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Areum Han

dblp:34/4826 · DBLP profile ↗
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3ranked-venue papers
2as first author
1since 2021 · last 2023
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-authorArtificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › biological database
genetic variation database
0.112007
SNP2NMD: A database of human single nucleotide polymorphisms causing nonsense-mediated mRNA decay · Bioinform. 2007

Methods — techniques the papers use, named apart from their topics

web interface · 0.1database construction · 0.1
YearPublicationVenuePosition
2023 Multi-objective optimization of explosive waste treatment process considering environment via Bayesian active learning
Sunghyun Cho, Areum Han, Jonggeol Na, Il Moon
Eng. Appl. Artif. Intell.4
2014 De Novo Prediction of PTBP1 Binding and Splicing Targets Reveals Unexpected Features of Its RNA Recognition and Function
abstract
The splicing regulator Polypyrimidine Tract Binding Protein (PTBP1) has four RNA binding domains that each binds a short pyrimidine element, allowing recognition of diverse pyrimidine-rich sequences. This variation makes it difficult to evaluate PTBP1 binding to particular sites based on sequence alone and thus to identify target RNAs. Conversely, transcriptome-wide binding assays such as CLIP identify many in vivo targets, but do not provide a quantitative assessment of binding and are informative only for the cells where the analysis is performed. A general method of predicting PTBP1 binding and possible targets in any cell type is needed. We developed computational models that predict the binding and splicing targets of PTBP1. A Hidden Markov Model (HMM), trained on CLIP-seq data, was used to score probable PTBP1 binding sites. Scores from this model are highly correlated (ρ = -0.9) with experimentally determined dissociation constants. Notably, we find that the protein is not strictly pyrimidine specific, as interspersed Guanosine residues are well tolerated within PTBP1 binding sites. This model identifies many previously unrecognized PTBP1 binding sites, and can score PTBP1 binding across the transcriptome in the absence of CLIP data. Using this model to examine the placement of PTBP1 binding sites in controlling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulated exons. Applying this model to rank exons across the mouse transcriptome identifies known PTBP1 targets and many new exons that were confirmed as PTBP1-repressed by RT-PCR and RNA-seq after PTBP1 depletion. We find that PTBP1 dependent exons are diverse in structure and do not all fit previous descriptions of the placement of PTBP1 binding sites. Our study uncovers new features of RNA recognition and splicing regulation by PTBP1. This approach can be applied to other multi-RRM domain proteins to assess binding site degeneracy and multifactorial splicing regulation.
Areum Han, Peter Stoilov, Anthony J. Linares, Yu Zhou 0021, Xiang-Dong Fu, Douglas L. Black
PLoS Comput. Biol.1
2007 SNP2NMD: A database of human single nucleotide polymorphisms causing nonsense-mediated mRNA decay
abstract
UNLABELLED: Elucidating the effects of genetic polymorphisms on genes and gene networks is an important step in disease association studies. We developed the SNP2NMD database for human SNPs (single nucleotide polymorphisms) that result in PTCs (premature termination codons) and trigger nonsense-mediated mRNA decay (NMD). The SNP2NMD Web interfaces provide extensive genetic information on and graphical views of the queried SNP, gene, and disease terms. AVAILABILITY: SNP2NMD is available from http://variome.net, or directly from http://bioportal.kobic.re.kr/SNP2NMD. SUPPLEMENTARY INFORMATION: http://bioportal.kobic.re.kr/SNP2NMD/Wiki.jsp?page=Statistics.
Areum Han, Woo-Yeon Kim
Bioinform.1