Shobeir Fakhraei

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We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence(More)
Drug-target interaction studies are important because they can predict drugs' unexpected therapeutic or adverse side effects. In silico predictions of potential interactions are valuable and can focus effort on in vitro experiments. We propose a prediction framework that represents the problem using a bipartite graph of drug-target interactions augmented(More)
The high development cost and low success rate of drug discovery from new compounds highlight the need for methods to discover alternate therapeutic effects for currently approved drugs. Computational methods can be effective in focusing efforts for such drug repurposing. In this paper, we propose a novel drug-target interaction prediction framework based(More)
Detecting unsolicited content and the spammers who create it is a long-standing challenge that affects all of us on a daily basis. The recent growth of richly-structured social networks has provided new challenges and opportunities in the spam detection landscape. Motivated by the social network, we develop methods to identify spammers in(More)
This paper describes the process of increasing reimbursement rates for physicians who provide services to Maryland Medicaid enrollees. It compares Maryland Medicaid reimbursement rates for physicians with Medicare fees in Maryland and Medicaid reimbursement rates of other states. It also provides an analysis of the impact of the increases in reimbursement(More)
As the amount of recorded digital information increases, there is a growing need for flexible recommender systems which can incorporate richly structured data sources to improve recommendations. In this paper, we show how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid recommender(More)
Many informative aspects of medical datasets may be extracted from comparative study of features discriminative power. Recently, consensus feature rankings have been proposed to achieve robust, unbiased and reliable rankings of attributes. We have studied the effect of classifier inclusion in a consensus feature ranking method for a medical dataset with(More)
Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values(More)
Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings(More)
Prior to neurosurgical resection of abnormal brain tissues in mTLE patients, focal points of the seizure should be identified via a set of examinations. Once decisive evidence is not present in noninvasive clinical profile of mTLE patients, extraoperative Electrocorticography (ECoG) is required which is the practice of using electrodes placed directly on(More)