High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is sti...
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High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search.
Intrusion detection systems (IDSs) have been substantially improved in recent past. However, network attacks have become more sophisticated and increasingly complex: many of current attacks are coordinated and origina...
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Intrusion detection systems (IDSs) have been substantially improved in recent past. However, network attacks have become more sophisticated and increasingly complex: many of current attacks are coordinated and originated in multiple networks. To detect these attacks, IDSs need to obtain information on network events from multiple networks or administrative domains. This work demonstrates that a Distributed IDS (DIDS) can be composed of existing IDSs, improving the detection of misuses in a multiple network environment. We use a grid middleware for creating a service-based intrusion detection grid. We demonstrate through experimental results that the proposed DIDS allows the integration of heterogeneous existing IDSs and improves the detection of attacks by exploring the synergy between existing IDSs.
Digital breast tomosynthesis (DBT) is a three-dimensional imaging technique providing an arbitrary set of reconstruction planes in the breast with limited series of projection images. This paper describes a comparison...
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The Department of Technology Systems (TSYS) at East Carolina University has implemented a new faculty mentoring process to enable junior faculty members to learn from senior faculty members as they prepare for a caree...
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The Department of Technology Systems (TSYS) at East Carolina University has implemented a new faculty mentoring process to enable junior faculty members to learn from senior faculty members as they prepare for a career in academia. Five tenured professors and associate professors in the TSYS department have recently established a new process to mentor faculty members within the department who have begun not yet completed their initial tenure process at a major university. This process includes a series of workshops regarding academic performance expectations during the early stages of an academic career. Our commitment to new faculty takes on several related, yet distinct features. Examples of these initiatives include: 1) reduced workloads for new faculty for the first two years to enable them to generate individual and collaborative research activities, funded grants, and publications, 2) periodic university-wide training to learn policies and procedures that affect day-to-day activities on a college campus, 3) periodic workshops hosted by senior faculty mentors, and 4) one-on-one discussions between senior faculty members and junior faculty members to encourage candid dialogue between professional colleagues. Another feature of the mentorship philosophy is a proposal to the dean of the college to offer newly hired faculty a contractual start date of July 1steach year instead of starting their contract one week prior to the start of fall classes, normally in late August. This additional period of time will be used to train new faculty in essential policies and procedures, to complete a variety of administrative tasks on campus, to get a head start on preparing for classes in the fall, and cleaning up those supplemental tasks that accompany every move to a new location. The goal of this early commitment to new faculty is to reduce the stress associated with preparing for a new course, in a new environment, with a new set of operating procedures. This will also enab
It is well known that the quality of service provided by a telecommunication network as measured by the probability of blocking decreases rapidly when the incident traffic exceeds the design limits of the network. A h...
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Digital breast tomosynthesis (DBT) is a three-dimensional imaging technique providing an arbitrary set of reconstruction planes in the breast with limited series of projection images. This paper describes a comparison...
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Digital breast tomosynthesis (DBT) is a three-dimensional imaging technique providing an arbitrary set of reconstruction planes in the breast with limited series of projection images. This paper describes a comparison between traditional shift-and-add (SAA) and point-by-point back projection (BP) algorithms by impulse response and modulation transfer function (MTF) analysis. Due to the partial isocentric motion of the x-ray tube in DBT, structures such as microcalcifications appear slightly blurred in traditional shift-and-add (SAA) images in the direction perpendicular to the direction of tube's motion. Point-by-point BP improved rendition of microcalcifications. The sharpness and morphology of calcifications were improved in a human subject images. A filtered back projection (FBP) deblurring approach was used to demonstrate deblurred point-by-point BP tomosynthesis images. The point-by-point BP rather than traditional SAA should be considered as the foundation of further deblurring algorithms for DBT reconstruction.
The WATERS Network (WATer and Environmental Research Systems Network) will be an integrated real-time distributed observing system which will enable academic and government scientists, engineers, educators, and practi...
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Current mammographic screening for breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Be...
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Current mammographic screening for breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Because the data set was very noisy and contained only weak features, we used a classifier designed for noisy data: decision fusion. Decision fusion outperformed both a support vector machine (SVM) and linear regression with forward stepwise feature selection on all three two-class classification tasks: normal tissue vs. cancer, normal tissue vs. benign lesions, and benign lesions vs. cancer. Decision fusion detected cancer moderately well (AUC=0.84 on normal vs. cancer), demonstrating promise as a screening tool. Decision fusion also detected benign lesions similarly well (AUC=0.83 on normal vs. benign lesions) and was the only classifier to achieve any success in separating benign from malignant lesions (AUC=0.64 on benign vs. cancer). The classification results suggest that the assayed proteins are more indicative of a secondary effect, such as immune response, rather than specific for breast cancer. In conclusion, the decision fusion classifier demonstrated some promise in detecting breast lesions and outperformed other classifiers, especially for the very noisy classification problem of distinguishing benign from malignant lesions.
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