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Data in large-scale genetic studies of complex human diseases, such as substance use disorders, are often incomplete. Despite great progress in genotype imputation, e.g., the IMPUTE2 method, considerably less progress has been made in inferring phenotypes. We designed a novel approach to integrate individuals' comorbid conditions with their genotype data to infer missing (unreported) diagnostic criteria...
Possible drug side-effects (SEs) are usually verified by many years of repeated clinical trials. Despite the effort, some drugs are still expected to cause adverse reactions in some patients. To better predict drug SEs without having to go through the laborious processes of testing and re-testing, machine learning (ML) techniques are more and more used to uncovered patterns in drug data for such purpose...
Resting-state function magnetic resonance imaging (fMRI) images allow us to see the level of activity in a patient's brain. We consider fMRI of patients before and after they underwent a smoking cessation treatment. Two classes of patients have been studied here, that one took the drug N-acetylcysteine and the ones took a placebo. Our goal was to classify the relapse in nicotine-dependent patients...
The development of automated approaches employing computational methods using data from publicly available drugs datasets for the prediction of drug side effects has been proposed. This paper presents the use of a hybrid machine learning approach to construct side effect classifiers using an appropriate set of data features. The presented approach utilizes the perspective of data analytics to investigate...
Cocaine dependence devastates millions of human lives. Despite of a variety of treatments, there is a very high rate of individual relapse to drug use. In the last decade, functional magnetic resonance imaging (fMRI) proved to be a powerful tool to diagnose and understand different pathologies. This work provides advances in the identification of cocaine dependence and in the relapse prediction based...
It has been reported that chronic heroin intake induces changes in central nervous system of human brain; however, few studies investigate the carry-over adverse effects on brain after heroin withdrawal. In this work we examined the alpha rhythms of resting-state Electroencephalogram (EEG) signals to measure the neuroelectrical differences between the heroin addicts after heroin withdrawal and normal...
This paper demonstrates an unsupervised learning approach to identify genes with significant differential expression across single-cell subpopulations induced by therapeutic treatment. Identifying this set of genes makes it possible to use well-established bioinformatics approaches such as pathway analysis to establish their biological relevance. Then, a biologist can use his/her prior knowledge to...
Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient and...
In vitro Multi-Electrode Arrays (MEA) are an extracellular recording technology that enables the analysis of networks of neurons in vitro. Neurons in culture exhibit a range of behavioral dynamics, which can be measured in terms of individual action potentials, network-wide synchronized firing and a host of other features that characterize network activity. MEA data analysis was historically focused...
More and more people are earing about the health and medical diagnosis problems. However, according to the administration's report, more than 200 thousand people in China, even 100 thousand in USA, die each year due to medication errors. More than 42% medication errors are caused by doctors because experts write the prescription according to their experiences which are quite limited. Technologies...
We investigate the problem of finding unknown associations between 'concepts' in a given text corpus. A 'concept' is an entity, which is referred to by a phrase or multiple phrases (in case an entity has several names or synonyms, e.g. "illness", "disease"), while an 'association' is a relationship defined in a particular domain. The pairwise associations computation poses major...
Precision medicine for cancer involves design of drug sensitivity prediction models that can predict patient response to various drugs. The drug response is usually represented by a single feature such as Area Under the Curve or IC50 derived from the experimental dose response curve. In this article, we consider the idea that predicting the dose response curve and generating the curve features instead...
It is known that, for teenagers, internet addiction ratio is proportional to smartphone addiction. It means that, for same school age group, the higher internet addiction ratio is, the higher smartphone addiction ratio is. The research purpose of this paper is to investigate correlation of internet addiction and smartphone addiction of teenagers. For this purpose, extensive internet addiction survey...
It is known that man is more immersed in internet use than woman. Actually, internet addiction ratio of man is higher than internet addiction ratio of woman. The research purpose of this paper is to investigate correlation of internet addiction and gender. For this purpose, extensive and national survey works of NIA(National Information Society Agency of Korea) are gathered and analyzed. Based on...
Performance Measurement Systems (PMS) have long captured the attention of organizational behavior and information systems (IS) research. The PMS in the study was implemented by public police forces, using advanced Business Intelligence (BI) technologies. The study examines the impact of enhancing that PMS, through analysis of the metric results over an 8-year time period that covered a transition...
Breast cancer care involves a number of clinical considerations, such as relevant patient characteristics, including age, hormone-receptor status and cancer stage, and choice among several interventions, like surgery, radiation therapy, and administered drugs. Discovering these relationships in real word care is a challenging problem due to the fragmentation of relevant data among multiple information...
Network pharmacology has become the new approach for drug mechanism research and novel drug design. Drug target prediction based on computational approach became one of the primary approaches. However, due to the diversity and complexity of herbal chemical structures, the performance of herb target prediction based on chemical structure similarity is limited by the quality and the data availability...
Illicit drug use is a perennial societal problem that has serious ramifications on the health and socioeconomic well-being of a society. Its identification and estimation in communities is difficult because of the privacy laws and doctor-patient confidentiality agreements, which may limit the availability of reports and data. Various analytical strategies and research methodologies rely on surveys...
Modeling sensitivity to anti-cancer drugs is a significant challenge in the area of systems medicine. Majority of current approaches generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. In this article, we approach the problem of modeling the relationship...
Link prediction has become an important and active research topic in recent years, which is prevalent in many real-world applications. Current research on link prediction focuses on predicting one single type of links, such as friendship links in social networks, or predicting multiple types of links independently. However, many real-world networks involve more than one type of links, and different...
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