Biomedical Processing Research Articles

GRISSOM Platform: Enabling Distributed Processing and Management of Biological Data Through Fusion of Grid and Web Technologies

Transcriptomic technologies have a critical impact in the revolutionary changes that reshape biological research. Through the recruitment of novel high-throughput instrumentation and advanced computational methodologies, an unprecedented wealth of quantitative data is produced. Microarray experiments are considered high-throughput, both in terms of data volumes (data intensive) and processing complexity (computationally intensive). In this paper, we present grids for in silico systems biology and medicine (GRISSOM), a web-based application that exploits GRID infrastructures for distributed data processing and management, of DNA microarrays (cDNA, Affymetrix, Illumina) through a generic, consistent, computational analysis framework. GRISSOM performs versatile annotation and integrative analysis tasks, through the use of third-party application programming interfaces, delivered as web services. In parallel, by conforming to service-oriented architectures, it can be encapsulated in other biomedical processing workflows, with the help of workflow enacting software, like Taverna Workbench, thus rendering access to its algorithms, transparent and generic. GRISSOM aims to set a generic paradigm of efficient metamining that promotes translational research in biomedicine, through the fusion of grid and semantic web computing technologies.

New Trends in Biomedical Signal Processing: A Crossroads Between Smart Sensors in E-Health and Virtual Physiological Human Initiatives

New Trends in Biomedical Signal Processing: A Crossroads Between Smart Sensors in E-Health and Virtual Physiological Human Initiatives

Integrating text mining into high-throughput assay analysis

There are two basic paradigms of use cases for the output of text mining tools in the genomics subfield of biomedical natural language processing (BioNLP). In the most common one, the tool is designed to produce output that will be viewed by an individual researcher, most commonly a database curator or a bench scientist. This use case has been heavily studied, thanks in part to shared tasks like BioCreative and TREC Genomics. The other use case is the integration of text mining into high-throughput assay analysis. This includes tasks ranging from sequence alignment to the evaluation of gene expression array data. In the past, text mining has been applied to these areas either as a post-processing step, or as an integrated part of the analysis algorithm. More recently, our lab has developed Hanalyzer, a 3R tool—a tool for the knowledge-based analysis of high-throughput assays based on Reading, Reasoning, and Reporting about experimental results in the context of pre-existing knowledge, including knowledge from text mining. The tool is designed to facilitate exploration of experimental data, explanation of observed patterns in the light of what is already known about the entities involved, and the generation of novel hypotheses. Results from a study of craniofacial development demonstrate that the system can be used to explain patterns in gene expression and to generate a set of hypotheses about the roles of four genes previously not known to be involved in tongue development. These hypotheses were experimentally validated by in situ hybridization and may have clinical consequences related to cleft lip and palate.

Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (Prince, V. and Roche, M.; 2009) [Book Review

This book, written by subject matter experts, comprises six sections and provides relevant theoretical frameworks and the latest empirical research findings in the area of biomedical natural language processing (BioNLP) according to a linguistic granularity. It is an excellent book for researchers or graduate students interested in NLP, artificial intelligence, and linguistics, as well as those interested in the most recent advances in information management and retrieval in the healthcare setting.

A Green Method for Processing Polymers using Dense Gas Technology

Dense CO can be used as an environmentally-benign polymer processing medium because of its liquid-like densities and gas-like mass transfer properties.In this work, polymer bio-blends of polycarbonate (PC), a biocompatible polymer, and polycaprolactone (PCL), a biodegradable polymer were prepared. Dense CO was used as a reaction medium for the melt-phase PC polymerization in the presence of dense CO-swollen PCL particles and this method was used to prepare porous PC/PCL blends. To extend the applicability of dense CO to the biomedical industry and polymer blend processing, the impregnation of ibuprofen into the blend was conducted and subsequent dissolution characteristics were observed.

Resource-aware secure ECG healthcare monitoring through body sensor networks

Real-time medical data about patients' physiological status can be collected simply by using wearable medical sensors based on a body sensor network. However, we lack an efficient, reliable, and secure BSN platform that can meet increasing needs in e-healthcare applications. Many such applications require a BSN to support multiple data rates with reliable and energyefficient data transmission. In this article we propose a secure and resource-aware BSN architecture to enable real-time healthcare monitoring, especially for secure wireless electrocardiogram data streaming and monitoring. A cross-layer framework was developed based on unequal resource allocation to support efficient biomedical data monitoring. In this framework important information (e.g., critical ECG data) is identified, and extra resources are allocated to protect it. Furthermore, BSN resource factors are exploited to guarantee a strict requirement of real-time performance. In this work we integrate biomedical information processing and transmission in a unified platform, where secure data transmission in a BSN proceeds with energy efficiency and minimum delay. In particular, we present a wearable ECG device consisting of small and low-power healthnode sensors for wireless three-lead ECG monitoring. Experimental and simulation results demonstrate that the proposed framework can support real-time wireless biomedical monitoring applications.

A chip scale nanofluidic pump using electrically controllable hydrophobicity

The traditional microfluidic systems and devices faced limitations such as power consumption and high driving force in the attempt for implementation as chemical analysis and environmental monitoring systems. The up-to-date development of chemistry and biology has generated great demand for lab-on-a-chip performing specific chemical and biological analysis, clinical diagnostics and biomedical processing. Manipulation of ultrasmall amount and great varieties of biofluids has also been a major issue challenging many researchers. Here we demonstrate in this article, a device utilizing electrically controllable surface tension as the driving force to deliver fluid flow in the order of nanoliters per minute or even smaller, without a dedicated actuator. This device is capable of pumping a continuous liquid column. This actuation mechanism of fluid flow in this device is based on electrowetting-on-dielectric (EWOD) effect and the physics of the fluid dynamics is governed by Navier–Stokes equation. It also has a built-in metering feature to precisely determine the flow rate without an additional flow sensor. The experimental results show that water can be electrically actuated successfully to flow in the microchannel at a flow rate of 18 nl/min under a potential of as low as 20 V. This is very attractive for applications which require an ultra miniaturized metering pump operated at a low power for portable environmental monitoring instruments, chemical analysis systems, implantable medical devices, drug delivery systems and clinical diagnostic systems.

Preparation, characterization and application of zwitterionic polymers and membranes: current developments and perspective

AbstractBecause of their unique pendant‐side chain structures and excellent properties, zwitterionic polymers and membranes, especially zwitterionic hybrids, have attracted increasing interest in recent years. Presently, they can be potentially applied in such fields as nonlinear optical systems, ionic conduction, biomedical processing and chemical separation. In view of their significance to academia and industrial processes, this review gives a brief summary of current developments in the preparation, characterization and potential application of zwitterionic polymers and membranes. Novel approaches to prepare zwitterionic hybrids are highlighted. The present state and future perspective are also discussed. Hopefully, this review can promote the understanding of zwitterionic hybrid polymers and membranes. Copyright © 2009 Society of Chemical Industry

Protein–protein interaction extraction by leveraging multiple kernels and parsers

Protein–protein interaction (PPI) extraction is an important and widely researched task in the biomedical natural language processing (BioNLP) field. Kernel-based machine learning methods have been used widely to extract PPI automatically, and several kernels focusing on different parts of sentence structure have been published for the PPI task. In this paper, we propose a method to combine kernels based on several syntactic parsers, in order to retrieve the widest possible range of important information from a given sentence. We evaluate the method using a support vector machine (SVM), and we achieve better results than other state-of-the-art PPI systems on four out of five corpora. Further, we analyze the compatibility of the five corpora from the viewpoint of PPI extraction, and we see that some of them have small incompatibilities, but they can still be combined with a little effort.

Low Biofouling Chitosan-Hyaluronic Acid Multilayers with Ultra-Low Friction Coefficients

Resistance to biofouling is an advantageous material property in a variety of biomedical and biofluid processing applications. Protein-resisting surface coatings must also be resistant to wear and degradation and in certain applications good aqueous lubricating properties are required. We show that cross-linked polyelectrolyte multilayers, consisting of chitosan and hyaluronan on polydimethylsiloxane (PDMS) surfaces, form a highly lubricating film that is resistant to wear and protein adsorption. The multilayer film shows much stronger resistance to protein adsorption from human whole saliva than both hydrophobic and hydrophilic PDMS surfaces; the latter two showed identical adsorbed salivary film thicknesses. The boundary friction coefficient under aqueous conditions was extremely low (mu approximately 0.01) between multilayer-coated PDMS substrates and the film is robust against dry rubbing and many hours of tribological experiments in a range of aqueous lubricants. The origins of the assembly's low friction coefficients and robustness are discussed. In addition, we found that the addition of negative phosphate ions to water lowers the boundary lubricating properties of negatively charged hydrophilic PDMS surfaces by 1 order of magnitude to mu approximately 0.01. We consider this to arise from the large hydration sheaths and resulting "ball-bearing" properties of the hydrated phosphate ions, which form a lubricating barrier against asperity contact. These findings offer new insights toward biolubrication processes and suggest that chitosan-hyaluronan polyelectrolyte multilayer films have the potential to be used in (bio-) applications requiring low friction as well as resistance to biofouling and wear.

Biomedical Discovery Acceleration, with Applications to Craniofacial Development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

ARTIFICIAL INTELLIGENCE METHODS IN ELECTROCARDIOGRAM AND ELECTROENCEPHALOGRAM DATA CLUSTERING

The paper focuses on the field of artificial intelligence techniques and their use in biomedical data processing. It concerns the clustering techniques inspired by various ant colonies. The behavior of ant colonies shows many interesting properties that have been used in static and dynamic combinatorial problem-solving tasks (mostly since 1990). Also applications to data clustering have been proposed. This branch is a subject of ongoing research. After the introduction into the state-of-the-art of ant-colony-inspired metaheuristics, an overview of ant-colony-inspired clustering metaheuristics is presented, together with the ACO_DTree method, developed by the first author, which is based on the autocatalytic collective behavior of real insect colonies. Over the basic algorithm it involves techniques to increase robustness and performance of the method. Application to electrocardiogram and electroencephalogram data processing is also presented, together with comparison to other clustering methods.

Acoustic study of dysarthria

This paper evaluates a biomedical speech processing tool to perform acoustic studies of eight types of dysarthria based on a multidimensional approach. The performance of three different assessment methods is evaluated with 20 acoustic features derived perceptually and objectively. Results: An assessment method based on self-organised maps neural networks provides the highest assessment index (88.1%) and the most accurate analysis of relevant features. It also enables the creation of bi-dimensional maps that are of great significance for tracking the evolution of the disorders. The tool provides reference judgements of difficult-to-perceive acoustic dimensions that are relevant to clinical practice, rehabilitation and research.

Bio-Swarm-Pipeline (BSP): A light-weight, extensible batch processing system for efficient biomedical data processing

A streamlined scientific workflow system that can track the details of the data processing history is critical for the efficient handling of fundamental routines used in scientific research. In the scientific workflow research community, the information that describes the details of data processing history is referred to as “provenance” which plays an important role in most of the existing workflow management systems. Despite its importance, however, provenance modeling and management is still a relatively new area in the scientific workflow research community. The proper scope, representation, granularity and implementation of a provenance model can vary from domain to domain and pose a number of challenges for an efficient pipeline design. This paper provides a case study on structured provenance modeling and management problems in the neuroimaging domain by introducing the Bio-Swarm-Pipeline. This new model, which is evaluated in the paper through real world scenarios, systematically addresses the provenance scope, representation, granularity, and implementation issues related to the neuroimaging domain. Although this model stems from applications in neuroimaging, the system can potentially be adapted to a wide range of bio-medical application scenarios.

Research of statistical criteria efficiency in tasks of biomedical data processing

Research of statistical criteria efficiency in tasks of biomedical data processing

Themes in biomedical natural language processing: BioNLP08

Address: 1US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA, 2University of Manchester and National Centre for Text Mining, 131 Princess Street, M7 1DN, UK, 3University of Colorado Health Sciences Center, US mail: PO Box 6511, Mail Stop 8303, Colorado, USA, 4Computational Medicine Center, Cincinnati Children's Hospital and Medical Center, 3333 Burnet Avenue, Cincinnati, Ohio 45229-3039, USA, 5University of Tokyo, Japan and University of Manchester, 131 Princess Street, M7 1DN, UK and 6University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, UK

The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes

BackgroundDetecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data. This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus).ResultsThe corpus consists of three parts, namely medical free texts, biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief linguist – also responsible for setting up the annotation guidelines – who resolved cases where the annotators disagreed. The resulting corpus consists of more than 20.000 sentences that were considered for annotation and over 10% of them actually contain one (or more) linguistic annotation suggesting negation or uncertainty.ConclusionStatistics are reported on corpus size, ambiguity levels and the consistency of annotations. The corpus is accessible for academic purposes and is free of charge. Apart from the intended goal of serving as a common resource for the training, testing and comparing of biomedical Natural Language Processing systems, the corpus is also a good resource for the linguistic analysis of scientific and clinical texts.

Auxetic materials for bioprostheses [In the Spotlight

In this paper, the properties of auxetic materials, their biomedical (bioprostheses) and signal processing applications are discussed. Materials with negative values of the Poisson's ratio (NPR) are referred to as auxetic.

Introducing meta-services for biomedical information extraction

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations.

Assessment of the machinability of Ti-6Al-4V alloy using the wear map approach

The high strength to density ratio of titanium alloys coupled with excellent corrosion resistance even at elevated temperatures make them ideal for aerospace applications. Moreover, the biocompatibility of titanium also enables its widespread use in the biomedical and food processing industries. However, the difficulty in machining titanium and its alloys along with the high cost of its extraction from ore form presents a major economic constraint. In the context of machining economics, the wear map approach is very useful in identifying the most suitable machining parameters over a feedrate-cutting velocity plane. To date, wear maps have only been prepared for the machining of ferrous alloys. In this article, a review of the machinability of Ti-6Al-4V alloy is presented with emphasis on comparing the wear performance of various tool materials. In addition, a new wear map for Ti-6Al-4V alloy is presented based on unified turning tests using H13A grade carbide inserts. This wear map can be used as a guide in the selection of cutting variables that ensure the least tool wear rates. This article contrasts the occurrence of a safety zone in the case of machining steels to that of an avoidance zone for Ti-6Al-4V alloy.