Diisocyanates as novel molecular binders for monolayer assembly of zeolite crystals on glassElectronic supplementary information (ESI) available: experimental details. See http://www.rsc.org/suppdata/cc/b2/b205046c/

2002 ◽  
pp. 1846-1847 ◽  
Author(s):  
Yu Sung Chun ◽  
Kwang Ha ◽  
Yun-Jo Lee ◽  
Jin Seok Lee ◽  
Hyun Sung Kim ◽  
...  
Tetrahedron ◽  
2000 ◽  
Vol 56 (36) ◽  
pp. 6965-6968 ◽  
Author(s):  
Goo Soo Lee ◽  
Yun-Jo Lee ◽  
Kwang Ha ◽  
Kyung Byung Yoon

2000 ◽  
Vol 122 (21) ◽  
pp. 5201-5209 ◽  
Author(s):  
So Yeun Choi ◽  
Yun-Jo Lee ◽  
Yong Soo Park ◽  
Kwang Ha ◽  
Kyung Byung Yoon

2000 ◽  
Vol 122 (38) ◽  
pp. 9308-9309 ◽  
Author(s):  
Alexander Kulak ◽  
Yong Soo Park ◽  
Yun-Jo Lee ◽  
Yu Sung Chun ◽  
Kwang Ha ◽  
...  

2008 ◽  
Vol 20 (11) ◽  
pp. 2183-2189 ◽  
Author(s):  
Baoquan Zhang ◽  
Ming Zhou ◽  
Xiufeng Liu

2004 ◽  
Vol 72 (1-3) ◽  
pp. 91-98 ◽  
Author(s):  
Kwang Ha ◽  
Jin Seon Park ◽  
Kyoung Sun Oh ◽  
Yun-Shan Zhou ◽  
Yu Sung Chun ◽  
...  

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.


2020 ◽  
Vol 36 (9) ◽  
pp. 2690-2696
Author(s):  
Jarkko Toivonen ◽  
Pratyush K Das ◽  
Jussi Taipale ◽  
Esko Ukkonen

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.


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