BIOINFORMATICS TOOL AND MODEL DEVELOPMENT FOR STUDYING BIOLOGICAL NETWORKS AND PROTEIN-PROTEIN INTERACTIONS
type: CreativeWork
description: To cater to the extensive needs of quantitative analysis of biological networks, PROFEAT webserver was developed for computing the network descriptors, which offers distinguished advantages over the others by: providing the most comprehensive (up-to 379) network descriptors at node/edge/network-levels; supporting different network types; offering user-friendly access; and enabling automatic detection of multiple networks from a single input. PROFEAT would deliver the expanded understandings of systems biology. As tissue-specific networks have transcended the global network in biomarker/target discovery, we developed TISPIN database for human TIssue-Specific Protein Interaction Networks, with improved features over the existing resources. PPIs have been explored as therapeutic targeting space, where the drug potency is highly depended on the competitive advantages over the substrates of targets, but the PPI kinetic constants are sometimes unclear to ensure the drug competitiveness. Therefore, we studied Quantitative-Sequence-Kinetic-Constants-Relationship to predict PPI kinetic constants (Kd, koff, kon) from primary sequences on an expanded dataset.
abstract: To cater to the extensive needs of quantitative analysis of biological networks, PROFEAT webserver was developed for computing the network descriptors, which offers distinguished advantages over the others by: providing the most comprehensive (up-to 379) network descriptors at node/edge/network-levels; supporting different network types; offering user-friendly access; and enabling automatic detection of multiple networks from a single input. PROFEAT would deliver the expanded understandings of systems biology. As tissue-specific networks have transcended the global network in biomarker/target discovery, we developed TISPIN database for human TIssue-Specific Protein Interaction Networks, with improved features over the existing resources. PPIs have been explored as therapeutic targeting space, where the drug potency is highly depended on the competitive advantages over the substrates of targets, but the PPI kinetic constants are sometimes unclear to ensure the drug competitiveness. Therefore, we studied Quantitative-Sequence-Kinetic-Constants-Relationship to predict PPI kinetic constants (Kd, koff, kon) from primary sequences on an expanded dataset.
author: ZHANG PENG
keywords: biological network, network descriptor, webserver, database, protein-protein interaction, kinetic constants
date published: 2016-08-18
date modified: 2026-07-24T03:33:09Z
provider: National University of Singapore
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: National University of Singapore