Jacob Schreiber

3.4k total citations · 1 hit paper
22 papers, 1.2k citations indexed

About

Jacob Schreiber is a scholar working on Molecular Biology, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Jacob Schreiber has authored 22 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 4 papers in Biomedical Engineering and 3 papers in Artificial Intelligence. Recurrent topics in Jacob Schreiber's work include Genomics and Chromatin Dynamics (10 papers), Epigenetics and DNA Methylation (6 papers) and RNA modifications and cancer (5 papers). Jacob Schreiber is often cited by papers focused on Genomics and Chromatin Dynamics (10 papers), Epigenetics and DNA Methylation (6 papers) and RNA modifications and cancer (5 papers). Jacob Schreiber collaborates with scholars based in United States, Austria and Germany. Jacob Schreiber's co-authors include William Stafford Noble, Mark Akeson, Jay Shendure, Sean Whalen, Katherine S. Pollard, Kevin Karplus, Jeffrey A. Bilmes, Seungsoo Kim, Anh Leith and José L. McFaline‐Figueroa and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Jacob Schreiber

21 papers receiving 1.2k citations

Hit Papers

A Genome-wide Framework for Mapping Gene Regulation via C... 2019 2026 2021 2023 2019 100 200 300
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.

  1. A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
    2019 370 citations Molly Gasperini, Andrew J. Hill et al. Cell profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jacob Schreiber United States 12 1.0k 228 174 78 77 22 1.2k
Christoph Zechner Germany 15 1.2k 1.2× 141 0.6× 136 0.8× 56 0.7× 103 1.3× 41 1.4k
Yong Fuga Li United States 10 565 0.6× 158 0.7× 105 0.6× 54 0.7× 27 0.4× 15 1.1k
Philip C. Zuzarte Canada 6 730 0.7× 97 0.4× 120 0.7× 112 1.4× 123 1.6× 8 842
Vincent Gardeux Switzerland 17 768 0.8× 58 0.3× 164 0.9× 130 1.7× 61 0.8× 43 1.2k
Guoliang Yu China 9 739 0.7× 51 0.2× 114 0.7× 96 1.2× 169 2.2× 13 920
Lucia Marucci United Kingdom 19 805 0.8× 203 0.9× 138 0.8× 22 0.3× 40 0.5× 51 1.0k
Miriam V. Gutschow United States 10 954 1.0× 141 0.6× 202 1.2× 79 1.0× 51 0.7× 12 1.3k
Haoyang Zeng United States 12 758 0.8× 22 0.1× 155 0.9× 62 0.8× 63 0.8× 24 976

Countries citing papers authored by Jacob Schreiber

Since Specialization
Citations

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

Fields of papers citing papers by Jacob Schreiber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jacob Schreiber

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

All Works

20 of 20 papers shown
1.
2.
Zhang, Ran, Jacob Schreiber, Diana R. O’Day, et al.. (2025). Cross-species imputation and comparison of single-cell transcriptomic profiles. Genome biology. 26(1). 40–40. 2 indexed citations
3.
Baur, Brittany, Junha Shin, Jacob Schreiber, et al.. (2023). Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation. PLoS Computational Biology. 19(7). e1011286–e1011286. 1 indexed citations
4.
Nair, Surag, Avanti Shrikumar, Jacob Schreiber, & Anshul Kundaje. (2022). fastISM: performant in silico saturation mutagenesis for convolutional neural networks. Bioinformatics. 38(9). 2397–2403. 14 indexed citations
5.
Schreiber, Jacob, Surag Nair, Akshay Balsubramani, & Anshul Kundaje. (2022). Accelerating in silico saturation mutagenesis using compressed sensing. Bioinformatics. 38(14). 3557–3564. 4 indexed citations
6.
Whalen, Sean, Jacob Schreiber, William Stafford Noble, & Katherine S. Pollard. (2021). Navigating the pitfalls of applying machine learning in genomics. Nature Reviews Genetics. 23(3). 169–181. 137 indexed citations
7.
Schreiber, Jacob & Ritambhara Singh. (2021). Machine learning for profile prediction in genomics. Current Opinion in Chemical Biology. 65. 35–41. 9 indexed citations
8.
Schreiber, Jacob, Jeffrey A. Bilmes, & William Stafford Noble. (2020). Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data. Bioinformatics. 37(4). 439–447. 2 indexed citations
9.
Schreiber, Jacob, Timothy Durham, Jeffrey A. Bilmes, & William Stafford Noble. (2020). Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome. Genome biology. 21(1). 63 indexed citations
10.
Schreiber, Jacob, Jeffrey A. Bilmes, & William Stafford Noble. (2020). apricot: Submodular selection for data summarization in Python. arXiv (Cornell University). 21(161). 1–6. 1 indexed citations
11.
Schreiber, Jacob, Jeffrey A. Bilmes, & William Stafford Noble. (2020). Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples. Genome biology. 21(1). 82–82. 22 indexed citations
12.
Schreiber, Jacob, Ritambhara Singh, Jeffrey A. Bilmes, & William Stafford Noble. (2020). A pitfall for machine learning methods aiming to predict across cell types. Genome biology. 21(1). 282–282. 32 indexed citations
13.
Erijman, Ariel, Łukasz Kozłowski, Salma Sohrabi-Jahromi, et al.. (2020). A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning. Molecular Cell. 78(5). 890–902.e6. 81 indexed citations
14.
Schreiber, Jacob, Timothy Durham, William Stafford Noble, & Jeffrey A. Bilmes. (2020). Avocado. 1–1. 1 indexed citations
15.
Chen, Wei, Aaron McKenna, Jacob Schreiber, et al.. (2019). Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair. Nucleic Acids Research. 47(15). 7989–8003. 129 indexed citations
16.
Gasperini, Molly, Andrew J. Hill, José L. McFaline‐Figueroa, et al.. (2019). A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens. Cell. 176(1-2). 377–390.e19. 370 indexed citations breakdown →
17.
Schreiber, Jacob & William Stafford Noble. (2017). Finding the optimal Bayesian network given a constraint graph. PeerJ Computer Science. 3. e122–e122. 7 indexed citations
18.
Schreiber, Jacob & Kevin Karplus. (2015). Analysis of nanopore data using hidden Markov models. Bioinformatics. 31(12). 1897–1903. 25 indexed citations
19.
Schreiber, Jacob, et al.. (2014). Nanopores Discriminate among Five C5-Cytosine Variants in DNA. Journal of the American Chemical Society. 136(47). 16582–16587. 87 indexed citations
20.
Schreiber, Jacob, et al.. (2013). Error rates for nanopore discrimination among cytosine, methylcytosine, and hydroxymethylcytosine along individual DNA strands. Proceedings of the National Academy of Sciences. 110(47). 18910–18915. 136 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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