Seunghyun Park

Concurrent simulation platform for energy-aware smart metering systems

We propose a simulation framework that can model a house equipped with various home appliances and next-generation smart metering devices. This simulator can predict the power dissipation profiles of individual appliances as well as the cumulative energy consumption of the house in a realistic manner. We utilize SystemC, a concurrent system-modeling methodology originally developed and populated in the design automation community. According to our experiments with various consumer electronics devices, the simulated and measured power profiles match very closely, producing the average correlation of 0.973. The deviation of simulated energy consumption from the measurement was also negligible. Using the proposed simulation platform, any electricity consumer interested in energy saving as well as the designer of a new smart metering system will be able to simulate and test their system from energy perspectives. As a case study, we show how the size of the accumulative power peak of a house can be reduced significantly by using the information provided by the proposed simulator.

HiTRACE: High-throughput robust analysis for capillary electrophoresis

MOTIVATION Capillary electrophoresis (CE) of nucleic acids is a workhorse technology underlying high-throughput genome analysis and large-scale chemical mapping for nucleic acid structural inference. Despite the wide availability of CE-based instruments, there remain challenges in leveraging their full power for quantitative analysis of RNA and DNA structure, thermodynamics and kinetics. In particular, the slow rate and poor automation of available analysis tools have bottlenecked a new generation of studies involving hundreds of CE profiles per experiment. RESULTS We propose a computational method called high-throughput robust analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in large-scale nucleic acid CE analysis, including the profile alignment that has heretofore been a rate-limiting step in the highest throughput experiments. We illustrate the application of HiTRACE on 13 datasets representing 4 different RNAs, 3 chemical modification strategies and up to 480 single mutant variants; the largest datasets each include 87 360 bands. By applying a series of robust dynamic programming algorithms, HiTRACE outperforms prior tools in terms of alignment and fitting quality, as assessed by measures including the correlation between quantified band intensities between replicate datasets. Furthermore, while the smallest of these datasets required 7-10 h of manual intervention using prior approaches, HiTRACE quantitation of even the largest datasets herein was achieved in 3-12 min. The HiTRACE method, therefore, resolves a critical barrier to the efficient and accurate analysis of nucleic acid structure in experiments involving tens of thousands of electrophoretic bands.

An Incremental Multicast Grouping Scheme for mmWave Networks with Directional Antennas

In millimeter wave wireless networks with directional antennas, the design of efficient multicast communications is one of the most challenging issues. In this letter, we propose an incremental multicast grouping (IMG) scheme where adaptive beamwidths are generated depending on the locations of multicast devices to maximize the sum rate of devices. We develop a simulator based on the IEEE 802.11ad MAC protocol. Simulation results demonstrate that the IMG scheme can improve the overall throughput by 28% to 79% compared with the conventional multicast schemes.

vHoT: a database for predicting interspecies interactions between viral microRNA and host genomes

Some viruses have been reported to transcribe microRNAs, implying complex relationships between the host and the pathogen at the post-transcriptional level through microRNAs in virus-infected cells. Although many computational algorithms have been developed for microRNA target prediction, few have been designed exclusively to find cellular or viral mRNA targets of viral microRNAs in a user-friendly manner. To address this, we introduce the viral microRNA host target (vHoT) database for predicting interspecies interactions between viral microRNA and host genomes. vHoT supports target prediction of 271 viral microRNAs from human, mouse, rat, rhesus monkey, cow, and virus genomes. vHoT is freely available at http://dna.korea.ac.kr/vhot.

deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks

MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them. Robust prediction of miRNA-mRNA pairs is of utmost importance in deciphering gene regulation but has been challenging because of high false positive rates, despite a deluge of computational tools that normally require laborious manual feature extraction. This paper presents an end-to-end machine learning framework for miRNA target prediction. Leveraged by deep recurrent neural networks-based auto-encoding and sequence-sequence interaction learning, our approach not only delivers an unprecedented level of accuracy but also eliminates the need for manual feature extraction. The performance gap between the proposed method and existing alternatives is substantial (over 25% increase in F-measure), and deepTarget delivers a quantum leap in the longstanding challenge of robust miRNA target prediction. [availability: http://data.snu.ac.kr/pub/deepTarget]

The use of exome genotyping to predict pathological Gleason score upgrade after radical prostatectomy in low-risk prostate cancer patients.

Background Active surveillance (AS) is a promising option for patients with low-risk prostate cancer (PCa), however current criteria could not select the patients correctly, many patients who fulfilled recent AS criteria experienced pathological Gleason score upgrade (PGU) after radical prostatectomy (RP). In this study, we aimed to develop an accurate model for predicting PGU among low-risk PCa patients by using exome genotyping. Methods We genotyped 242,221 single nucleotide polymorphisms (SNP)s on a custom HumanExome BeadChip v1.0 (Illuminam Inc.) in blood DNA from 257 low risk PCa patients (PSA <10 ng/ml, biopsy Gleason score (GS) ≤6 and clinical stage ≤T2a) who underwent radical prostatectomy. Genetic data were analyzed using an unconditional logistic regression to calculate an odds ratio as an estimate of relative risk of PGU, which defined pathologic GS above 7. Among them, we selected persistent SNPs after multiple testing using FDR method, and we compared accuracies from the multivariate logistic model incorporating clinical factors between included and excluded selected SNP information. Results After analysis of exome genotyping, 15 SNPs were significant to predict PGU in low risk PCa patients. Among them, one SNP – rs33999879 remained significant after multiple testing. When a multivariate model incorporating factors in Epstein definition – PSA density, biopsy GS, positive core number, tumor per core ratio and age was devised for the prediction of PGU, the predictive accuracy of the multivariate model was 78.4% (95%CI: 0.726–0.834). By addition the factor of rs33999879 in aforementioned multivariate model, the predictive accuracy was 82.9%, which was significantly increased (p = 0.0196). Conclusion The rs33999879 SNP is a predictor for PGU. The addition of genetic information from the exome sequencing effectively enhanced the predictive accuracy of the multivariate model to establish suitable active surveillance criteria.

In-depth analysis of interrelation between quality scores and real errors in illumina reads

In sequencing results, the quality score is reported for each base, representing the probability that the base is called incorrectly. The notion of quality scores was initially developed for conventional Sanger sequencing, but is widely used for next-generation sequencing techniques, including Illumina. In this paper, we carry out in-depth analysis of quality scores reported for Illumina reads and present how they are related to real errors in the reads. We confirmed strong interrelation between quality scores and real errors in Illumina reads, and observed that reverse reads tend to have lower quality scores than forward reads in paired-end reads do. In addition, we discovered other interesting patterns from quality score analysis. Our hope is that the findings in this paper will be helpful for designing error-correction and/or filtering methods for next-generation sequencing.

Multi-hop-based opportunistic concurrent directional transmission in 60 GHz WPANs

In millimeter Wave wireless personal area networks (mmWave WPANs), the design of efficient concurrent transmission considered high modulations up to a few Gbps is one of the most challenging issues. Even for the concurrent transmission over mmWave networks, the use of directional antenna is highly recommended to guarantee high modulations and to overcome short propagation range caused to high path loss in mmWave frequency. Nevertheless the directional antenna has many advantages, users may suffer from performance degradation due to coverage limitation of wide beamwidth, when the concurrent transmission supports the multicast communication for the target applications such as conference room, wireless displays and room gaming. In this paper, we propose a multi-hop-based opportunistic concurrent directional transmission (M-OCDT) scheme for the directional multicast communication where the relay mechanism is generated depending on the locations of multicast users to maximize the sum rate. The proposed M-OCDT scheme is designed based on the IEEE 802.15.3c standard and supports the optimized searching algorithm for the relay users. Extensive simulation results demonstrate that the M-OCDT scheme can improve the average overall throughput by 81 to 89 % compared with the conventional non-relay directional multicast procedure.

An Adaptive Allocation Algorithm using Directional CSMA/CA over mmWave Wireless Personal Area Networks

Directional antennas have the considerable benefits of higher antenna gain, long transmission distance and spatial reuse compared to omni-antennas. To support a directional antenna, IEEE 802.15.3c ...

Distributed Relay-Assisted Retransmission Scheme for Wireless Home Networks

A relay transmission is a promising technology to improve network performance in dynamic infrastructure. In this paper, we propose a distributed relay-assisted retransmission (DRR) scheme in multirate wireless home networks. The idea is to exploit overhearing nodes to retransmit on behalf of sender node after receiving the block acknowledgement (B-ACK) from destination node. For the first transmission, a basic relay (BR) node is used by considering the high data rate between source node and BR node. And then, for the retransmission, a retransmission relay (RR) node is used by considering the high data rate between RR node and destination node. The DRR scheme extends a distributed reservation protocol in WiMedia home networks and inquires the candidate relay node as BR nodes and RR nodes during beacon period. In addition, the DRR scheme can minimize control overhead for relay transmission because all nodes should send and listen to the beacon frames of neighbor nodes during beacon period. We also present the relay decision scheme and channel allocation procedure for maximizing the efficiency in the DRR scheme. Extensive simulation results demonstrate that the DRR scheme can improve the overall throughput by 40% and reduce the energy consumption by 47% compared with nonrelay transmission schemes when the number of nodes increases.