Risk assessment in the context of 21st century toxicology relies on the elucidation and understanding of mechanisms of toxicity. For that purpose, datasets generated by high-throughput technologies (e.g., high-through...
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ISBN:
(纸本)9781450338530
Risk assessment in the context of 21st century toxicology relies on the elucidation and understanding of mechanisms of toxicity. For that purpose, datasets generated by high-throughput technologies (e.g., high-throughput/content screening) combined with various omics data types are now generated in vitro to test large and diverse set of chemicals (e.g. ToxCast). the development of relevant computational approaches for the analysis and integration of these big data remains challenging and requires qualitative and quantitative evaluation. the current scope of sbv IMPROVER (Industrial Methodology for Process Verification in Research;http://***) is the verification of methods and concepts in systems biology research via challenges opened to the scientific community. Previous challenges brought new insights on methods and their associated results that address questions about diagnostic signatures, the translatability of biological responses/processes across species, and the relevance of biological causal network models. A new sbv IMPROVER challenge will be introduced aiming at evaluating (i) methodologies for the identification of specific biomarkers of exposure and (ii) the predictability by omics data of toxicity mechanisms when cells/tissues in vitro or whole organisms are exposed to individual chemical molecules or mixtures. Participants will be provided with high quality data sets to develop predictive models/classifiers. For this challenge, the integration of a priori biological knowledge in the development of computational approaches may be required to enable biological interpretability/understanding of the predictions. the results and post-challenge analyses will be shared withthe scientific community, and will open new avenues in the field of systems toxicology. Copyright is held by the author/owner(s).
In this paper, we describe a novel approach for reconstructing arbitrary whole-body human models from an arbitrary sparse subset of anthropometric dimensions. Firstly, a comprehensive set of dimensions is estimated fr...
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the main purpose of this study is to examine how flow field in aortic dissection is affected by its geometry and flow condition. Two models of DeBakey type I aortic dissection, which involves the entire aorta, were an...
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the proceedings contain 21 papers. the special focus in this conference is on Fundamentals of Software Engineering. the topics include: Towards smart systems of systems;automated integration of service-oriented softwa...
ISBN:
(纸本)9783319246437
the proceedings contain 21 papers. the special focus in this conference is on Fundamentals of Software Engineering. the topics include: Towards smart systems of systems;automated integration of service-oriented software systems;software architecture modeling and evaluation based on stochastic activity networks;modeling and efficient verification of broadcasting actors;a theory of integrating tamper evidence with stabilization;a safe stopping protocol to enable reliable reconfiguration for component-based distributed systems;efficient architecture-level configuration of large-scale embedded software systems;benchmarks for parity games;incremental realization of safety requirements;analyzing mutable checkpointing via invariants;high performance computing applications using parallel data processing units;improved iterative methods for verifying markov decision processes;a pre-congruence format for xy-simulation;tooled process for early validation of sysML models using modelica simulation;painless support for static and runtime verification of component-based applications;linear evolution of domain architecture in service-oriented software product lines;an interval-based approach to modelling time in event-B and from event-B models to dafny code contracts.
One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new similarity measure to compute the functional similarity between two genes. It is based ...
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Wavelet denoising effectiveness has been proven in neural signal processing applications characterized by a low SNR. this non-linear approach is implemented through the application of some thresholds on the detail sig...
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ISBN:
(纸本)9783319261294;9783319261287
Wavelet denoising effectiveness has been proven in neural signal processing applications characterized by a low SNR. this non-linear approach is implemented through the application of some thresholds on the detail signals coming from a sub-band decomposition. the computation of the thresholds could exhibit a high latency when involving some estimators such as the Median Absolute Deviation (MAD), which is critical for real-time applications. When a VLSI implementation is pursued for low-power purposes, such as in the neuroprosthetic field, these aspects cannot be overlooked. this paper presents an analysis of the main VLSI hardware implementation figures related to this specific aspect of the signal denoising by wavelet processing. Xilinx System Generator has been exploited as a design and co-simulation tool to ease the hardware development on off-the-shelf FPGA platforms. the MAD estimator has been both combinatorially and sequentially implemented, and compared against the sample standard deviation. the study reveals similar performance on the neural signals but dramatically worse implementation figures for the MAD. the combinatorial version of the MAD actually prevents an efficient implementation on medium-small devices. this result is important to perform a correct implementation choice for implantable real-time systems, where the device size is relevant for an usable realization.
Increased understanding of the transcriptomic patterns underlying head and neck squamous cell carcinoma (HNSCC) can facilitate earlier diagnosis and better treatment outcomes. Integrating knowledge from multiple studi...
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ISBN:
(纸本)9781450338530
Increased understanding of the transcriptomic patterns underlying head and neck squamous cell carcinoma (HNSCC) can facilitate earlier diagnosis and better treatment outcomes. Integrating knowledge from multiple studies is necessary to identify fundamental, consistent gene expression signatures that distinguish HNSCC patient samples from disease-free samples, and particularly for detecting HNSCC at an early pathological stage. this study utilizes feature integration and heterogeneous ensemble modeling techniques to develop robust models for predicting HNSCC disease status in both microarray and RNAseq datasets. Several alternative models demonstrated good performance, with MCC and AUC values exceeding 0.8. these models were also applied to discriminate between early pathological stage HNSCC and normal RNA-seq samples, showing encouraging results. the predictive modeling workflow was integrated into a software tool with a graphical user interface. this tool enables HNSCC researchers to harness frequently observed transcriptomic features and ensembles of previously developed models when investigating new HNSCC gene expression datasets. Copyright is held by the author/owner(s).
We introduce a swarm design methodology. the methodology uses a seven step process involving a high-level phase space to map the desired goal to a set of behaviors, castes, deployment schedules, and provably optimized...
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From the late 90s until today, the advances in high-throughput measurement technologies are remarkable and producing a huge amount of cancer genomic data. Due to the complexity of data, however, we have not still got ...
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ISBN:
(纸本)9781880843994
From the late 90s until today, the advances in high-throughput measurement technologies are remarkable and producing a huge amount of cancer genomic data. Due to the complexity of data, however, we have not still got a fully integrated view of genetic and transcriptional changes that differ among individuals. To visualize the differences in genetic and transcriptional data among patient samples, we focus on grouping of three types of features, i.e., genes, patient samples, and expression modules. We propose an integrative framework based on the biclustering of multiple types of biological data, i.e., copy number, gene expression, and module activity, by extending the Infinite Relational models (IRM), a non-parametric Bayesian model used to perform a biclustering of binary data, for continuous data. We demonstrate an utility of the model using a colorectal cancer (CRC) dataset. Our result discovers a clinical insight that the activity of modules related to an immune system is associated with CRC patients survival, which demonstrates the ability of our novel integrative approach to group not only genes and modules but also patient samples based on their genetic and transcriptional alterations. Copyright ISCA, BICOB 2015.
the proceedings contain 11 papers. the special focus in this conference is on Transactions on Computational Collective Intelligence. the topics include: Developing embodied agents for education applications with accur...
ISBN:
(纸本)9783319275420
the proceedings contain 11 papers. the special focus in this conference is on Transactions on Computational Collective Intelligence. the topics include: Developing embodied agents for education applications with accurate synchronization of gesture and speech;abstraction of heterogeneous supplier models in hierarchical resource allocation;shape recognition through tactile contour tracing;real-time tear film classification through cost-based feature selection;scalarized and Pareto knowledge gradient for multi-objective multi-armed bandits;extensibility based multiagent planner with plan diversity metrics;concurrent and distributed shortest-path searches in multiagent-based transport systems;overcoming limited onboard sensing in swarm robotics through local communication and the benefits of pattern-recognition when solving problems in a complex domain.
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