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Computational and Mathematical Methods in Medicine

J C Rangel-Reyes, J C Chimal-Eguía, E Castillo-Montiel
Therapeutic protocols in immunotherapy are usually proposed following the intuition and experience of the therapist. In order to deduce such protocols mathematical modeling, optimal control and simulations are used instead of the therapist's experience. Clinical efficacy of dendritic cell (DC) vaccines to cancer treatment is still unclear, since dendritic cells face several obstacles in the host environment, such as immunosuppression and poor transference to the lymph nodes reducing the vaccine effect. In view of that, we have created a mathematical murine model to measure the effects of dendritic cell injections admitting such obstacles...
2017: Computational and Mathematical Methods in Medicine
Lee Worden, Travis C Porco
We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis...
2017: Computational and Mathematical Methods in Medicine
Siming Tang, Wanbiao Ma, Peifan Bai
The Middle East respiratory syndrome (MERS) coronavirus, a newly identified pathogen, causes severe pneumonia in humans. MERS is caused by a coronavirus known as MERS-CoV, which attacks the respiratory system. The recently defined receptor for MERS-CoV, dipeptidyl peptidase 4 (DPP4), is generally expressed in endothelial and epithelial cells and has been shown to be present on cultured human nonciliated bronchiolar epithelium cells. In this paper, a class of novel four-dimensional dynamic model describing the infection of MERS-CoV is given, and then global stability of the equilibria of the model is discussed...
2017: Computational and Mathematical Methods in Medicine
P Padmanabhan, P Seshaiyer
We present new mathematical models that include the impact of using selected preventative measures such as insecticide treated nets (ITN) in controlling or ameliorating the spread of the Zika virus. For these models, we derive the basic reproduction number and sharp estimates for the final size relation. We first present a single-stage model which is later extended to a new multistage model for Zika that incorporates more realistic incubation stages for both the humans and vectors. For each of these models, we derive a basic reproduction number and a final size relation estimate...
2017: Computational and Mathematical Methods in Medicine
Andrea Bommert, Jörg Rahnenführer, Michel Lang
Finding a good predictive model for a high-dimensional data set can be challenging. For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable. This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial. We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features...
2017: Computational and Mathematical Methods in Medicine
Gang Wang, Yuanyuan Li, Xiufen Zou
Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules...
2017: Computational and Mathematical Methods in Medicine
Andreas Mayr, Benjamin Hofner, Elisabeth Waldmann, Tobias Hepp, Sebastian Meyer, Olaf Gefeller
Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting)...
2017: Computational and Mathematical Methods in Medicine
Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slightly altered data (e.g., cross-validation or bootstrap) to find the optimal number of boosting iterations and prevent overfitting. In our proposed approach, we augment the data set with randomly permuted versions of the true variables, so-called shadow variables, and stop the stepwise fitting as soon as such a variable would be added to the model...
2017: Computational and Mathematical Methods in Medicine
Tabea Treppmann, Katja Ickstadt, Manuela Zucknick
Bayesian variable selection becomes more and more important in statistical analyses, in particular when performing variable selection in high dimensions. For survival time models and in the presence of genomic data, the state of the art is still quite unexploited. One of the more recent approaches suggests a Bayesian semiparametric proportional hazards model for right censored time-to-event data. We extend this model to directly include variable selection, based on a stochastic search procedure within a Markov chain Monte Carlo sampler for inference...
2017: Computational and Mathematical Methods in Medicine
Qiongbin Lin, Qiuhua Liu, Tianyue Lai, Wu Wang
The filter problem with missing value for genetic regulation networks (GRNs) is addressed, in which the noises exist in both the state dynamics and measurement equations; furthermore, the correlation between process noise and measurement noise is also taken into consideration. In order to deal with the filter problem, a class of discrete-time GRNs with missing value, noise correlation, and time delays is established. Then a new observation model is proposed to decrease the adverse effect caused by the missing value and to decouple the correlation between process noise and measurement noise in theory...
2017: Computational and Mathematical Methods in Medicine
Pietro Cipresso, Aleksandar Matic, Guillaume Lopez, Silvia Serino
No abstract text is available yet for this article.
2017: Computational and Mathematical Methods in Medicine
Fan Yang, Yan He, Mubashir Hussain, Hong Xie, Pinggui Lei
Free-breathing cardiac magnetic resonance (CMR) imaging has short examination time with high reproducibility. Detection of the end-diastole and the end-systole frames of the free-breathing cardiac magnetic resonance, supplemented by visual identification, is time consuming and laborious. We propose a novel method for automatic identification of both the end-diastole and the end-systole frames, in the free-breathing CMR imaging. The proposed technique utilizes the convolutional neural network to locate the left ventricle and to obtain the end-diastole and the end-systole frames from the respiratory motion signal...
2017: Computational and Mathematical Methods in Medicine
Jie Sun, Ke Chen
Nucleotides play a central role in life-form metabolism, by interacting with proteins and mediating the function of proteins. It is estimated that nucleotides constitute about 15% of the biologically relevant ligands included in PDB. Prediction of binding sites of nucleotides is useful in understanding the function of proteins and can facilitate the in silico design of drugs. In this study, we propose a nucleotide-binding site predictor, namely, NSiteMatch. The NSiteMatch algorithm integrates three different strategies: geometrical analysis, energy calculation, and template comparison...
2017: Computational and Mathematical Methods in Medicine
Rendani Netshikweta, Winston Garira
Guinea worm disease (GWD) is both a neglected tropical disease and an environmentally driven infectious disease. Environmentally driven infectious diseases remain one of the biggest health threats for human welfare in developing countries and the threat is increased by the looming danger of climate change. In this paper we present a multiscale model of GWD that integrates the within-host scale and the between-host scale. The model is used to concurrently examine the interactions between the three organisms that are implicated in natural cases of GWD transmission, the copepod vector, the human host, and the protozoan worm parasite (Dracunculus medinensis), and identify their epidemiological roles...
2017: Computational and Mathematical Methods in Medicine
Yiran Wang, Zhifeng Chen, Jing Wang, Lixia Yuan, Ling Xia, Feng Liu
The k-t principal component analysis (k-t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k-t PCA that combines the k-t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k-t PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled k-t space from the original reconstructed k-t space...
2017: Computational and Mathematical Methods in Medicine
Konstantinos Bromis, Kostakis Gkiatis, Irene Karanasiou, George Matsopoulos, Eustratios Karavasilis, Matilda Papathanasiou, Efstathios Efstathopoulos, Nikolaos Kelekis, Vasileios Kouloulias
Previous studies in small-cell lung cancer (SCLC) patients have mainly focused on exploring neurocognitive deficits associated with prophylactic cranial irradiation (PCI). Little is known about functional brain alterations that might occur due to chemotherapy treatment in this population before PCI is administered. For this reason, we used resting-state functional Magnetic Resonance Imaging (fMRI) to examine potential functional connectivity disruptions in brain networks, including the Default Mode Network (DMN), the Sensorimotor Network, and the Task-Positive Network (TPN)...
2017: Computational and Mathematical Methods in Medicine
Stefanie Friedrichs, Juliane Manitz, Patricia Burger, Christopher I Amos, Angela Risch, Jenny Chang-Claude, Heinz-Erich Wichmann, Thomas Kneib, Heike Bickeböller, Benjamin Hofner
The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm...
2017: Computational and Mathematical Methods in Medicine
Saima Anis, Madad Khan, Saqib Khan
We introduced a new nonassociative algebra, namely, left almost algebra, and discussed some of its genetic properties. We discussed the relation of this algebra with flexible algebra, Jordan algebra, and generalized Jordan algebra.
2017: Computational and Mathematical Methods in Medicine
J S Estepa Jiménez, M Díaz Lagos, S A Martinez-Ovalle
Different types the spectrum of photons were studied; they were emitted from the flattening filter of a LINAC Varian 2100 C/D that operates at 15 MV. The simplified geometry of the LINAC head was calculated using the MCNPX code based on the studies of the materials of the flattening filter, namely, SST, W, Pb, Fe, Ta, Al, and Cu. These materials were replaced in the flattening filter to calculate the photon spectra at the output of this device to obtain the spectrum that makes an impact with the patient. The different spectra obtained were analyzed and compared to the emission from the original spectra configuration of the LINAC, which uses material W...
2017: Computational and Mathematical Methods in Medicine
Anqi Miao, Jian Zhang, Tongqian Zhang, B G Sampath Aruna Pradeep
A stochastic SIR model with vertical transmission and vaccination is proposed and investigated in this paper. The threshold dynamics are explored when the noise is small. The conditions for the extinction or persistence of infectious diseases are deduced. Our results show that large noise can lead to the extinction of infectious diseases which is conducive to epidemic diseases control.
2017: Computational and Mathematical Methods in Medicine
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