Armen Abnousi

1.9k total citations
19 papers, 502 citations indexed

About

Armen Abnousi is a scholar working on Molecular Biology, Plant Science and Artificial Intelligence. According to data from OpenAlex, Armen Abnousi has authored 19 papers receiving a total of 502 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 5 papers in Plant Science and 2 papers in Artificial Intelligence. Recurrent topics in Armen Abnousi's work include Genomics and Chromatin Dynamics (15 papers), RNA Research and Splicing (6 papers) and RNA modifications and cancer (4 papers). Armen Abnousi is often cited by papers focused on Genomics and Chromatin Dynamics (15 papers), RNA Research and Splicing (6 papers) and RNA modifications and cancer (4 papers). Armen Abnousi collaborates with scholars based in United States, China and Belgium. Armen Abnousi's co-authors include Ming Hu, Ivan Jurić, Bing Ren, Miao Yu, M. Margarita Behrens, Chenxu Zhu, Jacinta Lucero, Hui Huang, Rong Hu and Yun Li and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and SHILAP Revista de lepidopterología.

In The Last Decade

Armen Abnousi

19 papers receiving 498 citations

Peers

Countries citing papers authored by Armen Abnousi

Since Specialization

Citations

This map shows the geographic impact of Armen Abnousi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Armen Abnousi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Armen Abnousi more than expected).

Fields of papers citing papers by Armen Abnousi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Armen Abnousi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Armen Abnousi. The network helps show where Armen Abnousi may publish in the future.

Co-authorship network of co-authors of Armen Abnousi

This figure shows the co-authorship network connecting the top 25 collaborators of Armen Abnousi. A scholar is included among the top collaborators of Armen Abnousi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Armen Abnousi. Armen Abnousi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

#	Title	Journal	Authors	Indexed citations
1	Comparative characterization of human accelerated regions in neurons	Nature	Xiekui Cui, Han Yang et al.	5
2	Combinatorial transcription factor binding encodes cis-regulatory wiring of mouse forebrain GABAergic neurogenesis	Developmental Cell	Rinaldo Catta-Preta, Susan Lindtner et al.	4
3	HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets	Computational and Structural Biotechnology Journal	Jonathan D. Rosen, Lindsay Lee et al.	2
4	SnapHiC2: A computationally efficient loop caller for single cell Hi-C data	Computational and Structural Biotechnology Journal	Lindsay Lee, Armen Abnousi et al.	13
5	A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data	Briefings in Bioinformatics	Le Huang, Yuchen Yang et al.	4
6	SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data	Nature Methods	Miao Yu, Armen Abnousi et al.	49
7	Transcriptional network orchestrating regional patterning of cortical progenitors	Proceedings of the National Academy of Sciences	Athéna R. Ypsilanti, Kartik Pattabiraman et al.	27
8	Proximity Ligation-Assisted ChIP-Seq (PLAC-Seq)	Methods in molecular biology	Miao Yu, Ivan Jurić et al.	5
9	HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test	The American Journal of Human Genetics	Armen Abnousi, Ming Hu et al.	14
10	MUNIn: A statistical framework for identifying long-range chromatin interactions from multiple samples	SHILAP Revista de lepidopterología	Weifang Liu, Yuchen Yang et al.	2
11	HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets	Current Issues in Molecular Biology	Jonathan D. Rosen, Yuchen Yang et al.	3
12	Global analysis of histone modifications and long-range chromatin interactions revealed the differential cistrome changes and novel transcriptional players in human dilated cardiomyopathy	Journal of Molecular and Cellular Cardiology	Chia‐Feng Liu, Armen Abnousi et al.	11
13	FIREcaller: Detecting frequently interacting regions from Hi-C data	Computational and Structural Biotechnology Journal	Cheynna Crowley, Yuchen Yang et al.	14
14	MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments	PLoS Computational Biology	Ivan Jurić, Miao Yu et al.	66
15	An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome	Nature Structural & Molecular Biology	Chenxu Zhu, Miao Yu et al.	222
16	Sensory experience remodels genome architecture in neural circuit to drive motor learning	Nature	Tomoko Yamada, Yue Yang et al.	53
17	Alignment-free clustering of large data sets of unannotated protein conserved regions using minhashing	BMC Bioinformatics	Armen Abnousi, Shira L. Broschat et al.	2
18	A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions	PLoS ONE	Armen Abnousi, Shira L. Broschat et al.	3
19	An alignment-free approach to cluster proteins using frequency of conserved k-mers		Armen Abnousi, Shira L. Broschat et al.	3

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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