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Computational prediction of drug–target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions. [ABSTRACT FROM AUTHOR]

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression , resulting in two variants of ensemble models, EnsemDT and EnsemKRR , respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT . In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. [ABSTRACT FROM AUTHOR]

Background: Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. Results: We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully. Conclusions: Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data. [ABSTRACT FROM AUTHOR]

Background Acute upper gastrointestinal bleeding (UGIB) is a common medical emergency presented to the emergency department that requires early assessment and management. Many risk stratification scores have been developed to predict the clinical outcomes in patients with UGIB. The commonly used risk scores the Rockall scoring systems (PRS and FRS), Glasgow--Blatchford score (GBS) and AIMS65 score. Aim The aim of the present study was to assess and compare the ability of the wildly used risk scores the RS, GBS, and AIMS65 to predict the clinical outcomes in UGIB patients Patients and methods One hundred patients (age >18 years) with acute UGIB in the emergency department of Alexandria Main University Hospital were prospectively studied. All the study scores were calculated and compared using the area under the receiver operating characteristic curve (AUC) method to evaluate the performance of each score to predict the mortality, blood transfusion, endoscopic intervention, ICU admission, rebleeding, and length of hospital stay. Results Among the one hundred patients included in the study, 65% were males with a median of age 58 years. 56% had esophageal varices and 63% with liver disease. All the used scores were statistically significant in predicting all clinical outcomes. GBS had the best AUC among the AIMS65, PRS, and FRS scores in predicting mortality with (AUC= 0.80 vs. 0.76, 0.69), blood transfusion need with (AUC= 0.92 vs. 0.88, 0.87), ICU admission with (AUC= 0.86 vs. 0.83, 0.81), rebleeding with (AUC= 0.81 vs. 0.77, 0.69), and length of hospital stay with (AUC= 0.81 vs. 0.75. 0.79). Conclusion All the study scores (GBS, AIMS65, PRS, and FRS) were able to predict the clinical outcomes in the UGIB patients. GBS was the best performing risk score among the four scores for predicting all the clinical outcomes (mortality, blood transfusion, rebleeding, ICU admission, and length of hospital stay) except the prediction of endoscopic intervention in our study population. [ABSTRACT FROM AUTHOR]

Background: Lower body contouring after massive weight loss exhibits a great challenge for the plastic surgeon. Aim of the work: is to assess surgical outcomes of lower body contouring after massive weight loss. Methods: This prospective cohort study was conducted at Plastic Surgery Unit, General Surgery Department, Faculty of Medicine, Zagazig University Hospitals on ten patients who underwent lower body contouring after massive weight loss. Full medical assessment, scar placement and meticulous marking, surgical procedure and post-operative care. Results: The mean of patient satisfaction on a scale from zero to 10 was 9 ± 1.26, most of operated cases had no complications (70 %), and there was significant positive correlation between lipoaspirate amount and hospital stay. Conclusions: Lower body contouring is an extremely effective procedure to restore body contour in post bariatric patients. Careful preoperative marking, team work and few surgical tips are the keys for a procedure that is both satisfactory for the surgeon and safe for the patient. [ABSTRACT FROM AUTHOR]

The hepatitis C pandemic has been systematically studied and characterized in North America and Europe, but this important public health problem has not received equivalent attention in other regions. The objective of this systematic review was to characterize hepatitis C virus (HCV) epidemiology in selected countries of Asia, Australia and Egypt, i.e. in a geographical area inhabited by over 40% of the global population. Data references were identified through indexed journals and non-indexed sources. In this work, 7770 articles were reviewed and 690 were selected based on their relevance. We estimated that 49.3-64.0 million adults in Asia, Australia and Egypt are anti-HCV positive. China alone has more HCV infections than all of Europe or the Americas. While most countries had prevalence rates from 1 to 2% we documented several with relatively high prevalence rates, including Egypt (15%), Pakistan (4.7%) and Taiwan (4.4%). Nosocomial infection, blood transfusion (before screening) and injection drug use were identified as common risk factors in the region. Genotype 1 was common in Australia, China, Taiwan and other countries in North Asia, while genotype 6 was found in Vietnam and other Southeast Asian countries. In India and Pakistan genotype 3 was predominant, while genotype 4 was found in Middle Eastern countries such as Egypt, Saudi Arabia and Syria. We recommend implementation of surveillance systems to guide effective public health policy that may lead to the eventual curtailment of the spread of this pandemic infection. [ABSTRACT FROM AUTHOR]