Karim Abbasi

Karim Abbasi
Sharif University of Technology | SHARIF · Department of Computer Engineering

PostDoc Position

About

11
Publications
3,339
Reads
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169
Citations
Citations since 2016
11 Research Items
168 Citations
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Introduction
My current research interests focus on two topics: Deep Learning in Drug Discovery, Durg-Target Interaction prediction

Publications

Publications (11)
Article
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...
Article
MRI brain image analysis, including brain tumor detection, is a challenging task. MRI images are multimodal, and in recent years, multimodal medical image analysis has gotten more attention. Modes refer to data from multiple sources which are semantically correlated and sometimes provide complementary information to each other. In this paper, modal...
Preprint
Background In 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 mor...
Article
Full-text available
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 t...
Article
Full-text available
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 r...
Article
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 the...
Article
Full-text available
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gotten 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...
Article
In this paper, a new approach to scene parsing is proposed which integrates part-whole hierarchies relationship in the last feature map to assign a semantic class label to each pixel. Recently, deep learning-based approaches have had a great impact on scene parsing. However, these methods could not preserve the spatial information about the high-le...
Article
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 differ...
Article
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...

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Projects

Project (1)
Archived project
Drug-target interaction