MOTIVATION: The identification of new therapeutic uses of existing drugs, or drug repositioning, offers the possibility of faster drug development, reduced risk, lesser cost and shorter paths to approval. The advent of high throughput microarray technology has enabled comprehensive monitoring of transcriptional response associated with various disease states and drug treatments. This data can be used to characterize disease and drug effects and thereby give a measure of the association between a given drug and a disease. Several computational methods have been proposed in the literature that make use of publicly available transcriptional data to reposition drugs against diseases. METHOD: In this work, we carry out a data mining process using publicly available gene expression data sets associated with a few diseases and drugs, to identify the existing drugs that can be used to treat genes causing lung cancer and breast cancer. RESULTS: Three strong candidates for repurposing have been identified- Letrozole and GDC-0941 against lung cancer, and Ribavirin against breast cancer. Letrozole and GDC-0941 are drugs currently used in breast cancer treatment and Ribavirin is used in the treatment of Hepatitis C.

Literature pertaining to geriatric care contains rich information regarding the best practices related to geriatric health care issues. The publication domain of geriatric care is small as compared to other health related areas, however, there are over a million articles pertaining to different cases and case interventions capturing best practice outcomes. The knowledge extracted from these articles could be harvested and translated from research to practice in a quicker and more efficient manner. Geriatric literature contains multiple domains that contain information such as interventions, information on care for elderly, case studies and real life scenarios. These articles contain a variety of causal relationships such as the relationship between interventions and disorders. The goal of this study is to identify these causal relations from published abstracts. Natural language processing and statistical methods were adopted to identify and extract these causal relations with a precision of 79.54% and recall of 81%.

Event extraction is a key step in many text-mining applications such as question-answering, information extraction and summarization systems. In this study we used conditional random field (CRF) to extract causal events from PubMed articles related to Geriatric care. Abstracts of geriatric care domain were manually reviewed and categorized into 42 different sub domains. There are a total of 19, 677 sentences in the collected abstracts from PubMed, out of which 2, 856 sentences were selected and manually annotated with cause and effect events. The data set was then divided into training (2, 520), validation (252) and test (84) sentence sets. Features such as tokens, token categories, affixes, part of speech and shallow parser were used as inputs to the CRF model. A window of features before and after each token was used to determine its causal event label using CRF. A window of four features had the best performance with 84.6% precision, 87% recall, 85% and F-measure.

The circadian rhythm, or biological “clock,” allows the organism to anticipate and prepare for the changes in the physical environment. Studies have found that the internal clock consists of an array of genes and the protein products they encode, which regulate various physiological processes throughout the body. Cyanothece sp . ATCC 51142 is an organism that has both photosynthetic (producing oxygen) and nitrogen fixing ability. It has developed a temporal regulation in which N2 fixation and photosynthesis occur at different times throughout a diurnal cycle with very high levels of CO2 fixation during the light and high levels of N2 fixation in the dark. The mechanisms underlying the circadian rhythm and the signature genes elucidating this mechanism are addressed in this research. The objective is to integrate gene expression data with data and knowledge from prior studies using bibliomics techniques, in the de novo construction of quasi-complete regulatory networks to identify gene signatures in functional motifs and elucidate their role in circadian rhythms in Cyanothece sp . ATCC 51142. Tables 1 and 2 show the signature genes identified from topological analysis that lead to a specific pathway in Cyanothece sp . ATCC 51142. Figure 1 shows the pathways and their peak expression during the time of the day or night depending on the signature genes. Expression of pathways during the time period

BACKGROUND: Studies on amphibian limb regeneration began in the early 1700's but we still do not completely understand the cellular and molecular events of this unique process. Understanding a complex biological process such as limb regeneration is more complicated than the knowledge of the individual genes or proteins involved. Here we followed a systems biology approach in an effort to construct the networks and pathways of protein interactions involved in formation of the accumulation blastema in regenerating axolotl limbs. RESULTS: We used the human orthologs of proteins previously identified by our research team as bait to identify the transcription factor (TF) pathways and networks that regulate blastema formation in amputated axolotl limbs. The five most connected factors, c-Myc, SP1, HNF4A, ESR1 and p53 regulate ~50% of the proteins in our data. Among these, c-Myc and SP1 regulate 36.2% of the proteins. c-Myc was the most highly connected TF (71 targets). Network analysis showed that TGF-β1 and fibronectin (FN) lead to the activation of these TFs. We found that other TFs known to be involved in epigenetic reprogramming, such as Klf4, Oct4, and Lin28 are also connected to c-Myc and SP1. CONCLUSIONS: Our study provides a systems biology approach to how different molecular entities inter-connect with each other during the formation of an accumulation blastema in regenerating axolotl limbs. This approach provides an in silico methodology to identify proteins that are not detected by experimental methods such as proteomics but are potentially important to blastema formation. We found that the TFs, c-Myc and SP1 and their target genes could potentially play a central role in limb regeneration. Systems biology has the potential to map out numerous other pathways that are crucial to blastema formation in regeneration-competent limbs, to compare these to the pathways that characterize regeneration-deficient limbs and finally, to identify stem cell markers in regeneration.

In this paper we present a global analysis of colon rectal cancer genes and their associated miRNAs. Significant genes in colon cancer were obtained by mining the literature and cancer related miRNAs were obtained from miRbase. Five different features were used to analyze to obtain a global gene-miRNA profile. By combining the topological features along with miRNA-gene associations and gene propensity measures, we identified a set of genes and modules that are significant in CRC. The proposed methodology identified 123 significant modules of miRNA-genes that can be further studied for understanding the disease and marker discovery.

As basic science (ldquobenchrdquo) and medical practice (ldquobedsiderdquo) continue their exponential growth in complexity and scope, the need for finding hidden connections and translating knowledge across disciplines becomes inevitable. The proposed method combines semantic Web technology, graph algorithms, and user profiling to discover and prioritize novel cross-disciplinary associations based on each user's interest. A proof-of-concept system was developed and tested through a set of use cases. In each use case, novel associations are suggested and ranked by the system for individual user. We demonstrated the potential of the proposed method in facilitating hypothesis generation and knowledge translation across disciplines.

There has been a considerable amount of recent research in extraction of various kinds of binary associations (e.g., gene-gene, gene-protein, protein-protein, etc) using different text mining approaches. However, an important aspect of such associations is identifying the context in which such associations occur (e.g., "gene A activates protein B in the context of disease C in organ D under the influence of chemical E"). Such contexts can be represented appropriately by a multi-way relationship involving more than two objects rather than usual binary relationships. Such multi-way relations naturally lead to a hyper-graph representation of the knowledge. The hyper-graph based knowledge extraction from biological literature represents a computationally difficult problem due to its combinatorial nature. In this paper, we compare two different approaches to such hyper-graph extraction: one based on an exhaustive enumeration of all hyper-edges and the other based on an extension of the well-known A Priori algorithm.

This paper describes an automatic 3D surface scanning technique using a 3D scanner. It allows the acquisition of a complete surface model of a 3D artifact without any manual registration and human interference. A two-pass approach is applied using a rotary table. In the first pass, a sequence of 2D images of the artifact are collected using a small rotation step. An image similarity measure is then taken to compare adjacent images to assess the differences between consecutive images to establish the optimal scanning angles. In the second pass, 3D scans are taken using the scanning angles derived in the first pass. An iterative closest point (ICP) algorithm is employed to provide precise data alignment of neighboring scans. A new space encoding algorithm is developed to filter the over sampling areas for smoother polygonization. This technique is being implemented in a digital library project for the reconstructions of 3D computer models for artifacts from a collection in several museums.

Studies of gene expression in primary human disease tissue often span several years in order to achieve reasonably large sample sizes and to collect patient clinical information making this data particularly valuable. Due to the lack of a central repository, this data has only been available through disparate and non-publicly accessible sources following publication. We developed disease-to-gene expression mapper (D-GEM) as a publically accessible database and data mining toolbox for microarray data of human primary disease tissue. A statistical pipeline has also been implemented to identify genes over-expressed in disease tissue samples in comparison with normal control samples, or genes whose expression values are associated with clinical parameters such as patient survival rate. One potential application of this data is the identification of pathway specific cancer prognosis markers. By applying a novel, gene signatures for cancer prognosis in the context of known biological pathways in cancer development were identified and confirmed.

Abstract Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large‐scale information infrastructure, e.g., digital libraries. To provide large‐scale information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single‐agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium‐scale applications. In this article, we discuss two distributed (multi‐agent) information filtering approaches, that are distributed with respect to knowledge or functionality, to overcome the limitations of single‐agent centralized information filtering. Large‐scale experimental studies involving the well‐known TREC data set are also presented to illustrate the advantages of distributed filtering as well as to compare the different distributed approaches.

Abstract There has been considerable interest in recent years in providing automated information services, such as information classification, by means of a society of collaborative agents. These agents augment each other's knowledge structures (e.g., the vocabularies) and assist each other in providing efficient information services to a human user. However, when the number of agents present in the society increases, exhaustive communication and collaboration among agents result in a large communication overhead and increased delays in response time. This paper introduces a method to achieve selective interaction with a relatively small number of potentially useful agents, based on simple agent modeling and acquaintance lists. The key idea presented here is that the acquaintance list of an agent, representing a small number of other agents to be collaborated with, is dynamically adjusted. The best acquaintances are automatically discovered using a learning algorithm, based on the past history of collaboration. Experimental results are presented to demonstrate that such dynamically learned acquaintance lists can lead to high quality of classification, while significantly reducing the delay in response time.

MOTIVATION: As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to sift through these vast volumes of heterogeneous data and analysis tools. In this paper we present a general model for an information management system that is adaptable and scalable, followed by a detailed design and implementation of one component of the model. The prototype, called BioSifter, was applied to problems in the bioinformatics area. RESULTS: BioSifter was tested using 500 documents obtained from PubMed database on two biological problems related to genetic polymorphism and extracorporal shockwave lithotripsy. The results indicate that BioSifter is a powerful tool for biological researchers to automatically retrieve relevant text documents from biological literature based on their interest profile. The results also indicate that the first stage of information management process, i.e. data to information transformation, significantly reduces the size of the information space. The filtered data obtained through BioSifter is relevant as well as much smaller in dimension compared to all the retrieved data. This would in turn significantly reduce the complexity associated with the next level transformation, i.e. information to knowledge.

Information services such as searching, retrieval, and filtering are playing a dominant role in our life during the current information age. One critical functionality of these information services is to obtain effective classification for input documents. Thesaurus(vocabulary)-based document representation followed by clustering constitutes a popular approach to document classification. However, two alternatives exist to construct the information classificationsystem. The first one uses a single, monolithic, huge thesaurus and classifies all documents by one centralized machine. The second one exploits distributed computing environmentsby allowing multiple agents with small thesauri to collaborate with each other over a computer network. The objective of this paper is to compare these two approaches (i.e., single-agent and multi-agent) in terms of various criteria including response time, quality of classification, and economic/privacy considerations. Two experimental studies, involving classification of Computer Science and Medline documents, are presented to compare the performanceof a single-agent system with that of a multi-agent system in real world settings. These results indicate that a collaborative multi-agent system constitutes a attractive methodologyfor classifying a large volume of information efficiently, when the thesaurus is large.

Developing a learning suppliment to support the breadth-first computer science curriculum and taking advantage of emerging technologies to provide interactive, demand-driven simulations which enhance comprehension of abstract concepts in computer science resulted in the PACER (Personally Active Computing Exploration Resource) project. Development of this educational tool includes three fundamental objectives: simulation and experimentation, connection to supplementary information, and delivery to a wide audience across multiple computer platforms. All three objectives are required to support the educational demands of students who struggle with difficult theoretical material, seek access to information round-the-clock from home and work and use a variety of computer platforms. The PACER system meets learners' needs of repetition and visual simulation of complex ideas. It also offers the ability to pursue education from remote locations making accessible resources never before available. The interactive design of PACER encourages self-directed learning. It also challenges and supports commuting adults, students with disabilities, and traditional students to pursue fields of quantitative reasoning. Unlike traditional WWW-based learning environments, the PACER pages support interactive learning sessions as well as simulations with animation capabilities. The hierarchical organization of Web pages allows the PACER system to have learning modules that are inter-linked which in turn supports "demand-driven" learning. This feature allows PACER to be an active learning tool.

The goal of this research is to clarify the role of document classification in information filtering. An important function of classification, in managing computational complexity, is described and illustrated in the context of an existing filtering system. A parameter called classification homogeneity is presented for analyzing unsupervised automated classification by employing human classification as a control. Two significant components of the automated classification approach, vocabulary discovery and classification scheme generation, are described in detail. Results of classification performance revealed considerable variability in the homogeneity of automatically produced classes. Based on the classification performance, different types of interest profiles were created. Subsequently, these profiles were used to perform filtering sessions. The filtering results showed that with increasing homogeneity, filtering performance improves, and, conversely, with decreasing homogeneity, filtering performance degrades.

In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream must be handled efficiently. In this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties. A filtering system, SIFTER, has been implemented based on the model, using established techniques in information retrieval and artificial intelligence. These techniques include document representation by a vector-space model, document classification by unsupervised learning, and user modeling by reinforcement learning. The system can filter information based on content and a user's specific interests. The user's interests are automatically learned with only limited user intervention in the form of optional relevance feedback for documents. We also describe experimental studies conducted with SIFTER to filter computer and information science documents collected from the Internet and commercial database services. The experimental results demonstrate that the system performs very well in filtering documents in a realistic problem setting.

For a mobile robot to be practical, it needs to navigate in dynamically changing environments and manipulate objects in the environment with operating ease. The main challenges to satisfying these requirements in mobile robot research include the collection of robot environment information, storage and organization of this information, and fast task planning based on available information. Conventional approaches to these problems are far from satisfactory due to their requirement of high computation time. In this paper, we specifically address the problems of storage and organization of the environment information and fast task planning in the area of robotic research. We propose an special object-oriented data model (OODM) for information storage and management in order to solve the first problem. This model explicitly represents domain knowledge and abstracts a global perspective about the robot's dynamically changing environment. To solve the second problem, we introduce a fast task planning algorithm that fully uses domain knowledge related to robot applications and to the given environment. Our OODM based task planning method presents a general frame work and representation, into which domain specific information, domain decomposition methods and specific path planners can be tailored for different task planning problems. This method unifies and integrates the salient features from various areas such as database, artificial intelligence, and robot path planning, thus increasing the planning speed significantly.

Article Free Access Share on Detection of shifts in user interests for personalized information filtering Authors: W. Lam Department of Management Sciences, S306 Pappajohn Building, The University of Iowa, Iowa City, Iowa Department of Management Sciences, S306 Pappajohn Building, The University of Iowa, Iowa City, IowaView Profile , S. Mukhopadhyay Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, IN Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, INView Profile , J. Mostafa School of Library and Information Science, Indiana University, Bloomington, IN School of Library and Information Science, Indiana University, Bloomington, INView Profile , M. Palakal Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, IN Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, INView Profile Authors Info & Claims SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrievalAugust 1996 Pages 317–325https://doi.org/10.1145/243199.243279Published:18 August 1996Publication History 36citation915DownloadsMetricsTotal Citations36Total Downloads915Last 12 Months40Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The architecture of an expert system model using artificial neural networks is proposed. The proposed model effectively supports the necessary components of an expert system such as user interface facility knowledge base, inference engine, and explanation system. The expert system model (ESM) consists of several orders of simple neural networks, each realizing a simple task. These simple neural networks are organized vertically, thereby achieving a second level of parallelism. A novel way to handle both forward and backward chaining reasoning mechanisms is presented. A secondary network model monitors the reasoning patterns of the primary model.>

In most of the existing Automatic Speech Recognition Systems which make use of Markov Models, the output of the Markov Chain are strings whose symbols belong to a finite alphabet and are generated sequentially over the time domain. We propose a Markov Model System in which symbols are substituted by Spectral Lines which are sequentially generated over the frequency domain. Each spectral line is represented by Continuous Distribution of Parameters. Switching from time-domain to frequency domain drastically reduces the number of states on the Markov chain and the use of continuous parameters eliminates quantization error completely. An application will be presented with experimental results in a multi-speaker environment.