Biology subjects

Belkhatir, Z.

Publications and source records attributed to Belkhatir, Z..

8 recordsLinked to original sources

bioRxiv2024

Cancer radiomic feature variations due to reconstruction kernel choice and integral tube current.

PurposeRadiological cancer imaging features, or radiomics features, can be derived to diagnose disease or predict treatment response. However, variability between vendors, scanners, protocols, and even reconstruction software versions is an obstacle to the clinical use of radiomics features. This study aimed to characterize the impact of kernel reconstruction differences and integral tube current settings on radiomic features extracted from computed tomography (CT) scans. MethodsRadiomic features were extracted from CT scans of a 3D-printed phantom with five imprinted tumors using the CERR software system, resulting in 282 features. Batch effects were assessed via principal component analysis (PCA) and correlation measures. Robustness was measured using the concordance correlation coefficient (CCC) and Pearson correlation coefficient. Statistical analysis was performed using R software. ResultsPCA identified two clusters comprised of Standard, ASIRs, ASIRV, and soft kernels in one, and Lung and Bone Kernels in the other. Features displayed a gradient from ASIR10 to ASIR50 and ASIRV1 to ASIRV5 in terms of nearness to Standard Kernel feature values. Feature correlation matrices revealed little change in ASIRs, ASIRVs, and the Standard Kernel, but showed significant changes in Bone and Lung Kernel results. Combat-algorithm correction improved robustness, particularly in first-order statistic features, and mitigated batch effects due to the ASIRs and the standard kernel. Forty (40) out of 282 features were identified as robust. However, Combat-based correction performed poorly in harmonizing Bone and Lung reconstruction kernels. ConclusionsThe robustness of means and median radiomic features across kernel reconstruction choices, in contrast to the lack of robustness in many other radiomic features, suggests that kernel reconstruction effects are not well-addressed by current harmonization methods.

bioinformatics↗

bioRxiv2021

Parameter Sensitivity and Experimental Validation for Fractional-Order Dynamical Modeling of Neurovascular Coupling

GoalNeurovascular coupling is a fundamental mechanism linking neural activity to cerebral blood flow (CBF) response. Modeling this coupling is very important to understand brain functions, yet challenging due to the complexity of the involved phenomena. One key feature that different studies have reported is the time delay that is inherently present between the neural activity and cerebral blood flow, which has been described by adding a delay parameter in standard models. An alternative approach was recently proposed where the framework of fractional-order modeling is employed to characterize the complex phenomena underlying the neurovascular. Thanks to its nonlocal property, a fractional derivative is suitable for modeling delayed and power-law phenomena. MethodsIn this study, we analyzed and validated an effective fractional-order for the effective modeling and characterization of the neurovascular coupling mechanism. To show the added value of the fractional order parameters of the proposed model, we perform a parameter sensitivity analysis of the fractional model compared to its integer counterpart. Moreover, the model was validated using neural activity-CBF data related to both event and block design experiments that were acquired using electrophysiology and laser Doppler flowmetry recordings, respectively. ResultsThe validation results show the aptitude and flexibility of the fractional-order paradigm in fitting a more comprehensive range of well-shaped CBF response behaviors while maintaining a low model complexity. Comparison with the standard integer-order models shows the added value of the fractional-order parameters in capturing various key determinants of the cerebral hemodynamic response, e.g., post-stimulus undershoot. ConclusionsThis investigation authenticates the ability and adaptability of the fractional-order framework to characterize a wider range of well-shaped cerebral blood flow responses while preserving low model complexity through a series of unconstrained and constrained optimizations. Impact StatementThe present study proposes a novel fractional-order framework for modeling neurovascular coupling. A parameter sensitivity analysis demonstrates the potential flexibility, and effectiveness of the fractional-order paradigm in reconstructing the cerebral hemodynamics with manageable complexity; and a real experimental validation analysis demonstrates the ability of the model in modeling a wider range of well-shaped CBF responses.

bioengineering↗

bioRxiv2021

Long-term p21 and p53 trends regulate the frequency of mitosis events and cell cycle arrest

1Radiation exposure of healthy cells can halt cell cycle temporarily or permanently. In this work, two single cell datasets that monitored the time evolution of p21 and p53, one subjected to gamma irradiation and the other to x-ray irradiation, are analyzed to uncover the dynamics of this process. New insights into the biological mechanisms were found by decomposing the p53 and p21 signals into transient and oscillatory components. Through the use of dynamic time warping on the oscillatory components of the two signals, we found that p21 signaling lags behind its lead signal, p53, by about 3.5 hours with oscillation periods of around 6 hours. Additionally, through various quantification methods, we showed how p21 levels, and to a lesser extent p53 levels, dictate whether the cells are arrested in their cell cycle and how fast these cells divide depending on their long-term trend in these signals.

systems biology↗

bioRxiv2020

Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53

In this paper, we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this response system is malfunctioning, e.g. due to mutations in p53, uncontrolled cell proliferation may lead to the development of cancer. Understanding the behavior of p53 is thus crucial to prevent its failing. It has been shown in various experiments that periodicity of the p53 signal is one of the main descriptors of its dynamics, and that its pulsing behavior (regular vs. spontaneous) indicates the level and type of cellular stress. In the present work, we introduce an algorithm to score the local periodicity of a given time series (such as the p53 signal), which we call Detrended Autocorrelation Periodicity Scoring (DAPS). It applies pitch detection (via autocorrelation) on sliding windows of the entire time series to describe the overall periodicity by a distribution of localized pitch scores. We apply DAPS to the p53 time series obtained from single cell experiments and establish a correlation between the periodicity scoring of a cells p53 signal and the number of cell division events. In particular, we show that high periodicity scoring of p53 is correlated to a low number of cell divisions and vice versa. We show similar results with a more computationally intensive state-of-the-art periodicity scoring algorithm based on topology known as Sw1PerS. This correlation has two major implications: It demonstrates that periodicity scoring of the p53 signal is a good descriptor for cellular stress, and it connects the high variability of p53 periodicity observed in cell populations to the variability in the number of cell division events. AMS subject classification92C42, 92C37, 62M10

bioinformatics↗

bioRxiv2019

Optimal Mass Transport for Robust Texture Analysis

The emerging field of radiomics, which consists of transforming standard-of-care images to quantifiable scalar statistics, endeavors to reveal the information hidden in these macroscopic images. This field of research has found different applications ranging from phenotyping and tumor classification to outcome prediction and treatment planning. Texture analysis, which often consists of reducing spatial texture matrices to summary scalar features, has been shown to be important in many of the latter applications. However, as pointed out in many studies, some of the derived texture statistics are strongly correlated and tend to contribute redundant information; and are also sensitive to the parameters used in their computation, e.g., the number of gray intensity levels. In the present study, we propose first to consider texture matrices, with an emphasis on gray-level co-occurrence matrix (GLCM), as a non-parametric multivariate objects. The proposed modeling approach avoids evaluating redundant and strongly correlated features and also prevents the feature processing steps. Then, via the Wasserstein distance from optimal mass transport theory, we propose to compare these spatial objects to identify computerized tomography slices with dental artifacts in head and neck cancer. We demonstrate the robustness of the proposed classification approach with respect to the GLCM extraction parameters and the size of the training set. Comparisons with the random forest classifier, which is constructed on scalar texture features, demonstrates the efficiency and robustness of the proposed algorithm.

bioengineering↗

bioRxiv2019

Network-based clustering for drug sensitivity prediction in cancer cell lines

The study of large-scale pharmacogenomics provides an unprecedented opportunity to develop computational models that can accurately predict large cohorts of cell lines and drugs. In this work, we present a novel method for predicting drug sensitivity in cancer cell lines which considers both cell line genomic features and drug chemical features. Our network-based approach combines the theory of optimal mass transport (OMT) with machine learning techniques. It starts with unsupervised clustering of both cell line and drug data, followed by the prediction of drug sensitivity in the paired cluster of cell lines and drugs. We show that prior clustering of the heterogenous cell lines and structurally diverse drugs significantly improves the accuracy of the prediction. In addition, it facilities the interpretability of the results and identification of molecular biomarkers which are significant for both clustering of the cell lines and predicting the drug response.

bioinformatics↗

bioRxiv2019

Hierarchical Network Exploration using Gaussian MixtureModels

We present a framework based on optimal mass transport to construct, for a given network, a reduction hierarchy which can be used for interactive data exploration and community detection. Given a network and a set of numerical data samples for each node, we calculate a new computationally-efficient comparison metric between Gaussian Mixture Models, the Gaussian Mixture Transport distance, to determine a series of merge simplifications of the network. If only a network is given, numerical samples are synthesized from the network topology. The method has its basis in the local connection structure of the network, as well as the joint distribution of the data associated with neighboring nodes.\n\nThe analysis is benchmarked on networks with known community structures. We also analyze gene regulatory networks, including the PANTHER curated database and networks inferred from the GTEx lung and breast tissue RNA profiles. Gene Ontology annotations from the EBI GOA database are ranked and superimposed to explain the salient gene modules. We find that several gene modules related to highly specific biological processes are well-coordinated in such tissues. We also find that 18 of the 50 genes of the PAM50 breast-tumor prognostic signature appear among the highly coordinated genes in a single gene module, in both the breast and lung samples. Moreover these 18 are precisely the subset of the PAM50 recently identified as the basal-like markers.

bioinformatics↗

bioRxiv2019

A Novel Integrative Multiomics Method Reveals a Hypoxia-Related Subgroup of Breast Cancer with Significantly Decreased Survival

The remarkable growth of multi-platform genomic profiles has led to the multiomics data integration challenge. The effective integration of such data provides a comprehensive view of the molecular complexity of cancer tumors and can significantly improve clinical out-come predictions. In this study, we present a novel network-based integration method of multiomics data as well as a clustering technique involving the Wasserstein (Earth Movers) distance from the theory of optimal mass transport. We applied our proposed method of integrative Wasserstein-based clustering (iWCluster) to invasive breast carcinoma from The Cancer Genome Atlas (TCGA) project. The subtypes were characterized by the concordant effect of mRNA expression, DNA copy number alteration, and DNA methylation as well as the interaction network connectivity of the gene products. iW-Cluster is substantially more effective in distinguishing clusters with different survival rates as compared to isolated one-dimensional conventional omics analysis. Applying iWCluster to breast cancer TCGA data successfully recovered the known PAM50 molecular subtypes. In addition, iWCluster preserves the gene-specific data, which enables us to interpret the results and perform further analysis of significant genes for a specific cluster. The gene ontology enrichment analysis of significant genes in our substantially low survival sub-group leads to the well-known phenomenon of tumor hypoxia and the transcription factor ETS1 whose expression is induced by hypoxia. Increased expression of ETS1 is associated with an increased risk of recurrence and worse prognosis in breast cancer. Consequently, we believe iWCluster has the potential to discover novel subtypes by accentuating the genes that have concordant multiomics measurements in their interaction network, which are challenging to find without the network inference or with single omics analysis.

genomics↗