Jack Hanson

2.4k total citations
15 papers, 1.2k citations indexed

About

Jack Hanson is a scholar working on Molecular Biology, Materials Chemistry and Pharmaceutical Science. According to data from OpenAlex, Jack Hanson has authored 15 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 5 papers in Materials Chemistry and 1 paper in Pharmaceutical Science. Recurrent topics in Jack Hanson's work include Protein Structure and Dynamics (11 papers), Machine Learning in Bioinformatics (9 papers) and RNA and protein synthesis mechanisms (7 papers). Jack Hanson is often cited by papers focused on Protein Structure and Dynamics (11 papers), Machine Learning in Bioinformatics (9 papers) and RNA and protein synthesis mechanisms (7 papers). Jack Hanson collaborates with scholars based in Australia, China and United States. Jack Hanson's co-authors include Kuldip K. Paliwal, Yaoqi Zhou, Yuedong Yang, Thomas Litfin, Jaswinder Singh, Rhys Heffernan, Jihua Wang, Jianzhao Gao, Lukasz Kurgan and Akila Katuwawala and has published in prestigious journals such as Nature Communications, Bioinformatics and Journal of Molecular Biology.

In The Last Decade

Jack Hanson

14 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jack Hanson Australia	12	1.1k	265	143	39	33	15	1.2k
Julia Koehler Leman United States	16	869 0.8×	168 0.6×	148 1.0×	32 0.8×	49 1.5×	25	1.1k
Rhys Heffernan Australia	14	1.4k 1.3×	286 1.1×	273 1.9×	43 1.1×	29 0.9×	15	1.5k
David La United States	14	571 0.5×	169 0.6×	131 0.9×	10 0.3×	24 0.7×	19	720
James Lyons Australia	22	1.5k 1.4×	196 0.7×	282 2.0×	122 3.1×	26 0.8×	39	1.8k
Badri Adhikari United States	17	841 0.8×	293 1.1×	143 1.0×	26 0.7×	40 1.2×	31	956
Pietro Di Lena Italy	16	540 0.5×	136 0.5×	127 0.9×	65 1.7×	46 1.4×	40	782
Daniel Russel United States	14	871 0.8×	246 0.9×	47 0.3×	15 0.4×	65 2.0×	21	1.4k
James Bradford United Kingdom	18	977 0.9×	84 0.3×	167 1.2×	76 1.9×	104 3.2×	34	1.3k
Ahmed Elnaggar Germany	7	1.3k 1.2×	101 0.4×	255 1.8×	114 2.9×	49 1.5×	8	1.5k
Roland A. Pache Spain	12	580 0.5×	104 0.4×	80 0.6×	9 0.2×	39 1.2×	19	703

Countries citing papers authored by Jack Hanson

Since Specialization

Citations

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

Fields of papers citing papers by Jack Hanson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jack Hanson

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

All Works

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15 of 15 papers shown

Dodge, Anthony G., et al.. (2025). Continuous Spectrophotometric Assay for Defluorinase and Dechlorinase Activities With α‐Halocarboxylic Acids. Microbial Biotechnology. 18(8). e70216–e70216.

Cai, Yufeng, Zhe Sun, Yutong Lu, et al.. (2019). SPOT‐Fold: Fragment‐Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map. Journal of Computational Chemistry. 41(8). 745–750. 9 indexed citations

Hanson, Jack, Kuldip K. Paliwal, Thomas Litfin, Yuedong Yang, & Yaoqi Zhou. (2019). Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning. Journal of Computational Biology. 27(5). 796–814. 15 indexed citations

Katuwawala, Akila, et al.. (2019). DEPICTER: Intrinsic Disorder and Disorder Function Prediction Server. Journal of Molecular Biology. 432(11). 3379–3387. 48 indexed citations

Singh, Jaswinder, Jack Hanson, Kuldip K. Paliwal, & Yaoqi Zhou. (2019). RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nature Communications. 10(1). 5407–5407. 224 indexed citations

Hanson, Jack, Thomas Litfin, Kuldip K. Paliwal, & Yaoqi Zhou. (2019). Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning. Bioinformatics. 36(4). 1107–1113. 38 indexed citations

Hanson, Jack, Kuldip K. Paliwal, Thomas Litfin, & Yaoqi Zhou. (2019). SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning. Genomics Proteomics & Bioinformatics. 17(6). 645–656. 105 indexed citations

Nicolson, Aaron, Jack Hanson, James Lyons, & Kuldip K. Paliwal. (2018). Spectral Subband Centroids for Robust Speaker Identification Using Marginalization-based Missing Feature Theory. Griffith Research Online (Griffith University, Queensland, Australia). 6(1). 12–16. 5 indexed citations

Hanson, Jack, Kuldip K. Paliwal, & Yaoqi Zhou. (2018). Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures. Journal of Chemical Information and Modeling. 58(11). 2369–2376. 57 indexed citations

10.

Li, Zhixiu, Jack Hanson, Rhys Heffernan, et al.. (2018). SPIN2: Predicting sequence profiles from protein structures using deep neural networks. Proteins Structure Function and Bioinformatics. 86(6). 629–633. 52 indexed citations

11.

Singh, Jaswinder, Jack Hanson, Rhys Heffernan, et al.. (2018). Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning. Journal of Chemical Information and Modeling. 58(9). 2033–2042. 13 indexed citations

12.

Hanson, Jack, Kuldip K. Paliwal, Thomas Litfin, Yuedong Yang, & Yaoqi Zhou. (2018). Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks. Bioinformatics. 35(14). 2403–2410. 139 indexed citations

13.

Hanson, Jack, Kuldip K. Paliwal, Thomas Litfin, Yuedong Yang, & Yaoqi Zhou. (2018). Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks. Bioinformatics. 34(23). 4039–4045. 137 indexed citations

14.

Yang, Yuedong, Jianzhao Gao, Jihua Wang, et al.. (2016). Sixty-five years of the long march in protein secondary structure prediction: the final stretch?. Briefings in Bioinformatics. 19(3). bbw129–bbw129. 171 indexed citations

15.

Hanson, Jack, Yuedong Yang, Kuldip K. Paliwal, & Yaoqi Zhou. (2016). Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks. Bioinformatics. 33(5). 685–692. 216 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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