11524773
Towards precise classification of cancers based on robust gene functional expression profiles
BackgroundDevelopment of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level.ResultsInspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles.ConclusionThis modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.
25796104
Classification of Cancer Types Based on Decision Tree Analysis of Gene Function Expression Profiles
15077294
Fudan University: hierarchical video retrieval with adaptive multi-modal fusion
This paper describes Fudan University's interactive video retrieval system. The system uses an adaptive multi-modal fusion method and enables the user to browse the results hierarchically across different levels of temporal granularity.
18580310
1-Persistent Collision-Free CSMA Protocols for Opportunistic Optical Hyperchannels
Recently, a new WDM optical network infrastructure named SMART [1], which is capable of dynamically setting up, modifying, and tearing down optical connections, was proposed. The performance of SMART is determined by the performance of its hyperchannels, which are essentially optical buses or light-trails. Previously proposed CSMA protocols for unidirectional optical buses can be used as distributed medium access control protocols for hyperchannels. However, these protocols are either unfair, or not work-conserving. In this paper, we propose a class of work-conserving, collision-free, and fair CSMA protocols for opportunistic hyperchannels which are tailored hyperchannels for SMART. We show that the performances of our proposed protocols are much better than that of p i -persistent CSMA and that of priority-based CSMA protocol in terms of throughput and fairness.
725973
Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data
BackgroundThe wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level.ResultsThrough performing rigorous alignments of the Affymetrix probe sequences against a comprehensive pool of currently available transcript sequences, and further linking the probesets to the International Protein Index, we generated transcript-level or protein-level annotation tables for two popular Affymetrix expression arrays, Mouse Genome 430A 2.0 Array and Human Genome U133A Array. Application of our new annotations in re-examining existing expression data sets shows increased expression consistency among synonymous probesets and strengthened expression correlation between interacting proteins.ConclusionBy refining the standard Affymetrix annotation of microarray probesets from the gene level to the transcript level and protein level, one can achieve a more reliable interpretation of their experimental data, which may lead to discovery of more profound regulatory mechanism.
11674689
Integrated Cell-based Biosensor for Extracellular Electrophysiological Monitoring
A rigorous preselection of identified compounds by in vitro cellular screening is necessary prior to using the drug candidates for the further time consuming and parallel monitoring stage. In this paper we introduced an integrated cell based chip which can detect changes of electrophysiological parameters by using microelectronic chips such as light-addressable potentiometric sensors(LAPS), micro-electrodes array sensors(MEAS) and interdigitated array sensor(IDA) etc. to record the extracellular metabolism chemical and biological substances, action potential and living characteristics of cells representing the different response. In this research we presented novel ideas including an integrating multifunctional cell sensors chip, obtaining the living cells as the source of cell sensors with cardiac myocytes. Further developments will be done by culturing living olfactory and taste cells on surface of the devices. Now, we have completed the fabrication of the sensor and obtained some promising experiments results in different sensor units. This research will be interesting and useful for parallel extracellular monitoring.
16267058
Petroleum-contaminated groundwater remediation systems design: A data envelopment analysis based approach
Groundwater contamination is one of important environmental problems at petroleum-related sites, which is causing more and more attention. It can bring serious adverse effects on the environment and human health. Design of a groundwater remediation system is desired for supporting the management of petroleum-contaminated sites. Due to high remediation cost and extensive alternatives, efficient remediation design needs to be determined. Data envelopment analysis (DEA) is an approach to measure the relative efficiencies of decision-making units (DMUs) with multiple inputs and multiple outputs without underlying assumptions. However, few of these studies were applied to the design of groundwater remediation systems, where efficient alternatives for remediation systems design need to be determined. This paper proposes a hybrid decision support approach based on data envelopment analysis to determine the most preferable design alternatives for groundwater remediation systems. The total remediation system cost, amount of additional wells, and manpower requirements will be employed as the input variables, while the removal efficiency and technical feasibility as the output variables. The proposed approach will be applied to a case study of designing a groundwater remediation system at a petroleum-contaminated site in western Canada. The most preferable design alternative can be obtained to provide effective decision support for cleaning up the contaminated site.