scholarly journals Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways

2006 ◽  
Vol 3 (9) ◽  
pp. 515-526 ◽  
Author(s):  
Fei Hua ◽  
Sampsa Hautaniemi ◽  
Rayka Yokoo ◽  
Douglas A Lauffenburger
Keyword(s):  
Decision Tree ◽  
Mathematical Models ◽  
Large Scale ◽  
Therapeutic Interventions ◽  
Data Driven ◽  
Signalling Pathways ◽  
Apoptotic Pathway ◽  
Tree Analysis ◽  
Modelling Framework ◽  
Novel Approach

Mathematical models of highly interconnected and multivariate signalling networks provide useful tools to understand these complex systems. However, effective approaches to extracting multivariate regulation information from these models are still lacking. In this study, we propose a data-driven modelling framework to analyse large-scale multivariate datasets generated from mathematical models. We used an ordinary differential equation based model for the Fas apoptotic pathway as an example. The first step in our approach was to cluster simulation outputs generated from models with varied protein initial concentrations. Subsequently, decision tree analysis was applied, in which we used protein concentrations to predict the simulation outcomes. Our results suggest that no single subset of proteins can determine the pathway behaviour. Instead, different subsets of proteins with different concentrations ranges can be important. We also used the resulting decision tree to identify the minimal number of perturbations needed to change pathway behaviours. In conclusion, our framework provides a novel approach to understand the multivariate dependencies among molecules in complex networks, and can potentially be used to identify combinatorial targets for therapeutic interventions.

Overcoming challenges in spatio-temporal modelling of large-scale (global) data

2021 ◽  
Author(s):  
Aoibheann Brady ◽  
Jonathan Rougier ◽  
Yann Ziegler ◽  
Bramha Dutt Vishwakarma ◽  
Sam Royston ◽  
...  
Keyword(s):  
Large Scale ◽  
Holistic Approach ◽  
Global Scale ◽  
Bayesian Hierarchical ◽  
Modelling Framework ◽  
Novel Approach ◽  
Level Data ◽  
Geophysical Processes ◽  
Spatio Temporal ◽  
Data Separation

<p>Modelling spatio-temporal data on a large scale presents a number of obstacles for statisticians and environmental scientists. Issues such as computational complexity, combining point and areal data, separation of sources into their component processes, and the handling of both large volumes of data in some areas and sparse data in others must be considered. We discuss methods to overcome such challenges within a Bayesian hierarchical modelling framework using INLA.</p><p>In particular, we illustrate the approach using the example of source-separation of geophysical signals both on a continental and global scale. In such a setting, data tends to be available both at a local and areal level. We propose a novel approach for integrating such sources together using the INLA-SPDE method, which is normally reserved for point-level data. Additionally, the geophysical processes involved are both spatial (time-invariant) and spatio-temporal in nature. Separation of such processes into physically sensible components requires careful modelling and consideration of priors (such as physical model outputs where data is sparse), which will be discussed. We also consider methods to overcome the computational costs of modelling on such a large scale, from efficient mesh design, to thinning/aggregating of data, to considering alternative approaches for inference. This holistic approach to modelling of large-scale data ensures that spatial and spatio-temporal processes can be sensibly separated into their component parts, without being prohibitively expensive to model.</p>

Head and neck cancer, quality of life, and determinant factors: a novel approach using decision tree analysis

2018 ◽  
Vol 126 (6) ◽  
pp. 486-493 ◽  
Author(s):  
Niebla Bezerra de Melo ◽  
Ítalo de Macedo Bernardino ◽  
Daniela Pita de Melo ◽  
Daliana Queiroga Castro Gomes ◽  
Patrícia Meira Bento
Keyword(s):  
Quality Of Life ◽  
Head And Neck Cancer ◽  
Decision Tree ◽  
Head And Neck ◽  
Neck Cancer ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Determinant Factors ◽  
Novel Approach

Optimizing Investment Decisions Using DCF, Decision Tree Analysis, and Real Options Analysis

2017 ◽  
pp. 98-114
Author(s):  
Ramya Rajajagadeesan Aroul
Keyword(s):  
Decision Tree ◽  
Real Options ◽  
Large Scale ◽  
Investment Decisions ◽  
Call Option ◽  
Decision Tree Analysis ◽  
Discounted Cash Flow ◽  
Tree Analysis ◽  
Real Options Analysis ◽  
Distinct Approach

Large scale infrastructure expansions in hotels are exposed to uncertainty. Since the costs involved in these expansion projects are high and often irreversible, hotels would benefit from analyses that incorporate uncertainty along with traditional valuation techniques like the discounted cash flow (DCF) method. Decision tree analysis (DTA) and real options analysis (ROA) have been in use for the past couple of decades to handle uncertainties and optimize investment decisions. DTA provides a distinct approach to strategic investments that quantitatively takes into account the uncertainties involved in the investments. Under uncertainty, the decision about whether to expand is analogous to the decision about whether to exercise an American call option. By using ROA to the hotel expansion scenario, managers can incorporate and quantify, flexibility and timing in their analysis. The objective of this chapter is to detail the DCF, DTA and ROA methodologies and their applications specific to hotel expansion investments.

718: Modeling the Cost of Management Options for Stage I Nonseminomatous Germ Cell Tumors: A Decision Tree Analysis

2005 ◽  
Vol 173 (4S) ◽  
pp. 195-196
Author(s):  
Richard E. Link ◽  
Mohamad E. Allaf ◽  
Roberto Pili ◽  
Louis R. Kavoussi
Keyword(s):  
Decision Tree ◽  
Germ Cell ◽  
Germ Cell Tumors ◽  
Stage I ◽  
Decision Tree Analysis ◽  
Management Options ◽  
Tree Analysis ◽  
The Cost ◽  
Nonseminomatous Germ Cell Tumors

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach
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