221 to 229 of 229 Results
Sep 13, 2018 - Final Year Projects
Ng, Ken Jo, 2018, "Socially Aware Flocking - Simulation Code", https://doi.org/10.21979/N9/KQXXCG, DR-NTU (Data), V1
Artificial Intelligence (AI) has become an increasingly important and popular topic not just within the field of Computer Science but also the world at large. One of the current challenges in the field of AI is multi-agent planning, of which Swarm Intelligence (SI) is a possible... |
Aug 23, 2018 - Bitcoin Graph Analytics
Oggier, Frederique Elise; Phetsouvanh, Silivanxay; Datta, Anwitaman, 2018, "A 5251 node directed Bitcoin address subgraph", https://doi.org/10.21979/N9/HUBNNX, DR-NTU (Data), V2, UNF:6:fNWNuT5AIaNHIwT7O3fkZg== [fileUNF]
This dataset contains an address subgraph of the Bitcoin network comprising 5251 nodes. Every line contains 2 Bitcoin addresses separated by a comma. This represents a directed edge in this subgraph. This data was extracted from the Bitcoin transaction network by Phetsouvanh Sili... |
Aug 22, 2018 - Bitcoin Graph Analytics
Oggier, Frederique Elise; Phetsouvanh, Silivanxay; Datta, Anwitaman, 2018, "A 5026 node directed Bitcoin address subgraph", https://doi.org/10.21979/N9/JQUY8Q, DR-NTU (Data), V1
This dataset contains an address subgraph of the Bitcoin network comprising 5026 nodes. Every line contains 2 Bitcoin addresses separated by a comma. This represents a directed edge in this subgraph. This data was extracted from the Bitcoin transaction network by Phetsouvanh Sili... |
Aug 22, 2018 - Bitcoin Graph Analytics
Oggier, Frederique Elise; Phetsouvanh, Silivanxay; Datta, Anwitaman, 2018, "A 178 node directed Bitcoin address subgraph", https://doi.org/10.21979/N9/TJMQ8L, DR-NTU (Data), V1
This dataset contains an address subgraph of the Bitcoin network comprising 178 nodes. Every line contains 2 Bitcoin addresses separated by a comma. This represents a directed edge in this subgraph. This data was extracted from the Bitcoin transaction network by Phetsouvanh Siliv... |
Dec 22, 2017 - Cutting Simulation
Weng, Bin, 2017, "Interactive cutting of thin deformable objects", https://doi.org/10.21979/N9/CN9V0Y, DR-NTU (Data), V2
videos, models and codes for the paper "interactive cutting of thin deformable objects" |
Zheng, Jie, 2017, "NetLand: quantitative modeling and visualization of Waddington’s epigenetic landscape using probabilistic potential", https://doi.org/10.21979/N9/IP6DBD, DR-NTU (Data), V1
About NetLand NetLand is a software for quantitative modeling and visualization of Waddington’s epigenetic landscape. NetLand is intended for modeling, simulation and visualization of gene regulatory networks (GRNs) and their corresponding quasi-potential landscapes. Users can im... |
Sep 19, 2017 - SynLethDB
Zheng, Jie, 2017, "SynLethDB: synthetic lethality database towards discovery of selective and sensitive anticancer drug targets", https://doi.org/10.21979/N9/CVEVDZ, DR-NTU (Data), V1
This dataset is used in the SynLethDB which is the first comprehensive database that harbours a large set of synthetic lethality (SL) gene pairs collected from a variety of sources including biochemical assays, other related databases, computational predictions and text mining. I... |
Sep 19, 2017 - Sig2GRN
Zheng, Jie, 2017, "Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation", https://doi.org/10.21979/N9/SO9VRB, DR-NTU (Data), V1
Sig2GRN is a software tool which links the models of signaling pathway with gene regulatory networks (GRNs). A generalized logical model, Sign2GRN is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict th... |
Sep 19, 2017 - Knowledge-Guided Fuzzy Logic Network Modeling
Zheng, Jie, 2017, "Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data", https://doi.org/10.21979/N9/VLZWCL, DR-NTU (Data), V1
About Knowledge-guided fuzzy logic network model Knowledge-guided fuzzy logic network model is a new hybrid method to integrate the prior knowledge and data-driven learning for signaling pathway inference. It is applied to infer signaling pathways by exploiting both prior knowled... |
