Soo-Yong Shin

Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing

DNA computing relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. Sequence design involves with a number of heterogeneous and conflicting design criteria and traditional optimization methods may face difficulties. In this paper, we formulate the DNA sequence design as a multiobjective optimization problem and solve it using a constrained multiobjective evolutionary algorithm (EA). The method is implemented into the DNA sequence design system, NACST/Seq, with a suite of sequence-analysis tools to help choose the best solutions among many alternatives. The performance of NACST/Seq is compared with other sequence design methods, and analyzed on a traveling salesman problem solved by bio-lab experiments. Our experimental results show that the evolutionary sequence design by NACST/Seq outperforms in its reliability the existing sequence design techniques such as conventional EAs, simulated annealing, and specialized heuristic methods.

PIE: an online prediction system for protein–protein interactions from text

Protein–protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.

Aperture maneuver with compelled breath (AMC) for moving tumors: A feasibility study with a moving phantom

Respiration causes target motion, which is known to be one of the technical bottlenecks in radiotherapy, especially for stereotactic radio-surgery and intensity modulated radiotherapy (IMRT). To overcome this problem, aperture maneuver with compelled breath (AMC) has been developed. In order to simulate compelled respiratory motion, a moving phantom using a ventilator was designed. As the air flow was forced to the bellows, which simulates the lungs, by a ventilator, a film connected to the ventilator moved like the respiratory target motion. A software was developed to transfer multileaf collimator motion from breathless to actual periodic breathing conditions. Static fields as well as step-and-shoot IMRT fields were modified in accordance with moving shapes to follow the target position, using the software with the controlled breathing information. Film dosimetry for a small field and for IMRT fields with a moving phantom was performed. To evaluate clinical implementation, five healthy volunteers were tested to breathe through a ventilator, and all of them could adapt the compelled breath without any difficulties. Additive margins for a moving target with AMC were not larger than 3 mm for respiratory organ motions up to 18 mm, while those with the static beam were 9 mm. For IMRT fields, large discrepancies were present between a static target and a moving target with the static beam, while they coincided well with AMC. Clinical acceptable differences between the dose distributions from a static target with the static beam and from a moving target with AMC revealed that this technique could be applied clinically.

Correlation between National Influenza Surveillance Data and Google Trends in South Korea

Background In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea. Methods Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearsons correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons. Results The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05). Conclusions In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.

Evolutionary sequence generation for reliable DNA computing

Since DNA computing technologies use the bio-molecules as basic computing materials, DNA computing involves the possibilities for errors caused by the chemical characteristics of bio-molecules. To overcome these drawbacks, many researchers have studied the design of DNA sequences to reduce the possibilities for illegal reactions. We developed an evolutionary sequence generation system to minimize the potential errors in DNA sequences for reliable DNA computing. We verified our system by investigating the sequences designed by another sequence generator, and generated the sequences for solving travelling salesman problems.

Temperature Gradient-Based DNA Computing for Graph Problems with Weighted Edges

We propose an encoding method of numerical data in DNA using temperature gradient. We introduce melting temperature (Tm) for this purpose. Melting temperature is a unique characteristic to manipulate the hybridization and denaturation processes that used in the key steps in DNA computing such as the solution generation step and the amplification step. DNA strands of lower melting temperature tend to denature with ease and also be easily amplified by slightly modified polymerase chain reaction, called denaturation temperature gradient polymerase chain reaction. Using these properties, we implement a local search molecular algorithm using temperature gradient, which is contrasted to conventional exhaustive search molecular algorithms. The proposed methods are verified by solving an instance of the travelling salesman problem. We could effectively amplify the correct solution and the use of temperature gradient made the detection of solutions easier.

PIE the search

Motivation: Finding protein-protein interaction (PPI) information from literature is challenging but an important issue. However, keyword search in PubMed® is often time consuming because it requires a series of actions that refine keywords and browse search results until it reaches a goal. Due to the rapid growth of biomedical literature, it has become more difficult for biologists and curators to locate PPI information quickly. Therefore, a tool for prioritizing PPI informative articles can be a useful assistant for finding this PPI-relevant information. Results: PIE (Protein Interaction information Extraction) the search is a web service implementing a competition-winning approach utilizing word and syntactic analyses by machine learning techniques. For easy user access, PIE the search provides a PubMed-like search environment, but the output is the list of articles prioritized by PPI confidence scores. By obtaining PPI-related articles at high rank, researchers can more easily find the up-to-date PPI information, which cannot be found in manually curated PPI databases. Availability: http://www.ncbi.nlm.nih.gov/IRET/PIE/ Contact: sun.kim@nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

TRIP Database: a manually curated database of protein–protein interactions for mammalian TRP channels

Transient receptor potential (TRP) channels are a superfamily of Ca2+-permeable cation channels that translate cellular stimuli into electrochemical signals. Aberrant activity of TRP channels has been implicated in a variety of human diseases, such as neurological disorders, cardiovascular disease and cancer. To facilitate the understanding of the molecular network by which TRP channels are associated with biological and disease processes, we have developed the TRIP (TRansient receptor potential channel-Interacting Protein) Database (http://www.trpchannel.org), a manually curated database that aims to offer comprehensive information on protein–protein interactions (PPIs) of mammalian TRP channels. The TRIP Database was created by systematically curating 277 peer-reviewed literature; the current version documents 490 PPI pairs, 28 TRP channels and 297 cellular proteins. The TRIP Database provides a detailed summary of PPI data that fit into four categories: screening, validation, characterization and functional consequence. Users can find in-depth information specified in the literature on relevant analytical methods and experimental resources, such as gene constructs and cell/tissue types. The TRIP Database has user-friendly web interfaces with helpful features, including a search engine, an interaction map and a function for cross-referencing useful external databases. Our TRIP Database will provide a valuable tool to assist in understanding the molecular regulatory network of TRP channels.

A comprehensive manually curated protein–protein interaction database for the Death Domain superfamily

The Death Domain (DD) superfamily, which is one of the largest classes of protein interaction modules, plays a pivotal role in apoptosis, inflammation, necrosis and immune cell signaling pathways. Because aberrant or inappropriate DD superfamily-mediated signaling events are associated with various human diseases, such as cancers, neurodegenerative diseases and immunological disorders, the studies in these fields are of great biological and clinical importance. To facilitate the understanding of the molecular mechanisms by which the DD superfamily is associated with biological and disease processes, we have developed the DD database (http://www.deathdomain.org), a manually curated database that aims to offer comprehensive information on protein–protein interactions (PPIs) of the DD superfamily. The DD database was created by manually curating 295 peer-reviewed studies that were published in the literature; the current version documents 175 PPI pairs among the 99 DD superfamily proteins. The DD database provides a detailed summary of the DD superfamily proteins and their PPI data. Users can find in-depth information that is specified in the literature on relevant analytical methods, experimental resources and domain structures. Our database provides a definitive and valuable tool that assists researchers in understanding the signaling network that is mediated by the DD superfamily.

TRIP Database 2.0: A Manually Curated Information Hub for Accessing TRP Channel Interaction Network

Transient receptor potential (TRP) channels are a family of Ca2+-permeable cation channels that play a crucial role in biological and disease processes. To advance TRP channel research, we previously created the TRIP (TRansient receptor potential channel-Interacting Protein) Database, a manually curated database that compiles scattered information on TRP channel protein-protein interactions (PPIs). However, the database needs to be improved for information accessibility and data utilization. Here, we present the TRIP Database 2.0 (http://www.trpchannel.org) in which many helpful, user-friendly web interfaces have been developed to facilitate knowledge acquisition and inspire new approaches to studying TRP channel functions: 1) the PPI information found in the supplementary data of referred articles was curated; 2) the PPI summary matrix enables users to intuitively grasp overall PPI information; 3) the search capability has been expanded to retrieve information from ‘PubMed’ and ‘PIE the search’ (a specialized search engine for PPI-related articles); and 4) the PPI data are available as sif files for network visualization and analysis using ‘Cytoscape’. Therefore, our TRIP Database 2.0 is an information hub that works toward advancing data-driven TRP channel research.