This paper addresses a new approach to learn perceptual grouping of the extracted features of the convolutional neural network (CNN) to represent the structure contained in the image. In CNN, the spatial hierarchies between the high-level features are not taken into account. To do so, the perceptual grouping of features is utilized. To consider the intra-relationship between feature maps, modified Guided Co-occurrence Block (mGCoB) is proposed. This block preserves the joint co-occurrence of two features in the spatial domain and it prevents the co-adaptation. Also, to preserve the interrelationship in each feature map, the principle of common region grouping is utilized which states that the features which are located in the same feature map tend to be grouped together. To consider it, an MFC block is proposed. To evaluate the proposed approach, it is applied to some known semantic segmentation and image classification datasets that achieve superior performance.

Research paper thumbnail of A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data

Research paper thumbnail of TripletMultiDTI: Multimodal representation learning in drug-target interaction prediction with triplet loss function

Expert Systems with Applications

Research paper thumbnail of DeepTraSynergy: drug combinations using multimodal deep learning with transformers

Bioinformatics

Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidiscip... more Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synerg...

Research paper thumbnail of DeepTraSynergy: drug combinations using multimodal deep learning with transformers

Bioinformatics

Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidiscip... more Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synerg...

Research paper thumbnail of DeepTraSynergy: drug combinations using multimodal deep learning with transformers

Bioinformatics

Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidiscip... more Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synerg...

Research paper thumbnail of Multivariate pattern recognition by machine learning methods

Research paper thumbnail of Multimodal brain tumor detection using multimodal deep transfer learning

Research paper thumbnail of TripletMultiDTI: Multimodal Representation Learning in Drug-Target Interaction Prediction

BackgroundIn drug discovery, drug-target interaction (DTI) plays a crucial role. Identifying DTI ... more BackgroundIn drug discovery, drug-target interaction (DTI) plays a crucial role. Identifying DTI in a wet-lab experiment is time-consuming, labor-intensive, and costly. Using reliable computational methods to predict DTI mitigates the enormous costs and time of drug discovery. Deep learning-based methods for predicting DTI have recently gained more attention. ResultsIn this paper, a new multimodal approach to DTI is proposed. It is shown that a discriminative feature representation of the drug-target pair plays the main role in multimodal DTI prediction. To achieve this goal, we propose a new multimodal approach that utilizes triplet loss jointly with the prediction loss. The proposed approach is abbreviately called TripletMultiDTI. The proposed approach has two main contributions: a new architecture that fuses the multimodal knowledge to predict interaction affinity labels and a new loss function that utilizes the triplet loss. Triplet loss encourages clustering of feature space su...

Research paper thumbnail of BACE-1 Design of a bioinformatics model to predict drug compound properties and its application in inhibition of HIV replication and BACE-1 میرک ،یسابع یلع

In this paper, a new method for the problem of predicting the compound molecule properties in the... more In this paper, a new method for the problem of predicting the compound molecule properties in the lead optimization step in drug design is presented. In the lead optimization step, the amount of available biological data on small molecule compounds is low. In recent years, this challenge has been considered and transfer learning and deep learning techniques have been used to solve it. For this purpose, similar data sets have been used as auxiliary data to learn a reliable model. In this method, compound feature extraction plays an essential role in transferring knowledge from similar (auxiliary) data sets to the target data set. In this paper, the effect of using Edge weighted Graph Convolutional Network (EGCN) is assessed which able to consider the feature vector of the compound bond as well as the atom feature vector. To evaluate the method, we have applied the proposed approach on BACE and HIV datasets. The obtained results show that the proposed method is able to extract more ef...

Research paper thumbnail of AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders

BMC Bioinformatics, 2021

Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–t... more Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. Results This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matri...

Research paper thumbnail of Incorporating part-whole hierarchies into fully convolutional network for scene parsing

Expert Systems with Applications, 2020

Research paper thumbnail of Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives

Current Medicinal Chemistry, 2021

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational ... more Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a...

Research paper thumbnail of DeepCDA: deep cross-domain compound–protein affinity prediction through LSTM and convolutional neural networks

Bioinformatics, 2020

Motivation An essential part of drug discovery is the accurate prediction of the binding affinity... more Motivation An essential part of drug discovery is the accurate prediction of the binding affinity of new compound–protein pairs. Most of the standard computational methods assume that compounds or proteins of the test data are observed during the training phase. However, in real-world situations, the test and training data are sampled from different domains with different distributions. To cope with this challenge, we propose a deep learning-based approach that consists of three steps. In the first step, the training encoder network learns a novel representation of compounds and proteins. To this end, we combine convolutional layers and long-short-term memory layers so that the occurrence patterns of local substructures through a protein and a compound sequence are learned. Also, to encode the interaction strength of the protein and compound substructures, we propose a two-sided attention mechanism. In the second phase, to deal with the different distributions of the training and te...

Research paper thumbnail of Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery

Journal of Chemical Information and Modeling, 2019

Research paper thumbnail of Modality adaptation in multimodal data

Expert Systems with Applications, 2021

Abstract Recently, multimodal data has received much attention. In classical machine learning, it... more Abstract Recently, multimodal data has received much attention. In classical machine learning, it is assumed that all data comes from one modality while in multimodal machine learning, the information comes from different modalities. In multimodal machine learning, transiting, or fusing knowledge from different modalities is an important step. Hence, in these steps, the different marginal distributions between different modalities should be taken into account. However, in recent years, modality adaptation has not gotten enough attention. The motivation of this work is to consider modality adaptation to effectively encode the shared common or complementary knowledge in multimodal data. To reduce the modality shift, we present a new perspective on the modality adaptation algorithm. In multimodal data, by applying the existing domain adaptation techniques to reduce the modality shift, a problem arises because of the insufficient capability of those techniques in preserving complementary knowledge. Our proposed modality adaptation is designed such that it simultaneously considers both the shared and complementary knowledge of each modality while preserving the discriminative ability of each modality in the label space. To evaluate the proposed approach, we have applied it to two different multimodal applications: multi-view object detection and RGBD image semantic segmentation. Our results show that the proposed modality adaptation technique is successful in transferring and fusing knowledge.

Research paper thumbnail of Transfer subspace learning via low-rank and discriminative reconstruction matrix

Knowledge-Based Systems, 2019

Abstract In this paper, we investigate the unsupervised domain transfer learning in which there i... more Abstract In this paper, we investigate the unsupervised domain transfer learning in which there is no label in the target samples whereas the source samples are all labeled. We use the transformation matrix to transfer both target and source samples to a common subspace where they have the same distribution and each target sample in the transformed space is constructed of a linear combination of the source samples. To preserve the local and global structure of the samples in the transferred domain, the low-rank and sparse constraints are imposed on the reconstruction coefficient matrix. In this paper, in order to consider the discriminative ability of the target and source samples, the information content of the reconstruction coefficient matrix is utilized. To capture the discriminative ability of the target samples, it is assumed that the class labels of the source samples which are linearly incorporated in constructing a target sample should be the same. Based on this assumption, it is assured that the target samples are well distributed over the transferred domain. To handle this, we utilize the linear entropy to measure the discriminant power of the target domain. This term considers the discriminative ability of the target samples without using their hidden labels. Also, to assess the discriminative ability of source samples, we use max-margin classifier where the kernel matrix is defined by using the reconstruction coefficient matrix. To evaluate the proposed approach, it is applied on MSRC, VOC 2007, CMU PIE, Office, Caltech-256, Extended Yale B and two imbalanced datasets. The experimental results show that our proposed approach outperforms its competitors.

Research paper thumbnail of Learning Spatial Hierarchies of High-level Features in Deep Neural Network

Journal of Visual Communication and Image Representation

Abstract This paper addresses a new approach to learn perceptual grouping of the extracted featur... more Abstract This paper addresses a new approach to learn perceptual grouping of the extracted features of the convolutional neural network (CNN) to represent the structure contained in the image. In CNN, the spatial hierarchies between the high-level features are not taken into account. To do so, the perceptual grouping of features is utilized. To consider the intra-relationship between feature maps, modified Guided Co-occurrence Block (mGCoB) is proposed. This block preserves the joint co-occurrence of two features in the spatial domain and it prevents the co-adaptation. Also, to preserve the interrelationship in each feature map, the principle of common region grouping is utilized which states that the features which are located in the same feature map tend to be grouped together. To consider it, an MFC block is proposed. To evaluate the proposed approach, it is applied to some known semantic segmentation and image classification datasets that achieve superior performance.