| Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC |
42 |
| Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC |
27 |
| SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins |
26 |
| iMethyl-STTNC: Identification of N-6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences |
24 |
| Self-binding peptides: Binding-upon-folding versus folding-upon-binding |
23 |
| iRNA-PseKNC(2methyl): Identify RNA 2 '-O-methylation sites by convolution neural network and Chou's pseudo components |
21 |
| Mathematical modeling of tumor-immune cell interactions |
18 |
| Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition |
17 |
| DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC |
17 |
| iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition |
16 |
| PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework |
16 |
| Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer |
16 |
| Is the cell really a machine? |
16 |
| To vaccinate or not to vaccinate: A comprehensive study of vaccination-subsidizing policies with multi-agent simulations and mean-field modeling |
16 |
| pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC |
16 |
| Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine |
15 |
| iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC |
15 |
| The fossilized birth-death model for the analysis of stratigraphic range data under different speciation modes |
14 |
| pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments |
14 |
| Theoretical minimal RNA rings recapitulate the order of the genetic code's codon-amino acid assignments |
13 |
| Magnetohydrodynamic bioconvective flow of Williamson nanofluid containing gyrotactic microorganisms subjected to thermal radiation and Newtonian conditions |
13 |
| Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC |
12 |
| Mathematical model of immune response to hepatitis B |
12 |
| Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach |
12 |
| Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method |
12 |
| Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information |
12 |
| MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components |
11 |
| A mathematical model of antibody-dependent cellular cytotoxicity (ADCC) |
11 |
| Free-energy minimization in joint agent-environment systems: A niche construction perspective |
11 |
| Asymmetric evolutionary games with environmental feedback |
11 |
| dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components |
11 |
| What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance |
11 |
| Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou's General PseAAC |
11 |
| Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC |
11 |
| Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC |
10 |
| A coupled bulk-surface model for cell polarisation |
10 |
| Niche emergence as an autocatalytic process in the evolution of ecosystems |
10 |
| Modelling skin wound healing angiogenesis: A review |
10 |
| How ecosystems recover from pulse perturbations: A theory of short- to long-term responses |
10 |
| Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC |
10 |
| Modeling the 2016-2017 Yemen cholera outbreak with the impact of limited medical resources |
10 |
| Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions |
9 |
| Assessing the efficiency of Wolbachia driven Aedes mosquito suppression by delay differential equations |
9 |
| Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains |
9 |
| Mathematical model and intervention strategies for mitigating tuberculosis in the Philippines |
9 |
| Group size effects in social evolution |
9 |
| NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC |
9 |
| Reentrainment of the circadian pacemaker during jet lag: East-west asymmetry and the effects of north-south travel |
9 |
| A guideline to study the feasibility domain of multi-trophic and changing ecological communities |
8 |
| Mathematical models for chemotaxis and their applications in self-organisation phenomena |
8 |
