Confidence intervals by constrained optimization—An algorithm and software package for practical identifiability analysis in systems biology

Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question for models’ predictability: how accurately can the models’ parameters be recovered from available experimental data. The methods based on profile likelihood are among the most reliable methods of practical identification. However, these methods are often computationally demanding or lead to inaccurate estimations of parameters’ confidence intervals. Development of methods, which can accurately produce parameters’ confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling. We propose an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, designed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article. The CICO algorithm is implemented in a software package freely available in Julia (https://github.com/insysbio/LikelihoodProfiler.jl) and Python (https://github.com/insysbio/LikelihoodProfiler.py).

Download Full-text

Conditional Robust Calibration (CRC): a new computational Bayesian methodology for model parameters estimation and identifiability analysis

10.1101/197400 ◽

2017 ◽

Author(s):

Fortunato Bianconi ◽

Chiara Antonini ◽

Lorenzo Tomassoni ◽

Paolo Valigi

Keyword(s):

Systems Biology ◽

Parameter Space ◽

Model Calibration ◽

Time Course ◽

Initial Conditions ◽

Profile Likelihood ◽

Computational Cost ◽

Parameters Estimation ◽

Model Parameters ◽

Identifiability Analysis

AbstractComputational modeling is a remarkable and common tool to quantitatively describe a biological process. However, most model parameters, such as kinetics parameters, initial conditions and scale factors, are usually unknown because they cannot be directly measured.Therefore, key issues in Systems Biology are model calibration and identifiability analysis, i.e. estimate parameters from experimental data and assess how well those parameters are determined by the dimension and quality of the data.Currently in the Systems Biology and Computational Biology communities, the existing methodologies for parameter estimation are divided in two classes: frequentist methods and Bayesian methods. The first ones are based on the optimization of a cost function while the second ones estimate the posterior distribution of model parameters through different sampling techniques.In this work, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for model calibration and identifiability analysis. The algorithm is an iterative procedure based on parameter space sampling and on the definition of multiple objective functions related to each output variables. The method estimates step by step the probability density function (pdf) of parameters conditioned to the experimental measures and it returns as output a subset in the parameter space that best reproduce the dataset.We apply CRC to six Ordinary Differential Equations (ODE) models with different characteristics and complexity to test its performances compared with profile likelihood (PL) and Approximate Bayesian Computation Sequential Montecarlo (ABC-SMC) approaches. The datasets selected for calibration are time course measurements of different nature: noisy or noiseless, real or in silico.Compared with PL, our approach finds a more robust solution because parameter identifiability is inferred by conditional pdfs of estimated parameters. Compared with ABC-SMC, we have found a more precise solution with a reduced computational cost.

Download Full-text

IMPROVED FRACTAL IMAGE COMPRESSION BASED ON ROBUST FEATURE DESCRIPTORS

International Journal of Image and Graphics ◽

10.1142/s0219467811004251 ◽

2011 ◽

Vol 11 (04) ◽

pp. 571-587 ◽

Cited By ~ 10

Author(s):

WILLIAM ROBSON SCHWARTZ ◽

HELIO PEDRINI

Keyword(s):

Image Compression ◽

Computational Cost ◽

Computational Time ◽

Natural Scenes ◽

Fractal Image Compression ◽

Fractal Image ◽

Feature Descriptors ◽

High Compression ◽

Speed Up ◽

Computationally Intensive

Fractal image compression is one of the most promising techniques for image compression due to advantages such as resolution independence and fast decompression. It exploits the fact that natural scenes present self-similarity to remove redundancy and obtain high compression rates with smaller quality degradation compared to traditional compression methods. The main drawback of fractal compression is its computationally intensive encoding process, due to the need for searching regions with high similarity in the image. Several approaches have been developed to reduce the computational cost to locate similar regions. In this work, we propose a method based on robust feature descriptors to speed up the encoding time. The use of robust features provides more discriminative and representative information for regions of the image. When the regions are better represented, the search for similar parts of the image can be reduced to focus only on the most likely matching candidates, which leads to reduction on the computational time. Our experimental results show that the use of robust feature descriptors reduces the encoding time while keeping high compression rates and reconstruction quality.

Download Full-text

A Modified SSOR Preconditioning Strategy for Helmholtz Equations

Journal of Applied Mathematics ◽

10.1155/2012/365124 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Shi-Liang Wu ◽

Cui-Xia Li

Keyword(s):

Convergence Rate ◽

Linear Systems ◽

Iterative Methods ◽

Low Cost ◽

Computational Cost ◽

Coefficient Matrix ◽

Computational Time ◽

Helmholtz Equations ◽

Speed Up ◽

Methods Numerical

The finite difference method discretization of Helmholtz equations usually leads to the large spare linear systems. Since the coefficient matrix is frequently indefinite, it is difficult to solve iteratively. In this paper, a modified symmetric successive overrelaxation (MSSOR) preconditioning strategy is constructed based on the coefficient matrix and employed to speed up the convergence rate of iterative methods. The idea is to increase the values of diagonal elements of the coefficient matrix to obtain better preconditioners for the original linear systems. Compared with SSOR preconditioner, MSSOR preconditioner has no additional computational cost to improve the convergence rate of iterative methods. Numerical results demonstrate that this method can reduce both the number of iterations and the computational time significantly with low cost for construction and implementation of preconditioners.

Download Full-text

Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood

Bioinformatics ◽

10.1093/bioinformatics/btp358 ◽

2009 ◽

Vol 25 (15) ◽

pp. 1923-1929 ◽

Cited By ~ 621

Author(s):

A. Raue ◽

C. Kreutz ◽

T. Maiwald ◽

J. Bachmann ◽

M. Schilling ◽

...

Keyword(s):

Profile Likelihood ◽

Dynamical Models ◽

Identifiability Analysis ◽

Practical Identifiability ◽

Partially Observed

Download Full-text

A new Bayesian methodology for nonlinear model calibration in Computational Systems Biology

10.1101/633180 ◽

2019 ◽

Cited By ~ 1

Author(s):

Fortunato Bianconi ◽

Lorenzo Tomassoni ◽

Chiara Antonini ◽

Paolo Valigi

Keyword(s):

Systems Biology ◽

Nonlinear Model ◽

Model Calibration ◽

Sequential Monte Carlo ◽

Profile Likelihood ◽

Computational Cost ◽

Robustness Analysis ◽

Model Parameters ◽

Computational Systems Biology ◽

Proposal Distribution

AbstractComputational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e. estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function (pdf) of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC.

Download Full-text

Improving the Computational Cost for Copied Region Detection in Forensic Images

Journal of Science and Technology Issue on Information and Communications Technology ◽

10.31130/jst.2016.28 ◽

2016 ◽

Vol 2 (1) ◽

pp. 55

Author(s):

Tu Huynh-Kha ◽

Thuong Le-Tien ◽

Synh Ha ◽

Khoa Huynh-Van

Keyword(s):

Wavelet Transform ◽

Euclidean Distance ◽

Research Work ◽

Computational Cost ◽

Correlation Coefficients ◽

Zernike Moments ◽

Computational Time ◽

Discrete Wavelet ◽

Feature Vectors ◽

Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

Download Full-text

A divide-and-conquer algorithm for quantum state preparation

Scientific Reports ◽

10.1038/s41598-021-85474-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Israel F. Araujo ◽

Daniel K. Park ◽

Francesco Petruccione ◽

Adenilton J. da Silva

Keyword(s):

Computational Cost ◽

Quantum Circuit ◽

Divide And Conquer ◽

Computational Time ◽

Quantum Computers ◽

Dimensional Vector ◽

Quantum Devices ◽

Divide And Conquer Algorithm ◽

Quantum State Preparation ◽

Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

Download Full-text

Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽

10.3390/sym13040645 ◽

2021 ◽

Vol 13 (4) ◽

pp. 645

Author(s):

Muhammad Farooq ◽

Sehrish Sarfraz ◽

Christophe Chesneau ◽

Mahmood Ul Hassan ◽

Muhammad Ali Raza ◽

...

Keyword(s):

Computational Cost ◽

Real Life ◽

Distance Measures ◽

Computational Time ◽

High Dimensional ◽

Test Error ◽

Nearest Neighbours ◽

Comparable Performance ◽

Asymmetric Least Squares ◽

Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

Download Full-text

Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

Applied Sciences ◽

10.3390/app11020813 ◽

2021 ◽

Vol 11 (2) ◽

pp. 813

Author(s):

Shuai Teng ◽

Zongchao Liu ◽

Gongfa Chen ◽

Li Cheng

Keyword(s):

Feature Extraction ◽

Crack Detection ◽

Computational Cost ◽

Concrete Structures ◽

Detection Algorithm ◽

Computational Time ◽

Image Size ◽

Important Indicator ◽

Feature Extractor ◽

Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

Download Full-text

Dynamic maintenance model for high average-utility pattern mining with deletion operation

Applied Intelligence ◽

10.1007/s10489-021-02539-4 ◽

2021 ◽

Author(s):

Jimmy Ming-Tai Wu ◽

Qian Teng ◽

Shahab Tayeb ◽

Jerry Chun-Wei Lin

Keyword(s):

Pattern Mining ◽

Computational Cost ◽

Practical Applications ◽

Itemset Mining ◽

Dynamic Databases ◽

Speed Up ◽

Dynamic Maintenance ◽

Average Utility ◽

High Utility ◽

Maintenance Model

AbstractThe high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.

Download Full-text