Daniel Quang

2.9k total citations · 2 hit papers
13 papers, 1.7k citations indexed

About

Daniel Quang is a scholar working on Molecular Biology, Oncology and Surgery. According to data from OpenAlex, Daniel Quang has authored 13 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 2 papers in Oncology and 1 paper in Surgery. Recurrent topics in Daniel Quang's work include Genomics and Chromatin Dynamics (7 papers), Genomics and Phylogenetic Studies (6 papers) and RNA and protein synthesis mechanisms (3 papers). Daniel Quang is often cited by papers focused on Genomics and Chromatin Dynamics (7 papers), Genomics and Phylogenetic Studies (6 papers) and RNA and protein synthesis mechanisms (3 papers). Daniel Quang collaborates with scholars based in United States and China. Daniel Quang's co-authors include Xiaohui Xie, Yifei Chen, Yuanfang Guan, Stephen C.J. Parker, Hongyang Li, Tingyang Li, Dimitrios A. Pappas, Ziyan Wang, Fan Zhu and Joel M. Kremer and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Cancer Research.

In The Last Decade

Daniel Quang

13 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DANN: a deep learning approach for annotating the pathogenicity of genetic variants

2014 682 citations Daniel Quang, Yifei Chen et al. Bioinformatics profile →
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

2016 541 citations Daniel Quang, Xiaohui Xie Nucleic Acids Research profile →

Peers

Daniel Quang

MB

GENET

CR

AI

RHEUM

Hannah Tipney United States

Mulin Jun Li China

Wenwu Cui United States

Monika Ray United States

Žiga Avsec Germany

Pouya Khankhanian United States

Karthik A. Jagadeesh United States

Greg Schuler United States

Venkata Satagopam Luxembourg

Marcin Imieliński United States

Hannah Tipney United States

Daniel Quang

733 ×0.6

MB

416 ×1.0

GENET

80 ×0.5

CR

97 ×0.7

AI

64 ×0.7

RHEUM

Citations per year, relative to Daniel Quang Daniel Quang (= 1×) peers Hannah Tipney

Countries citing papers authored by Daniel Quang

Since Specialization

Citations

This map shows the geographic impact of Daniel Quang'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 Daniel Quang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Quang more than expected).

Fields of papers citing papers by Daniel Quang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Quang. 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 Daniel Quang. The network helps show where Daniel Quang may publish in the future.

Co-authorship network of co-authors of Daniel Quang

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Quang. A scholar is included among the top collaborators of Daniel Quang 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 Daniel Quang. Daniel Quang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Wu, Shaocheng, Hongyang Li, Daniel Quang, & Yuanfang Guan. (2020). Three-Plane–assembled Deep Learning Segmentation of Gliomas. Radiology Artificial Intelligence. 2(2). e190011–e190011. 18 indexed citations

2.

Quang, Daniel & Xiaohui Xie. (2019). FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Methods. 166. 40–47. 108 indexed citations

3.

Guan, Yuanfang, Hongjiu Zhang, Daniel Quang, et al.. (2019). Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers. Arthritis & Rheumatology. 71(12). 1987–1996. 94 indexed citations

4.

Rai, Vivek, Daniel Quang, Michael R. Erdos, et al.. (2019). Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Molecular Metabolism. 32. 109–121. 70 indexed citations

5.

Quang, Daniel, Yuanfang Guan, & Stephen C.J. Parker. (2018). YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU. Bioinformatics. 34(20). 3578–3580. 9 indexed citations

6.

Li, Tingyang, et al.. (2018). Network Propagation Predicts Drug Synergy in Cancers. Cancer Research. 78(18). 5446–5457. 62 indexed citations

7.

Li, Hongyang, Daniel Quang, & Yuanfang Guan. (2018). Anchor: trans-cell type prediction of transcription factor binding sites. Genome Research. 29(2). 281–292. 43 indexed citations

8.

Li, Yi, Daniel Quang, & Xiaohui Xie. (2017). Understanding Sequence Conservation With Deep Learning. 400–406. 5 indexed citations

9.

Quang, Daniel & Xiaohui Xie. (2016). DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research. 44(11). e107–e107. 541 indexed citations breakdown →

10.

Quang, Daniel, Michael R. Erdos, Stephen C.J. Parker, & Francis S. Collins. (2015). Motif signatures in stretch enhancers are enriched for disease-associated genetic variants. Epigenetics & Chromatin. 8(1). 23–23. 21 indexed citations

11.

Quang, Daniel & Xiaohui Xie. (2014). EXTREME: an online EM algorithm for motif discovery. Bioinformatics. 30(12). 1667–1673. 33 indexed citations

12.

Quang, Daniel, Yifei Chen, & Xiaohui Xie. (2014). DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 31(5). 761–763. 682 indexed citations breakdown →

13.

Khazanov, Nickolay A., Kelly L. Damm‐Ganamet, Daniel Quang, & Heather A. Carlson. (2012). Overcoming sequence misalignments with weighted structural superposition. Proteins Structure Function and Bioinformatics. 80(11). 2523–2535. 2 indexed citations

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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