Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies

Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSC's that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL SHI, allowing it to generate much simpler and smaller concepts that are specific-enough to answer a given query. With independence between computed MSC's, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries

Ontology alignment with semantic validation

The present invention relates to computer implemented methods and system for determining correspondences between terms in two or more ontologies. The methods and systems are designed to accept as inputs ontologies in Web Ontology Language (OWL) syntax or any other ontology syntax, to calculate a similarity measure between terms in the ontologies, extract an alignment based on this similarity measure, and verify this alignment according to the semantics contained in the ontologies. This process is designed to be executed iteratively until the similarity measures converge, or until another suitable finalization condition is met. The result of these methods and of the systems implementing these methods is an alignment between two or more ontologies establishing semantic correspondences between the terms in the ontologies

<title>Edge-preserving image compression for magnetic-resonance images using dynamic associative neural networks (DANN)-based neural networks</title>

With the tremendous growth in imaging applications and the development of filmless radiology, the need for compression techniques that can achieve high compression ratios with user specified distortion rates becomes necessary. Boundaries and edges in the tissue structures are vital for detection of lesions and tumors, which in turn requires the preservation of edges in the image. The proposed edge preserving image compressor (EPIC) combines lossless compression of edges with neural network compression techniques based on dynamic associative neural networks (DANN), to provide high compression ratios with user specified distortion rates in an adaptive compression system well-suited to parallel implementations. Improvements to DANN-based training through the use of a variance classifier for controlling a bank of neural networks speed convergence and allow the use of higher compression ratios for `simple' patterns. The adaptation and generalization capabilities inherent in EPIC also facilitate progressive transmission of images through varying the number of quantization levels used to represent compressed patterns. Average compression ratios of 7.51:1 with an averaged average mean squared error of 0.0147 were achieved

VMIPVS: a visual medical image processing and visualization system

Image processing and visualization are very important in the medical field. This is clearly demonstrated by the wide variety of imaging schemes used such as magnetic resonance imaging, ultrasound imaging, x-ray imaging (computed tomography, CT), single photon emission tomography, and positron emission tomography. All of these modalities produce 2-D images. It is evident that image processing and visualization routines could expedite research in the field. In fact, if routines that already exist could be reused, it would further assist the progress. Environments that provide all the functionality required are not available (aside from the fact that all the functionality might not be understood, yet). A visual programming environment (VPE) tailored to the integration of all needed functions, through the combination of the best features of different packages into a single unified interface, was the main goal to be accomplished by the design of VMIPVS. Important issues addressed by VMIPVS are interoperability, data conversion, display formats, and extendibility

Integrated patient oriented workstation for radiological diagnosis

Many computerized database managers for retrieval of radiological data for all of patient's history have been designed, but they lack the ability to display images in a convenient way. This paper describes a radiology diagnoses system that integrates a virtual light board on a high resolution multi monitor workstation platform with diagnosis reports and report making, scheduling, image processing and an adaptive user interface using an object oriented approach

Multimodal deep representation learning for protein interaction identification and protein family classification

Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks

Protein Family Classification from Scratch: A CNN Based Deep Learning Approach

Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of these proteins. However, compared with identified proteins, uncharacterized proteins consist of a notable percentage of the overall proteins in the bioinformatics research field. Traditional family classification methods often devote themselves to extracting N-Gram features from sequences while ignoring motif information as well as affinity information between motifs and adjacent amino acids. Previous clustering-based algorithms have typically been used to define protein features with domain knowledge and annotate protein families based on extensive data samples. In this paper, we apply CNN based amino acid representation learning with limited characterized proteins to explore the performances of annotated protein families by taking into account the amino acid location information. Additionally, we apply the method to all reviewed protein sequences with their families retrieved from the UniProt database to evaluate our approach. Last but not least, we verify our model using those unreviewed protein records, which is typically ignored by other methods

<title>Neural network compression for medical images: the dynamic autoassociative neural net compression system</title>

This paper discusses the use of a novel model of neural networks, the generalized neural network model, to build the primitives for an adaptive compression system. This model adds to the today's connectionist model paradigms to include the behave-act, evolve-learn, and behave-control functions of neural networks, which allow the definition of connectionist systems that overcome the drawbacks of previous feedforward neural network-based compression systems. The approach yields a compression system that surpasses known compression algorithms in three main aspects: very high compression rate with a low introduced distortion, ability to tackle a broad set of data, and feasibility for on-line real-time compression

ATM-based fiber optic network for scalable and modular PACS design

In this work we present our initial effort in developing a scalable and modular network architecture for cost-effective medical communication system providing real-time universal access in multimedia format. Target applications of such an integrated network design include telemedicine, teleradiology, electronic claims processing and supply ordering, teleconsultation, and home patient monitoring