Jack Lanchantin

4.3k total citations · 2 hit papers
12 papers, 881 citations indexed

About

Jack Lanchantin is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jack Lanchantin has authored 12 papers receiving a total of 881 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 4 papers in Artificial Intelligence and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jack Lanchantin's work include Genomics and Chromatin Dynamics (3 papers), Machine Learning in Bioinformatics (3 papers) and RNA and protein synthesis mechanisms (3 papers). Jack Lanchantin is often cited by papers focused on Genomics and Chromatin Dynamics (3 papers), Machine Learning in Bioinformatics (3 papers) and RNA and protein synthesis mechanisms (3 papers). Jack Lanchantin collaborates with scholars based in United States and Spain. Jack Lanchantin's co-authors include Yanjun Qi, Ji Gao, Mary Lou Soffa, Ritambhara Singh, Tianlu Wang, Vicente Ordóñez, Gabriel Robins, Beilun Wang, Zeming Lin and Sainbayar Sukhbaatar and has published in prestigious journals such as Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics and PubMed.

In The Last Decade

Jack Lanchantin

11 papers receiving 860 citations

Hit Papers

Black-Box Generation of Adversarial Text Sequences to Eva... 2018 2026 2020 2023 2018 2021 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. Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
    2018 357 citations Ji Gao, Jack Lanchantin et al. profile →
  2. General Multi-label Image Classification with Transformers
    2021 212 citations Jack Lanchantin, Tianlu Wang et al. profile →

Peers

Jack Lanchantin
Murtadha Ahmed China
Ameen Banjar Saudi Arabia
Yuyin Sun China
Mantao Xu China
Xiangzhong Fang China
Satish Chandra India
András Kocsor Hungary
Yasunori Endo Japan
Yunfan Li China
Murtadha Ahmed China
Jack Lanchantin
Citations per year, relative to Jack Lanchantin Jack Lanchantin (= 1×) peers Murtadha Ahmed

Countries citing papers authored by Jack Lanchantin

Since Specialization
Citations

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

Fields of papers citing papers by Jack Lanchantin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jack Lanchantin

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

All Works

12 of 12 papers shown
1.
Weston, Jason, et al.. (2024). TOOLVERIFIER: Generalization to New Tools via Self-Verification. 5026–5041. 1 indexed citations
2.
Dessì, Roberto, et al.. (2023). Robustness of Named-Entity Replacements for In-Context Learning. 10914–10931. 2 indexed citations
3.
Lanchantin, Jack, et al.. (2023). A Data Source for Reasoning Embodied Agents. Proceedings of the AAAI Conference on Artificial Intelligence. 37(7). 8438–8446. 5 indexed citations
4.
Lanchantin, Jack, et al.. (2023). Learning to Reason and Memorize with Self-Notes. 11891–11911.
5.
Lanchantin, Jack, Tianlu Wang, Vicente Ordóñez, & Yanjun Qi. (2021). General Multi-label Image Classification with Transformers. 16473–16483. 212 indexed citations breakdown →
6.
Lanchantin, Jack & Yanjun Qi. (2020). Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data. Bioinformatics. 36(Supplement_2). i659–i667. 11 indexed citations
7.
Singh, Ritambhara, et al.. (2020). FastSK: fast sequence analysis with gapped string kernels. Bioinformatics. 36(Supplement_2). i857–i865. 5 indexed citations
8.
Gao, Ji, Jack Lanchantin, Mary Lou Soffa, & Yanjun Qi. (2018). Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. 50–56. 357 indexed citations breakdown →
9.
Singh, Ritambhara, Jack Lanchantin, Gabriel Robins, & Yanjun Qi. (2016). Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16(5). 1524–1536. 2 indexed citations
10.
Singh, Ritambhara, Jack Lanchantin, Gabriel Robins, & Yanjun Qi. (2016). DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics. 32(17). i639–i648. 176 indexed citations
11.
Lanchantin, Jack, Ritambhara Singh, Beilun Wang, & Yanjun Qi. (2016). DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS. PubMed. 22. 254–265. 82 indexed citations
12.
Lin, Zeming, Jack Lanchantin, & Yanjun Qi. (2016). MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 28 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Murtadha Ahmed Breakdown of academic impact, for papers by Ameen Banjar Breakdown of academic impact, for papers by Yuyin Sun Breakdown of academic impact, for papers by Mantao Xu Breakdown of academic impact, for papers by Xiangzhong Fang Breakdown of academic impact, for papers by Satish Chandra Breakdown of academic impact, for papers by András Kocsor Breakdown of academic impact, for papers by Yasunori Endo Breakdown of academic impact, for papers by Yunfan Li
Rankless by CCL
2026