44 citations · 110 across the 12 of their papers we have counts for
13 papers
Towards a Rigorous Evaluation of Time-series Anomaly Detection
Siwon Kim, Kukjin Choi, Hyun-Soo Choi +2
In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. Howeve…
TargetNet: Functional microRNA Target Prediction with Deep Neural Networks
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs…
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
Jeonghee Jo, Bumju Kwak, Byunghan Lee +1
A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent…
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Bumju Kwak, Jiwon Park, Taewon Kang +3
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric…
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Seonwoo Min, Seunghyun Park, Siwon Kim +3
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modelin…
Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA
Jaekoo Lee, Byunghan Lee, Jongyoon Song +4
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly,…
DNA Steganalysis Using Deep Recurrent Neural Networks
Ho Bae, Byunghan Lee, Sunyoung Kwon +1
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various me…
Neural Universal Discrete Denoiser
Taesup Moon, Seonwoo Min, Byunghan Lee +1
We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser. Unlike other approaches that utilize supervised learning for denoising, w…
Deep Learning in Bioinformatics
Seonwoo Min, Byunghan Lee, Sungroh Yoon
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced ra…
DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks
Byunghan Lee, Taehoon Lee, Byunggook Na +1
A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary between a pair of exon and intr…
NASCUP: Nucleic Acid Sequence Classification by Universal Probability
Sunyoung Kwon, Gyuwan Kim, Byunghan Lee +3
Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statis…
DUDE-Seq: Fast, Flexible, and Robust Denoising for Targeted Amplicon Sequencing
Byunghan Lee, Taesup Moon, Sungroh Yoon +1
We consider the correction of errors from nucleotide sequences produced by next-generation targeted amplicon sequencing. The next-generation sequencing (NGS) platforms can provide…
Will solid-state drives accelerate your bioinformatics? In-depth profiling, performance analysis, and beyond
Sungmin Lee, Hyeyoung Min, Sungroh Yoon
A wide variety of large-scale data has been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computin…