Although advances in high-throughput omics technologies revolutionized our understanding of the genomic underpinnings of cancer, there are still many challenges in understanding how patients with common driver mutations may display diverging ...

Personalized relevance parameterization methods (PReP-AD) based on artificial intelligence computation techniques are introduced to investigate the impact of gene expressions on Alzheimer's disease (AD) progression. Our PReP-AD methods make use of the ...

Electronic Medical Records (EMRs) collect and describe events and patient health history, related to his interaction with a healthcare facility or clinical trials. Raw data in EMRs are voluminous and heterogeneous. They need to be collected and stored ...

Optimization of combination antiretroviral therapy (CART) for treatment of human immunodeficiency virus (HIV) infection usually targets achievement of suppression of plasma viral load at specific time points after therapy initiation (e.g. 12 or 24 weeks)...

A large percentage of emergency department (ED) visits originates from a small percentage of patients who keep returning to the ED. Being able to flag these frequent users in advance can help clinicians to take appropriate interventions to reduce the ...

In the realm of data driven clinical research, medical concepts, or phenotypes, are used to serve as indicators for patient clusters of interest. Often, studies will use groups of algorithmically generated phenotypes (feature groups) to predict the ...

With the explosion of data in healthcare, there is a growing need to develop intelligent methods for automatically mining and implementing analyses from these data. In clinical applications, longitudinal patient records are often stored in disparate ...

We have developed an analytics environment, which uses clinical data from an Enterprise Data Warehouse to construct diagnostic models for use in clinical settings. We have focused on models based on Bayesian networks. The resulting system allows ...

Several graph datasets exist which have additional attributes, representing properties of either nodes or edges in the graph. Recent research has focused on finding integrated or cohesive clusters where the clusters are not only densely connected but ...

DNA is a long polymer encoding information of all living organisms. It usually folds in a double-helix shape and packed in chromosomes. Nonetheless DNA exists in many other conformations, including A-DNA, B-DNA and Z-DNA. B-DNA is the most common form ...

Reconstructing genomes of organisms from high-throughput sequencing experiments without a reference genome available (de novo assembly) is a challenging problem which has been approached in several ways in the past decade. Although numerous methods are ...

Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes is therefore a growing area. Here we focus on the representation of ...

We conduct machine learning experiments on time-dependent gene expression measurements associated with the immune response to influenza in humans. We employ three partitions of the two data sets focusing on H1N1 only, H3N2 only and H1N1 and H3N2 ...

Since the development of next generation sequencing technologies, genome assembly has become one of the most computational and I/O intensive analyses done on the genomic data. The flood of genomic sequence data has increased the demand for more ...

Cancer initiation and progression are caused by"driver"mutations that are vastly outnumbered by the "passenger" mutations that accumulate due to cancer-associated genome instability. With genome-wide detection of somatic mutations now becoming ...

Metabolomics encompasses analysis of metabolites using profiling techniques such as mass spectroscopy (MS) and nuclear magnetic resonance (NMR). Statistical analysis is performed on the profiled data to determine variations in the levels of metabolites. ...

We propose a novel network querying algorithm, which adopts the context-sensitive random walk (CSRW) model and the concept of the network conductance. The proposed algorithm identifies the seed network in the target network based on the CSRW node ...

We present a novel framework for counting small sub-graph patterns in integrated genome-scale networks. An integrated network was built using the physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons ...

Prostate cancer is one of the most prevalent cancers in males in the United States and amongst the leading causes of cancer related deaths. A particularly virulent form of this disease is castration-resistant prostate cancer (CRPC), where patients no ...

In this paper, we demonstrate that in one dimensional domain, highly nonlinear reaction dynamics given by Hill function may have dramatic changes when discretization size is smaller than a critical value. Moreover, we discuss numerical methods to ...