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).
the proceedings contain 90 papers. the special focus in this conference is on Numerical Analysis and Applications. the topics include: Behavior of weak solutions to the boundary value problems for second order ellipti...
ISBN:
(纸本)9783319570983
the proceedings contain 90 papers. the special focus in this conference is on Numerical Analysis and Applications. the topics include: Behavior of weak solutions to the boundary value problems for second order elliptic quasi-linear equation with constant and variable nonlinearity exponent in a neighborhood of a conical boundary point;CVA computing by PDE models;chaotic dynamics of structural members under regular periodic and white noise excitations;convergence order of a finite volume scheme for the time-fractional diffusion equation;convergence of alternant theta-method with applications;a numerical study on the compressibility of subblocks of schur complement matrices obtained from discretized Helmholtz equations;convergence outside the initial layer for a numerical method for the time-fractional heat equation;multi-preconditioned domain decomposition methods in the krylov subspaces;use of asymptotics for new dynamic adapted mesh construction for periodic solutions with an interior layer of reaction-diffusion-advection equations;a singularly perturbed boundary value problems with fractional powers of elliptic operators;a higher order difference scheme for the time fractional diffusion equation withthe steklov nonlocal boundary value problem of the second kind;local discontinuous galerkin methods for reaction-diffusion systems on unstructured triangular meshes;numerical modeling of fluid flow in liver lobule using double porosity model;algorithms for numerical simulation of non-stationary neutron diffusion problems;regularization methods of the continuation problem for the parabolic equation and computer simulation of plasma dynamics in open plasma trap.
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 most common model of machine learning algorithms involves two life-stages, namely the learning stage and the application stage. the cost of human expertise makes difficult the labeling of large sets of data for th...
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the proceedings contain 25 papers. the topics discussed include: qualitative modeling and simulation of bacterial regulatory networks;integrated analysis from abstract stochastic process algebra models;an exact Browni...
ISBN:
(纸本)3540885617
the proceedings contain 25 papers. the topics discussed include: qualitative modeling and simulation of bacterial regulatory networks;integrated analysis from abstract stochastic process algebra models;an exact Brownian Dynamics method for cell simulation;multiscale modeling of neuronal signaling;systems biology of halophilic archaea;a partial granger causality approach to explore causal networks derived from multi-parameter data;functional evolution of Ribozyme-Catalyzed metabolisms in a graph-based toy-universe;component-based modeling of RNA structure folding;a language for biochemical systems;the attributed Pi calculus;automatic complexity analysis and model reduction of nonlinear biochemical systems;formal analysis of abnormal excitation in Cardiac tissue;the distribution of mutational effects on fitness in a simple circadian clock;and parallel stochastic simulation of diffusive systems.
the proceedings contain 37 papers. the topics discussed include: an effective multi-level algorithm based on simulated annealing for bisecting graph;exact solution of permuted submodular MinSum problems;efficient shap...
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ISBN:
(纸本)9783540741954
the proceedings contain 37 papers. the topics discussed include: an effective multi-level algorithm based on simulated annealing for bisecting graph;exact solution of permuted submodular MinSum problems;efficient shape matching via graph cuts;simulating classics mosaics with graph cuts;an energy minimisation approach to attributed graph regularisation;a pupil localization algorithm based on adaptive gabor filtering and navigating radial symmetry;decomposing document images by heuristic search;skew detection based on elongate feature;active appearance models fitting with occlusion;combining left and right irises for personal authentication;bottom-up recognition and parsing of the human body;an automatic portrait system based on and-or graph representation;and object category recognition using generative template boosting.
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.
the proceedings contain 13 papers. the special focus in this conference is on Computational models of Natural Argument and Empathic Computing. the topics include: A cognitive approach to relevant argument generation;a...
ISBN:
(纸本)9783319462172
the proceedings contain 13 papers. the special focus in this conference is on Computational models of Natural Argument and Empathic Computing. the topics include: A cognitive approach to relevant argument generation;argumentation mining in parliamentary discourse;automatically detecting fallacies in system safety arguments;modeling user music preference through usage scoring and user listening behavior for generating preferred playlists;comparing affect recognition in peaks and onset of laughter;modeling work stress using heart rate and stress coping profiles;wizard-of-oz support using a portable dialogue corpus;identifying significant task-based predictors of emotion in learning;design of populations in symbiotic evolution to generate chord progression in consideration of the entire music structure and item-based learning for music emotion prediction using EEG data.
When QSAR models are fitted, it is important to validate any fitted model-to check that it is plausible that its predictions will carry over to fresh data not used in the model fitting exercise. there are two standard...
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When QSAR models are fitted, it is important to validate any fitted model-to check that it is plausible that its predictions will carry over to fresh data not used in the model fitting exercise. there are two standard ways of doing this-using a separate hold-out test sample and the computationally much more burdensome leave-one-out cross-validation in which the entire pool of available compounds is used both to fit the model and to assess its validity. We show by theoretical argument and empiric study of a large QSAR data set that when the available sample size is small-in the dozens or scores rather than the hundreds, holding a portion of it back for testing is wasteful, and that it is much better to use cross-validation, but ensure that this is done properly.
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