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Jan Hoinka

dblp:77/11538 · DBLP profile ↗
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6ranked-venue papers
2as first author
1since 2021 · last 2025
0000-0002-0652-381XORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 6 · 2 first-author · 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
5 papers
Bioinformatics and computational biology · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
single-cell analysis
0.412019
Accurate Sub-population Detection and Mapping Across Single Cell Experiments with PopCorn · RECOMB 2019
Bioinformatics and computational biology › sequence analysis
aptamer analysis
0.322014
AptaCluster - A Method to Cluster HT-SELEX Aptamer Pools and Lessons from Its Application · RECOMB 2014
Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers · Bioinform. 2012
Bioinformatics and computational biology › structural bioinformatics › structural motif analysis
sequence-structure motif analysis
0.112012
Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers · Bioinform. 2012
Data mining › structured data mining › graph mining
motif discovery
0.112016
AptaTRACE: Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends in HT-SELEX Experiments · RECOMB 2016
Data mining
pattern mining
0.112016
AptaTRACE: Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends in HT-SELEX Experiments · RECOMB 2016
Bioinformatics and computational biology › RNA biology › RNA analysis › RNA bioinformatics
RNA structure analysis
0.012012
Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers · Bioinform. 2012

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

selection trend analysis · 0.5HT-SELEX · 0.5ensemble-based motif discovery · 0.1
YearPublicationVenuePosition
2025 Improved Algorithms for Bi-Partition Function Computation
John D. Bridgers, Jan Hoinka, Süleyman Cenk Sahinalp, Salem Malikic, Teresa M. Przytycka, Funda Ergün
WABI2
2019 Accurate Sub-population Detection and Mapping Across Single Cell Experiments with PopCorn
Yijie Wang 0004, Jan Hoinka, Teresa M. Przytycka
RECOMB2
2018 AptaBlocks: Accelerating the Design of RNA-Based Drug Delivery Systems
Yijie Wang 0004, Jan Hoinka, Piotr Swiderski, Teresa M. Przytycka
RECOMB2
2016 AptaTRACE: Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends in HT-SELEX Experiments
Phuong Dao, Jan Hoinka, Yijie Wang 0004, Mayumi Takahashi, Jiehua Zhou, Fabrizio Costa, John Rossi, John Burnett, Rolf Backofen, Teresa M. Przytycka
RECOMB2
2014 AptaCluster - A Method to Cluster HT-SELEX Aptamer Pools and Lessons from Its Application
Jan Hoinka, Alexey Berezhnoy, Zuben E. Sauna, Eli Gilboa, Teresa M. Przytycka
RECOMB1
2012 Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers
abstract
MOTIVATION: Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process. RESULTS: To close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.
Jan Hoinka, Elena Zotenko, Adam Friedman, Zuben E. Sauna, Teresa M. Przytycka
Bioinform.1