The rapid increase in high-throughput, complex, and heterogeneous data has led to the adoption of network-structured models and analyses for interpretation. However, these data are inherently complex and challenging to understand, prompting researchers to turn to graph embedding methods to facilitate analysis. While general network embedding techniques have shown promise in improving downstream prediction and classification tasks, real-world data are complicated due to cross-domain interactions between different types of entities. Multilayered networks have been successful in integrating biological data to represent biological systems' hierarchy, but embedding nodes based on different types of interactions remains an unsolved problem. To address this challenge, we propose the Motif-aware deep representation learning in multilayer (MARML) networks for learning network representations. Our method considers recurring motif patterns, topological information, and attributive information from other sources as node features. We validated the MARML method using various multilayer network datasets. In addition, by incorporating motif information, MARML considers higher order connections across different hierarchies. The learned features exhibited excellent accuracy in tasks related to link prediction and link differentiation, enabling us to distinguish between existing and disconnected triplets. Through the integration of both intrinsic node attributes and topological network structures, we enhance our understanding of complex biological systems.

Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep graph learning on a collection of patient factors from multiple diagnostic disciplines to better represent breast cancer patient information and predict molecular subtype. Our method models breast cancer patient data into a multi-relational directed graph with extracted feature embeddings to directly represent patient information and diagnostic test results. We develop a radiographic image feature extraction pipeline to produce vector representation of breast cancer tumors in DCE-MRI and an autoencoder-based genomic variant embedding method to map variant assay results to a low-dimensional latent space. We leverage related-domain transfer learning to train and evaluate a Relational Graph Convolutional Network to predict the probabilities of molecular subtypes for individual breast cancer patient graphs. Our work found that utilizing information from multiple multimodal diagnostic disciplines improved the model's prediction results and produced more distinct learned feature representations for breast cancer patients. This research demonstrates the capabilities of graph neural networks and deep learning feature representation to perform multimodal data fusion and representation in the breast cancer domain.

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.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.

The SPARQL LeftJoin abstract operator is not distributive over Union; this limits the algebraic manipulation of graph patterns, which in turn restricts the ability to create query plans for distributed processing or query optimization. In this paper, we present semQA, an algebraic extension for the SPARQL query language for RDF, which overcomes this issue by transforming graph patterns through the use of an idempotent disjunction operator Or as a substitute for Union. This permits the application of a set of equivalences that transform a query into distinct forms. We further present an algorithm to derive the solution set of the original query from the solution set of a query where Union has been substituted by Or. We also analyze the combined complexity of SPARQL, proving it to be NP-complete. It is also shown that the SPARQL query language is not, in the general case, fixed-parameter tractable. Experimental results are presented to validate the query evaluation methodology presented in this paper against the SPARQL standard to corroborate the complexity analysis and to illustrate the gains in processing cost reduction that can be obtained through the application of semQA.

OBJECTIVES: To develop mechanisms to formulate queries over the semantic representation of cancer-related data services available through the cancer Biomedical Informatics Grid (caBIG). DESIGN: The semCDI query formulation uses a view of caBIG semantic concepts, metadata, and data as an ontology, and defines a methodology to specify queries using the SPARQL query language, extended with Horn rules. semCDI enables the joining of data that represent different concepts through associations modeled as object properties, and the merging of data representing the same concept in different sources through Common Data Elements (CDE) modeled as datatype properties, using Horn rules to specify additional semantics indicating conditions for merging data. Validation In order to validate this formulation, a prototype has been constructed, and two queries have been executed against currently available caBIG data services. DISCUSSION: The semCDI query formulation uses the rich semantic metadata available in caBIG to build queries and integrate data from multiple sources. Its promise will be further enhanced as more data services are registered in caBIG, and as more linkages can be achieved between the knowledge contained within caBIG's NCI Thesaurus and the data contained in the Data Services. CONCLUSION: semCDI provides a formulation for the creation of queries on the semantic representation of caBIG. This constitutes the foundation to build a semantic data integration system for more efficient and effective querying and exploratory searching of cancer-related data.

A dual-channel 3D MRI segmentation technique based on grouping artificial immune networks (GAIN) is introduced to detect MS lesion in MR images. The technique demonstrates the ability of artificial immune networks to handle MS lesions detection in T1- and T2-weighted brain MRI. The GAIN-based MRI segmentation technique was evaluated using simulated MS brain images from the McConnell Brain Imaging Centre, Montreal Neurological Institute of McGill University. 3D anisotropic filtering is used to handle noise artifacts in the simulated 3D MRI data sets. Experimental results demonstrated that dual channel MS segmentation approach exhibited high accuracy in segmenting the simulated MS brain data and an even higher accuracy when compared to techniques based on single channel 3D MRI data sets in terms of the Dice coefficient, an objective measure of overlap.

Rapid developments in the Internet and multimedia applications allow us to access large amounts of image and video data. While significant progress has been made in digital data compression, content-based functionalities are still quite limited. Many existing techniques in content-based retrieval are based on global visual features extracted from the entire image. In order to provide more efficient content-based functionalities for video applications, it is necessary to extract meaningful video objects from scenes to enable object-based representation of video content. Object-based representation is also introduced by MPEG-4 to enable content-based functionality and high coding efficiency. In this paper, we propose a new algorithm that automatically extracts meaningful video objects from video sequences. The algorithm begins with the robust motion segmentation on the first two successive frames. To detect moving objects, segmented regions are grouped together according to their spatial similarity. A binary object model for each moving object is automatically derived and tracked in subsequent frames using the generalized Hausdorff distance. The object model is updated for each frame to accommodate for complex motions and shape changes of the object. Experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of our proposed algorithm.

Large image databases have emerged in various applications in recent years. A prime requisite of these databases is the means by which their contents can be indexed and retrieved. A multilevel signature file called the Two Signature Multi-level Signature File ( 2SMLSF ) is introduced as an efficient access structure for large image databases. The 2SMLSF encodes image information into binary signatures and creates a tree structures can be efficiently searched to satisfy a user's query. Two types of signatures are generated. Type I signatures are used at all tree levels except the leaf level and are based only on the domain objects included in the image. Type II signatures, on the other hand, are stored at the leaf level and are based on the included domain objects and their spatial relationships. The 2SMLSF was compared analytically to existing signature file techniques. The 2SMLSF significantly reduces the storage requirements; the index structure can answer more queries; and the 2SMLSF performance significantly improves over current techniques. Both storage reduction and performance improvement increase with the number of objects per image and the number of images in the database. For an example large image database, a storage reduction of 78% may be archieved while the performance improvement may reach 98%.

In his comments, Guy Smith discussed three points he believes are either not correct or unclear. These points are: 1) The uniqueness of thermometer coding in the algorithm is not supported in the paper. 2) The BKNN algorithm is not equivalent to the k-nearest neighbor algorithm. 3) The relationship between examplars and classes is not clear. Following is a reply to each of the above three points:

A framework for retrieving images by spatial similarity (FRISS) in ima ge databases is presented. In this framework, a robust retrieval by spatial similarity (RSS) algorithm is defined as one that incorporates both directional and topological spatial constraints, retrieves similar images, and recognized images even after they undergo translation, scaling, rotation (both perfect and multiple), or any arbitrary combination of transformatioins. The FRISS framework is discussed and used as a base for comparing various existing RSS algorithms. Analysis shows that none of them satisfies all the FRISS specifications. An algorithm, SIM dtc , is then presented. SIM dtc introduces the concept of a rotation correction angle (RCA) to align objects in one image spatially closer to matching objects in another image for more accurate similarity assessment. Similarity between two images is a function of the number of common objects between them and the closeness of directional and topological spatial relationships between object pairs in both images. The SIM dtc retrieval is invariant under translation, scaling, and perfect rotation, and the algorithm is able to rank multiple rotation variants. The algorithm was tested using synthetic images and the TESSA image database. Analysis shows the robustness of the SIM dtc algorithm over current algorithms.

An algorithm is developed that detects well-localized, unfragmented, thin edges in medical images based on optimization of edge configurations using a genetic algorithm (GA). Several enhancements were added to improve the performance of the algorithm over a traditional GA. The edge map is split into connected subregions to reduce the solution space and simplify the problem. The edge-map is then optimized in parallel using incorporated genetic operators that perform transforms on edge structures. Adaptation is used to control operator probabilities based on their participation. The GA was compared to the simulated annealing (SA) approach using ideal and actual medical images from different modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. Quantitative comparisons were provided based on the Pratt figure of merit and on the cost-function minimization. The detected edges were thin, continuous, and well localized. Most of the basic edge features were detected. Results for different medical image modalities are promising and encourage further investigation to improve the accuracy and experiment with different cost functions and genetic operators.

Presents a knowledge-based approach for labeling two-dimensional (2-D) magnetic resonance (MR) brain images using the Boolean neural network (BNN), which has binary inputs and outputs, integer weights, fast learning and classification, and guaranteed convergence. The approach consists of two components: a BNN clustering algorithm and a constraint satisfying Boolean neural network (CSBNN) labeling procedure. The BNN clustering algorithm is developed to initially segment an image into a number of regions. Then the segmented regions are labeled with the CSBNN, which is a modified version of BNN. The CSBNN uses a knowledge base that contains information on image-feature space and tissue models as constraints. The method is tested using sets of MR brain images. The regions of the different brain tissues are satisfactorily segmented and labeled. A comparison with the Hopfield neural network and the traditional simulated annealing method for image labeling is provided. The comparison results show that the CSBNN approach offers a fast, feasible, and reliable alternative to the existing techniques for medical image labeling.

In this paper we present two supervised pattern classifiers designed using Boolean neural networks. They are: 1) nearest-to-an-exemplar classifier; and 2) Boolean k-nearest neighbor classifier. The emphasis during the design of these classifiers was on simplicity, robustness, and the ease of hardware implementation. The classifiers use the idea of radius of attraction to achieve their goal. Mathematical analysis of the algorithms presented in the paper is done to prove their feasibility. Both classifiers are tested with well-known binary and continuous feature valued data sets yielding results comparable with those obtained by similar existing classifiers.

In this paper we describe a set of multimedia tools for use in a filmless radiology department. The purpose of these tools is to create multimedia radiology reports, containing text, images, and voice annotations. The text part of the reports is transcribed from dictations made by the radiologists while they use an advanced multi monitor diagnosis workstation to read the radiological studies. At the base of the system we implemented an object oriented distributed database system. A collection of clients and servers control the acquisition and processing of the multimedia information.

Presents a feature recognition network for pattern recognition that learns the patterns by remembering their different segments. The base algorithm for this network is a Boolean net algorithm that the authors developed during past research. Simulation results show that the network can recognize patterns after significant noise, deformation, translation and even scaling. The network is compared to existing popular networks used for the same purpose, especially the Neocognitron. The network is also analyzed as regards to interconnection complexity and information storage/retrieval.>

A video image tracking system for the micromotion analysis of the total hip replacement (THR) design is presented in this paper. Micromotion with optimal amount of translation and rotation is crucial for the ingrowth of bone tissue on the prosthesis surface. By sampling the input sequential images from the video camera, low-level image processing technique is applied to get the information about the micromotion on the bone/implant interface.>

The system is guaranteed theoretically to compress to any feasible rate, with as low a distortion rate as required. It also exhibits user selectable compression and error rates, ability to compress general data types, and adaptation to the data source. The compression system is based on a novel family of connectionist algorithms and generative algorithms used in conjunction with new artificial neural network models that permit the determination of a quasi-optimal architecture for compressing a given data source.>

It is shown that the Boolean-neural network can be used to solve NP-complete problems. The problem under consideration is the traveling salesman problem. The Boolean neural network has been modified to include the iterative procedure for solving combinatorial optimization problems. An architecture that utilizes this modified Boolean neural network (MBNN) is proposed for solving this problem. The simulation results have been found to be comparable to the simulated annealing algorithm (SAA), which is used as a test base. The MBNN implementation involves low hardware complexity, good noise immunity, and fast circuitry. This is very important in real-time systems and commercial job scheduling applications.>

Optimal performance of a manufacturing layout consisting of several mobile robots performing specific transportation tasks is addressed. A synergistic approach based on decentralized path planning, heuristic-algorithm-based navigation, and localized collision avoidance is presented. A comprehensive, flexible multiple mobile robots system (MMRS) consisting of standard patterns, a local controller, and individual mobile robots is described. The mobile robots are capable of performing their own path planning and collision avoidance by means of ultrasonic sensors, standard patterns, and rotary optical encoders. Specially designed algorithms for the mobile robot and local controller are presented. To ensure greater flexibility in designing the layout, the MMRS was interfaced with a specially designed CAD subsystem. Simulations using varying numbers of robots were performed on a sample layout that was designed using the CAD subsystem. The results of the simulation illustrate the validity of the approach.>

A navigation system for automatic vision-guided vehicles which uses an efficient double heuristic search algorithm for path planning is presented. It is capable of avoiding unknown obstacles and recovering from unidentifiable locations. A linked list representation of the path network database makes the implementation feasible in any high-level language and renders it suitable for real-time application. Extensive simulated experiments have been conducted to verify the validity of the proposed algorithms. The combination of the techniques of robot navigation in unexplored terrain and the global map method proved to be a valid technique for automated guided vehicle (AGV) guidance. A learning mechanism is used in the AGV by updating the path network during navigation. Simulated results supported all the theoretically expected conclusions, since the robot planned its path correctly between the requested nodes and maneuvered its way around the obstacles. Overall, the results were very encouraging.>

A real-time system for position determination of a mobile robot in an industrial environment is presented. A complete error analysis of the system is conducted, and the necessary conditions required for achieving accuracy are evaluated mathematically. A method for segmentation using chromatic information has been developed. It makes extensive use of image moments to calculate geometric parameters and correct errors due to perspective distortion. Architectures for calculation of higher-order moments of up to seventh order in real time are discussed. Methods for performing these complex calculations in real time by using only simple circuit elements such as adders and accumulators are given.>

A fault-tolerant architecture for an automatic navigation system is presented. The system employs a mixed type of architecture in which the speed advantages of both pipelined and parallel architectures are exploited to achieve real-time navigation. The fault-tolerant architecture is presented using two reconfiguration strategies. To evaluate the proposed architecture, its reliability, availability, and safety are investigated using Markov models. The feasibility of implementing the proposed architecture is studied. It is found that the architecture be implemented to perform the required real-time navigation of the automatic vision-guided vehicle.>

A video tracking system based on the principles of optimalizing control is presented. This system is designed such that expensive segmentation procedures are not necessary, and is well suited for fast hardware implementation. At the image received from the camera a perturbation procedure is performed, simulating the variations on the intensity values that would occur if the camera were moved. An index of performance (IP) is evaluated at the results of this perturbation; this IP does not need to detect the position of the tracked object accurately, but rather varies in such a way as to detect the motion of the object with respect to the camera. Algorithms for translation and scaling of the tracking object have been developed using area, centroid, and template matching as indices of performance. Software simulation experiments show that the control law developed yields good results even under variations in background and illumination and under multidimensional motion.>

As mobile robots are taking on more and more of the tasks that were normally delegated to humans, they need to acquire higher degrees of autonomous operation, which calls for accurate and efficient position determination and/or verification. The critical geometric dimensions of a standard pattern are used here to locate the relative position of the mobile robot with respect to the pattern; by doing so, the method does not depend on values of any intrinsic camera parameters, except the focal length. In addition, this method has the advantages of simplicity and flexibility. This standard pattern is also provided with a unique identification code, using bar codes, that enables the system to find the absolute location of the pattern. These bar codes also assist in the scanning algorithms to locate the pattern in the environment. A thorough error analysis and experimental results obtained through software simulation are presented, as well as the current direction of our work.

An image processor used as a position transducer is treated on an input-output basis. If processing speed is sufficiently fast the input-output characteristic is independent of frequency and can be represented as a gain function. The input is a function of the image pattern (including intensity if grey level is used) and the output is used for control or other purposes but control is the issue considered here. A Fourier phase in conjunction with projection concepts is used to obtain control signals in the image processing. A solution to two-dimensional rotation of the object input pattern is presented. The technique can easily be extended to deal with dilation problem. Results that are important to closed loop design are included.