Drug Repositioning makes a significant contribution to industry and research due to its ability to reduce the time and cost of
drug discovery through making use of the existing drugs. At the time of writing this research, many computational methods have been
proposed; however, few of them were able to integrate chemical space (drugs) and genomic space (targets) with disease space. In
addition, using the feature-based method in Drug Target Interaction (DTI) and Target Disease Interaction (TDI) models are not wellexploited.
Hence, developing an efficient approach in order to predict potential DTI and TDI is necessary. In this research, we introduce
an integrated computational framework to predict potential interactions of drug-target and target-disease basing on features extracted
from drugs, targets, and diseases using various learning methods (e.g., Random Forest, Decision Trees, Logistic Regression).
Published In:IJCSN Journal Volume 8, Issue 3
Date of Publication : June 2019
Pages : 331-338
Tables : 03
Eman Ismail :
Computer Science Department, Faculty of Computers and Information
Helwan University, Cairo, Egypt.
Discriminant analysis, face recognition, featureextraction, graph-based embedding, local discriminant embedding (LDE), small-sample-size (SSS) problem
For enhancing the drug repositioning problem, we
involved the target as a link between drug and disease
classes; most of the developed approaches do not address
the problem as two relations: drug-target and targetdisease.
Conducted experiments revealed that involving
the target information boosts the performance relatively.
The two models we defined, i.e., the Drug Target
Interaction (DTI) and Target Disease Interaction (TDI),
showed that the target correlated with both the drug and
disease. In addition, applying the Positive-Unlabeled (PU)
approach to obtain distribution from the unlabeled space
caused our models to be unbiased towards the positive
predictions. Using the feature-based approach for the DTI
and TDI models was an efficient solution to overcome the
limitations practiced in the similarity and network
approaches. Although Drug Repositioning is an efficient
way to shorten the drug discovery process, we still need,
not surprisingly, input from expertise in biochemistry to
validate our findings.
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