| A |
| association analysis | An Association Analysis of Breast Cancer with Carotenoids. |
| Attractors landscape | Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| B |
| Bioinformatics | Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review
Utilizing Functional Annotation of Disease Genes for Disease Clustering
The Velvet Assembler Using OpenACC Directives |
| biomarker discovery | Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review |
| biomarkers | An Association Analysis of Breast Cancer with Carotenoids. |
| C |
| cancer | An Association Analysis of Breast Cancer with Carotenoids. |
| Cell differentiation and morphogenesis | Clarifying the biological nature of the interaction between the systems-based epigenetic landscape and the epigenome |
| CNN | Classifying Protein Families with Learned Compressed Representations |
| complex networks | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach |
| COVID-19 | Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis |
| D |
| Discrete Boolean networks | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach
Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis
Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| Disease clustering | Utilizing Functional Annotation of Disease Genes for Disease Clustering |
| Disease similarity and relationship | Utilizing Functional Annotation of Disease Genes for Disease Clustering |
| E |
| Epigenetic regulation | Clarifying the biological nature of the interaction between the systems-based epigenetic landscape and the epigenome |
| epigenomics | Clarifying the biological nature of the interaction between the systems-based epigenetic landscape and the epigenome |
| Epithelial cancer | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach
Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| epithelial-to-mesenchymal transition | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach
Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| F |
| Feedback-based interactions | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach |
| G |
| gene mutations | Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| gene regulatory networks | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach
Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis
Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| H |
| Human blood pressure patterns | Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis |
| human exposome | An Association Analysis of Breast Cancer with Carotenoids. |
| Hypertension | Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis |
| I |
| Inductive Vs Transductive SVM | Transmembrane Protein Inter-Helical Residue Contacts Prediction Using Transductive Support Vector Machines |
| Inflammatory response | Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis |
| Inter-Helical Residue Contacts | Transmembrane Protein Inter-Helical Residue Contacts Prediction Using Transductive Support Vector Machines |
| M |
| machine learning | Classifying Protein Families with Learned Compressed Representations |
| mammalian cell cycle | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach |
| medical systems biology | Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| Methematical modeling | Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis |
| multi-omics | Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review |
| N |
| neural network | Classifying Protein Families with Learned Compressed Representations |
| O |
| OpenACC | The Velvet Assembler Using OpenACC Directives |
| P |
| parallel program | The Velvet Assembler Using OpenACC Directives |
| Protein family classification | Classifying Protein Families with Learned Compressed Representations |
| R |
| reachability analysis | Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| S |
| semi-tensor product | Uncovering the role of mutations in Epithelial-to-Mesenchymal transition through computational analysis of the underlying gene regulatory network |
| systems biology | Uncovering the interdependence between hypertension and the inflammatory response for the patient affected by Covid 19 through mathematical modeling and computer-based analysis
Clarifying the biological nature of the interaction between the systems-based epigenetic landscape and the epigenome |
| Systems biology of cancer | Uncovering the particularities of the dynamical interaction between cancer-related Epithelial-Mesenchymal Transition and the Mammalian Cell Cycle: a feedback-based Boolean networks interconnection approach |
| Systems-based epigenetics | Clarifying the biological nature of the interaction between the systems-based epigenetic landscape and the epigenome |
| T |
| tensor decompositions | Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review |
| Transductive Support Vector Machines | Transmembrane Protein Inter-Helical Residue Contacts Prediction Using Transductive Support Vector Machines |
| transmembrane protein | Transmembrane Protein Inter-Helical Residue Contacts Prediction Using Transductive Support Vector Machines |
| V |
| VAE | Classifying Protein Families with Learned Compressed Representations |
| Velvet | The Velvet Assembler Using OpenACC Directives |
