Showing papers in "Journal of Theoretical Biology in 2019"

TL;DR: This paper proposes a multi-kernel SVM to predict subcellular localization of both multi-location and single-location proteins, and makes use of the evolutionary information extracted from position specific scoring matrix (PSSM) and physicochemical properties of proteins, by Chou's general PseAAC and other efficient functions.

TL;DR: It is argued that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity ofIts underlying processes.

TL;DR: A novel predictor namely SPrenylC-PseAAC is proposed, which can help in predicting the Prenylation sites in an efficient and accurate way by integrating the Chou's Pseudo Amino Acid Composition and relative/absolute position-based features.

TL;DR: The reported outcomes show that iRNA-PseKNC(2methyl) method might be beneficial for the academic research and drug design and obtained better outcomes than existing method in terms of all evaluation parameters.

TL;DR: A new predictor, called "iPPI-PseAAC(CGR)", was developed by incorporating the information of "chaos game representation" into the PseAAC (Pseudo Amino Acid Composition), where some key sequence-order or sequence-pattern information can be more effectively incorporated during the treatment of the protein pair samples.

TL;DR: The experimental results show that the method proposed in this paper selects fewer feature genes and achieves higher classification accuracy, and rL-GenSVM uses regularization parameters to avoid overfitting and can be widely applied to high-dimensional and small-sample tumor data classification.

TL;DR: This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models and incorporating one or more immune cell types and evaluating immune cell effects on tumor progression.

TL;DR: This study presents a three-dimensional generalisation of this mathematical framework through the maturing theory of coupled bulk-surface semilinear partial differential equations in which protein compartmentalisation becomes natural.

TL;DR: In the present study, a novel method is proposed for the prediction of intramolecular disulfide bonds accurately using statistical moments and PseAAC and it outperforms various previously reported studies.

TL;DR: SBP interaction with their targets is almost a binding phenomenon at single-molecule level, but presence of a polypeptide linker between the SBP and target can promote the binding efficiency since the linker restriction largely increases the probability of SBP-target encounters in a statistical physics point of view.

TL;DR: This work demonstrates that the well-known Rosenzweig-MacArthur predator-prey model can undergo a rate-induced critical transition in response to a continuous decline in the habitat quality, resulting in a collapse of the predator and prey populations.

TL;DR: The elements of evolutionary game theory and mathematical epidemiology are combined to comprehensively evaluate the performance of vaccination-subsidizing policies in the face of a seasonal epidemic and imply that subsidies should be aimed at voluntary vaccinators while avoiding overspending.

TL;DR: This work considers populations evolving in a heterogeneous environment, which consists of patches of different qualities that are occupied by one individual each, and introduces environmental feedback between an individual's strategy and the quality of its patch results in rich eco-evolutionary dynamics.

TL;DR: A half-serious/half-tongue-in-cheek model is developed to explain how cliques form in academia, in which assumptions in which scholars alter their research line according to available problems leads to clustering of academics and the formation of "hot" research topics.

TL;DR: Magnetohydrodynamic (MHD) bioconvective flow of Williamson nanomaterial in frame of gyrotactic microorganisms is addressed and transformed nonlinear systems of momentum, energy, nanoparticles concentration and motile microorganisms density are solved numerically through the bvp4c technique.

TL;DR: Non-normality promotes the emergence of patterns in cases where a classical linear analysis would not predict them, and relies on the fact that non-normal networks are pervasively found, motivating the general interest of the mechanism here discussed.

TL;DR: Epidemic modeling has and continues to play a critical role in informing HCV elimination strategies worldwide and indicates elimination is achievable in a wide variety of epidemic settings with harm reduction scale-up combined with modest levels of HCV treatment for PWID.

TL;DR: This paper has extracted several features solely using the protein sequence and carried out two different types of feature selection on them and their results have proven comparable on training set and significantly improved on the independent test set.

TL;DR: A mathematical model is proposed to study the effect of awareness among the people in crop pest management using plant biomass, pest and aware population using time delay as the bifurcation parameter.

TL;DR: The proposed system has achieved better accuracy than state-of-art systems in the prediction of antioxidant proteins from non-antioxidant proteins using 10-fold-cross-validation tests, suggesting that the use of statistical moments with a multilayer neural network could bear more effective and efficient results.

TL;DR: In this paper, the authors discuss several continuum cell-cell adhesion models based on the underlying microscopic assumptions and propose an improvement on these models leading to sharp fronts and intermingling invasion fronts between different cell type populations.

TL;DR: A Fu-SulfPred model is designed using stratified structure of three kinds of decision trees in order to identify possible protein S-sulfenylation sites by means of reconstructing training datasets and sample rescaling technology and shows promising results.

TL;DR: This study developed a predictor named LFPred to identify lysine formylation sites using sequence features (including amino acid composition, binary profile features, and amino acid index) combined K-nearest neighbor algorithm as classifier and chose discrete window instead of continuous window according to information entropy.

TL;DR: Effects due to the evolution of nonuniform patterns under growth, suggesting important roles for growth in reaction-diffusion systems beyond modifying instability regimes are suggested.

TL;DR: The experimental results show that the proposed multi-information fusion prediction method can effectively improve the prediction performance of PPIs.

TL;DR: This framework shows that qualitative changes to the outcome can be directly attributable to eco-evolutionary feedbacks, for example, shifting the dynamics between stable monomorphism or polymorphism and cycling, as well as changing the nature of the cycles.

TL;DR: A novel and high throughput computational model is proposed which can identify the subGolgi proteins precisely and has yielded an accuracy of 98% in jackknife cross-validation, 94% in independent data and 96% in 10-fold cross- validation test.

TL;DR: A number of common modeling approaches are reviewed, including Boolean (logical) models, systems of piecewise-linear or fully non-linear ordinary differential equations, and stochastic models (including hybrid deterministic/stochastic approaches), to help novice modelers choose a modeling strategy suitable to their problem.

TL;DR: In particular, the number of attractors in such networks scales as the DNA content raised to the 0.63 power as discussed by the authors, and the theory correctly predicts a power law relationship between number of cell types and the DNA contents per cell, and a comparable slope.

TL;DR: In this article, the boundary behaviours of motile Leishmania mexicana promastigotes are explored in a computational study using the boundary element method, with a model flagellar beating pattern that has been identified from digital videomicroscopy.