Amarda Shehu

3.4k total citations · 1 hit paper
149 papers, 2.3k citations indexed

About

Amarda Shehu is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Amarda Shehu has authored 149 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 134 papers in Molecular Biology, 51 papers in Materials Chemistry and 25 papers in Computational Theory and Mathematics. Recurrent topics in Amarda Shehu's work include Protein Structure and Dynamics (104 papers), Enzyme Structure and Function (41 papers) and Machine Learning in Bioinformatics (35 papers). Amarda Shehu is often cited by papers focused on Protein Structure and Dynamics (104 papers), Enzyme Structure and Function (41 papers) and Machine Learning in Bioinformatics (35 papers). Amarda Shehu collaborates with scholars based in United States, Israel and Spain. Amarda Shehu's co-authors include Uday Kamath, Daniel Veltri, Brian Olson, Lydia E. Kavraki, Kevin Molloy, Cecilia Clementi, Kenneth De Jong, Ruth Nussinov, Buyong Ma and Erion Plaku and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Bioinformatics.

In The Last Decade

Amarda Shehu

141 papers receiving 2.2k citations

Hit Papers

Deep learning improves antimicrobial peptide recognition 2018 2026 2020 2023 2018 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. Deep learning improves antimicrobial peptide recognition
    2018 367 citations Daniel Veltri, Amarda Shehu et al. Bioinformatics profile →

Peers

Amarda Shehu
Gianluca Pollastri Ireland
Séan O’Donoghue Australia
Alexander Rives United States
Sándor Pongor Italy
Tom Sercu United States
Paul Wrede Germany
Thérèse E. Malliavin France
Shandar Ahmad Japan
Brian Hie United States
Salvatore Candido United States
Gianluca Pollastri Ireland
Amarda Shehu
Citations per year, relative to Amarda Shehu Amarda Shehu (= 1×) peers Gianluca Pollastri

Countries citing papers authored by Amarda Shehu

Since Specialization
Citations

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

Fields of papers citing papers by Amarda Shehu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amarda Shehu

This figure shows the co-authorship network connecting the top 25 collaborators of Amarda Shehu. A scholar is included among the top collaborators of Amarda Shehu 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 Amarda Shehu. Amarda Shehu 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.
Zhao, Zeyi, Arthur S. H. Wu, William M. Wuest, et al.. (2025). An Instructible Chemist-AI Alignment Framework for Generating Quaternary Ammonium Compound Structures. 4740–4750.
2.
Singh, Jai, et al.. (2025). The ethics of national artificial intelligence plans: an empirical lens. AI and Ethics. 5(4). 3803–3831. 1 indexed citations
3.
Shehu, Amarda, et al.. (2024). Revisiting Evolutionary Algorithms for Optimization for Deep Learning: Introducing DL-HEA. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 435–438.
4.
Bromberg, Yana, et al.. (2024). Variant Effect Prediction in the Age of Machine Learning. Cold Spring Harbor Perspectives in Biology. 16(7). a041467–a041467. 7 indexed citations
5.
Bromberg, Yana, et al.. (2024). In the twilight zone of protein sequence homology: do protein language models learn protein structure?. Bioinformatics Advances. 4(1). vbae119–vbae119. 3 indexed citations
6.
Bhattarai, Manish, K. Ø. Rasmussen, Amarda Shehu, et al.. (2023). Examining DNA breathing with pyDNA-EPBD. Bioinformatics. 39(11). 3 indexed citations
7.
Jong, Kenneth De, et al.. (2022). Guiding Protein Conformation Sampling with Conformation Space Maps. EPiC series in computing. 83. 20–8. 1 indexed citations
8.
Shehu, Amarda, et al.. (2022). Analysis of AlphaFold2 for Modeling Structures of Wildtype and Variant Protein Sequences. EPiC series in computing. 83. 53–39. 3 indexed citations
9.
Ma, Buyong, et al.. (2022). Fewer Dimensions, More Structures for Improved Discrete Models of Dynamics of Free versus Antigen-Bound Antibody. Biomolecules. 12(7). 1011–1011. 2 indexed citations
10.
Jong, Kenneth De, et al.. (2021). Adaptive Stochastic Optimization to Improve Protein Conformation Sampling. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(5). 2759–2771. 2 indexed citations
11.
Rajabi, Zahra, Amarda Shehu, & Özlem Uzuner. (2020). A Multi-channel BiLSTM-CNN Model for Multilabel Emotion Classification of Informal Text. 303–306. 27 indexed citations
12.
Shehu, Amarda, et al.. (2020). From Unsupervised Multi-Instance Learning to Identification of Near-Native Protein Structures. EPiC series in computing. 70. 59–48. 2 indexed citations
13.
Barbará, Daniel, et al.. (2020). Anomaly Detection-Based Recognition of Near-Native Protein Structures. IEEE Transactions on NanoBioscience. 19(3). 562–570. 2 indexed citations
14.
Barbará, Daniel, et al.. (2020). Exploring Deep Neural Network Architectures: A Case Study on Improving Antimicrobial Peptide Recognition. EPiC series in computing. 3 indexed citations
15.
Shehu, Amarda, et al.. (2019). Connecting Molecular Energy Landscape Analysis with Markov Model-based Analysis of Equilibrium Structural Dynamics. EPiC series in computing. 60. 181–171. 3 indexed citations
16.
Ma, Buyong, et al.. (2016). Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Computational Biology. 12(4). e1004619–e1004619. 160 indexed citations
17.
Ma, Buyong, et al.. (2015). Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm. PLoS Computational Biology. 11(9). e1004470–e1004470. 46 indexed citations
18.
Nordman, Jacob C., Daniel Veltri, Kun Yang, et al.. (2013). Menthol Inhibits 5-HT3 Receptor–Mediated Currents.
19.
Shehu, Amarda, Lydia E. Kavraki, & Cecilia Clementi. (2008). Unfolding the fold of cyclic cysteine‐rich peptides. Protein Science. 17(3). 482–493. 18 indexed citations
20.
Shehu, Amarda, Lydia E. Kavraki, & Cecilia Clementi. (2006). On the Characterization of Protein Native State Ensembles. Biophysical Journal. 92(5). 1503–1511. 32 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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