David K. Gifford

37.8k total citations · 9 hit papers
159 papers, 20.7k citations indexed

About

David K. Gifford is a scholar working on Molecular Biology, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, David K. Gifford has authored 159 papers receiving a total of 20.7k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Molecular Biology, 35 papers in Artificial Intelligence and 31 papers in Computer Networks and Communications. Recurrent topics in David K. Gifford's work include Genomics and Chromatin Dynamics (33 papers), Gene expression and cancer classification (22 papers) and Distributed systems and fault tolerance (19 papers). David K. Gifford is often cited by papers focused on Genomics and Chromatin Dynamics (33 papers), Gene expression and cancer classification (22 papers) and Distributed systems and fault tolerance (19 papers). David K. Gifford collaborates with scholars based in United States, France and Netherlands. David K. Gifford's co-authors include Richard A. Young, Tong Ihn Lee, Tommi Jaakkola, Stuart S. Levine, Laurie A. Boyer, Rudolf Jaenisch, Heather L. Murray, Julia Zeitlinger, Megan F. Cole and Douglas A. Melton and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

David K. Gifford

157 papers receiving 19.9k citations

Hit Papers

align trajectories sort by overperformance

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.

Core Transcriptional Regulatory Circuitry in Human Embryonic Stem Cells

2005 3.4k citations David K. Gifford, Richard A. Young et al. Cell profile →
Polycomb complexes repress developmental regulators in murine embryonic stem cells

2006 2.0k citations David K. Gifford, Richard A. Young et al. profile →
Transcriptional regulatory code of a eukaryotic genome

2004 1.7k citations David K. Gifford, Richard A. Young et al. profile →
Genome-wide Map of Nucleosome Acetylation and Methylation in Yeast

2005 1.1k citations David K. Gifford, Richard A. Young et al. Cell profile →
Control of Pancreas and Liver Gene Expression by HNF Transcription Factors

2004 1.1k citations David K. Gifford, Richard A. Young et al. profile →
Weighted voting for replicated data

1979 916 citations David K. Gifford profile →
Overcast: reliable multicasting with on overlay network

2000 710 citations John Jannotti, David K. Gifford et al. Operating Systems Design and Implementation profile →
Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle

2001 503 citations Itamar Simon, David K. Gifford et al. Cell profile →
Predictable and precise template-free CRISPR editing of pathogenic variants

2018 380 citations Max W. Shen, David K. Gifford et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David K. Gifford United States	58	14.5k	3.1k	2.3k	1.5k	1.4k	159	20.7k
Jianxin Wang China	69	10.3k 0.7×	2.6k 0.8×	630 0.3×	2.1k 1.4×	386 0.3×	1.1k	22.1k
Joel E. Richardson United States	35	24.3k 1.7×	687 0.2×	4.6k 2.0×	2.0k 1.3×	876 0.6×	81	34.7k
Eleazar Eskin United States	55	6.5k 0.5×	1.6k 0.5×	6.9k 3.0×	2.3k 1.5×	241 0.2×	227	15.7k
Ron Shamir Israel	62	10.7k 0.7×	624 0.2×	1.9k 0.8×	1.7k 1.1×	347 0.3×	296	15.1k
Temple F. Smith United States	50	12.5k 0.9×	528 0.2×	2.2k 1.0×	2.5k 1.7×	215 0.2×	152	16.6k
Hiroaki Kitano Japan	52	8.7k 0.6×	409 0.1×	1.0k 0.4×	1.6k 1.1×	262 0.2×	285	15.6k
Alan Colman Australia	55	7.4k 0.5×	572 0.2×	3.3k 1.4×	542 0.4×	1.5k 1.1×	235	11.0k
Utpal Banerjee United States	58	5.7k 0.4×	831 0.3×	587 0.3×	334 0.2×	232 0.2×	145	10.9k
Sridhar Ramaswamy United States	58	16.4k 1.1×	1.7k 0.6×	1.4k 0.6×	3.0k 2.0×	787 0.6×	141	28.9k
John A. Robinson Switzerland	57	5.5k 0.4×	526 0.2×	700 0.3×	2.8k 1.9×	825 0.6×	414	14.1k

Countries citing papers authored by David K. Gifford

Since Specialization

Citations

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

Fields of papers citing papers by David K. Gifford

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David K. Gifford. 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 David K. Gifford. The network helps show where David K. Gifford may publish in the future.

Co-authorship network of co-authors of David K. Gifford

This figure shows the co-authorship network connecting the top 25 collaborators of David K. Gifford. A scholar is included among the top collaborators of David K. Gifford 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 David K. Gifford. David K. Gifford 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:

20 of 20 papers shown

Dai, Zheng & David K. Gifford. (2023). Constrained Submodular Optimization for Vaccine Design. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 5045–5053. 1 indexed citations

Carter, Brandon, et al.. (2021). Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy. 3. 93–138.

Lin, Lin, Benjamin Holmes, Max W. Shen, et al.. (2020). Comprehensive Mapping of Key Regulatory Networks that Drive Oncogene Expression. Cell Reports. 33(8). 108426–108426. 11 indexed citations

Liu, Ge, Haoyang Zeng, Jonas Mueller, et al.. (2019). Antibody complementarity determining region design using high-capacity machine learning. Bioinformatics. 36(7). 2126–2133. 103 indexed citations

Sharon, Nadav, Raghav Chawla, Jonas Mueller, et al.. (2019). A Peninsular Structure Coordinates Asynchronous Differentiation with Morphogenesis to Generate Pancreatic Islets. Cell. 176(4). 790–804.e13. 97 indexed citations

Mueller, Jonas, David K. Gifford, & Tommi Jaakkola. (2017). Sequence to Better Sequence: Continuous Revision of Combinatorial Structures.. International Conference on Machine Learning. 2536–2544. 31 indexed citations

Hashimoto, Tatsunori, David K. Gifford, & Tommi Jaakkola. (2016). Learning population-level diffusions with generative recurrent networks. International Conference on Machine Learning. 2417–2426. 1 indexed citations

Kooshesh, Kameron, Budhaditya Banerjee, Richard I. Sherwood, et al.. (2016). High-throughput mapping of regulatory DNA. DSpace@MIT (Massachusetts Institute of Technology). 73 indexed citations

Rhee, Ho Sung, Michael Closser, Yuchun Guo, et al.. (2016). Expression of Terminal Effector Genes in Mammalian Neurons Is Maintained by a Dynamic Relay of Transient Enhancers. Neuron. 92(6). 1252–1265. 59 indexed citations

10.

Paggi, Joseph M., Yuchun Guo, Boris Zinshteyn, et al.. (2016). Identification of new branch points and unconventional introns in Saccharomyces cerevisiae. RNA. 22(10). 1522–1534. 27 indexed citations

11.

Hashimoto, Tatsunori, David K. Gifford, & Tommi Jaakkola. (2016). Learning Population-Level Diffusions with Generative RNNs. International Conference on Machine Learning. 2417–2426. 9 indexed citations

12.

Zeng, Haoyang, Tatsunori Hashimoto, Daniel Kang, & David K. Gifford. (2015). GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding. Bioinformatics. 32(4). 490–496. 30 indexed citations

13.

Sherwood, Richard I., Tatsunori Hashimoto, Colm P. O’Donnell, et al.. (2014). Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. PMC. 3 indexed citations

14.

Mazzoni, Esteban O., Shaun Mahony, Michael Closser, et al.. (2013). Synergistic binding of transcription factors to cell-specific enhancers programs motor neuron identity. Nature Neuroscience. 16(9). 1219–1227. 4 indexed citations

15.

Hashimoto, Tatsunori, Tommi Jaakkola, Richard I. Sherwood, et al.. (2012). Lineage-based identification of cellular states and expression programs. Bioinformatics. 28(12). i250–i257. 3 indexed citations

16.

Mazzoni, Esteban O., Shaun Mahony, Michelina Iacovino, et al.. (2011). Embryonic stem cell-based system for mapping developmental transcriptional programs. Europe PMC (PubMed Central). 58 indexed citations

17.

Dowell, Robin D., et al.. (2009). Toggle involving cis -interfering noncoding RNAs controls variegated gene expression in yeast. Proceedings of the National Academy of Sciences. 106(43). 18321–18326. 156 indexed citations

18.

Bar‐Joseph, Ziv, S. Farkash, David K. Gifford, Itamar Simon, & Roni Rosenfeld. (2004). Deconvolving cell cycle expression data with complementary information. Bioinformatics. 20(suppl_1). i23–i30. 41 indexed citations

19.

Jannotti, John, David K. Gifford, Kirk L. Johnson, M. Frans Kaashoek, & James W. O’Toole. (2000). Overcast: reliable multicasting with on overlay network. Operating Systems Design and Implementation. 14. 710 indexed citations breakdown →

20.

Gifford, David K., et al.. (1985). Coordinating Independent Atomic Actions.. 92–95. 24 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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