Sebastian Raschka

3.7k total citations · 2 hit papers
23 papers, 1.6k citations indexed

About

Sebastian Raschka is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Sebastian Raschka has authored 23 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 9 papers in Artificial Intelligence and 8 papers in Computational Theory and Mathematics. Recurrent topics in Sebastian Raschka's work include Computational Drug Discovery Methods (8 papers), Face recognition and analysis (6 papers) and Protein Structure and Dynamics (4 papers). Sebastian Raschka is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Face recognition and analysis (6 papers) and Protein Structure and Dynamics (4 papers). Sebastian Raschka collaborates with scholars based in United States. Sebastian Raschka's co-authors include Vahid Mirjalili, Wenzhi Cao, Arun Ross, Leslie A. Kuhn, Sang Jung Kim, Kaiping Chen, Shyam K. More, Dinesh Devadoss, Mar Huertas and Marc D. Basson and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Access and Proteins Structure Function and Bioinformatics.

In The Last Decade

Sebastian Raschka

22 papers receiving 1.5k citations

Hit Papers

Python Machine Learning 2015 2026 2018 2022 2015 2018 100 200 300 400
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. Python Machine Learning
    2015 421 citations Sebastian Raschka profile →
  2. MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack
    2018 419 citations Sebastian Raschka The Journal of Open Source Software profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sebastian Raschka United States 15 350 295 240 161 149 23 1.6k
Muhammad Iqbal Pakistan 18 460 1.3× 152 0.5× 419 1.7× 147 0.9× 84 0.6× 75 1.5k
Jiawei Luo China 15 720 2.1× 265 0.9× 435 1.8× 123 0.8× 198 1.3× 71 2.1k
Aritz Pérez Spain 15 718 2.1× 210 0.7× 433 1.8× 140 0.9× 115 0.8× 46 2.7k
Ernestina Menasalvas Spain 23 468 1.3× 194 0.7× 234 1.0× 115 0.7× 90 0.6× 131 1.6k
Sabri Boughorbel Qatar 15 496 1.4× 302 1.0× 231 1.0× 88 0.5× 80 0.5× 46 1.6k
Pedro Contreras Venezuela 9 386 1.1× 183 0.6× 131 0.5× 123 0.8× 71 0.5× 33 1.5k
Tzu-Tsung Wong Taiwan 12 593 1.7× 191 0.6× 207 0.9× 86 0.5× 78 0.5× 19 2.2k
Francisco F. Rivera Spain 14 525 1.5× 223 0.8× 121 0.5× 96 0.6× 123 0.8× 89 1.9k

Countries citing papers authored by Sebastian Raschka

Since Specialization
Citations

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

Fields of papers citing papers by Sebastian Raschka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sebastian Raschka

This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Raschka. A scholar is included among the top collaborators of Sebastian Raschka 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 Sebastian Raschka. Sebastian Raschka 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.
Chen, Jiaxing, Leslie A. Kuhn, & Sebastian Raschka. (2023). Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots. Methods in molecular biology. 2714. 235–268.
2.
Chen, Kaiping, et al.. (2022). Visual Framing of Science Conspiracy Videos. HOPE (Hauptbibliothek Open Publishing Environment) (University of Zurich). 4(1). 12 indexed citations
3.
Raschka, Sebastian, et al.. (2020). Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition. Methods. 180. 89–110. 63 indexed citations
4.
Raschka, Sebastian, et al.. (2020). Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition. Biomolecules. 10(3). 454–454. 24 indexed citations
5.
Mirjalili, Vahid, Sebastian Raschka, & Arun Ross. (2020). PrivacyNet: Semi-Adversarial Networks for Multi-Attribute Face Privacy. IEEE Transactions on Image Processing. 29. 9400–9412. 79 indexed citations
6.
Cao, Wenzhi, Vahid Mirjalili, & Sebastian Raschka. (2020). Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters. 140. 325–331. 134 indexed citations
7.
Cao, Wenzhi, Vahid Mirjalili, & Sebastian Raschka. (2019). Rank-consistent Ordinal Regression for Neural Networks. arXiv (Cornell University). 16 indexed citations
8.
Cao, Wenzhi, Vahid Mirjalili, & Sebastian Raschka. (2019). Rank Consistent Logits for Ordinal Regression with Convolutional Neural Networks. arXiv (Cornell University). 1 indexed citations
9.
Raschka, Sebastian. (2019). Automated discovery of GPCR bioactive ligands. Current Opinion in Structural Biology. 55. 17–24. 14 indexed citations
10.
Mirjalili, Vahid, Sebastian Raschka, & Arun Ross. (2019). FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers. IEEE Access. 7. 99735–99745. 33 indexed citations
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13.
Raschka, Sebastian. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. The Journal of Open Source Software. 3(24). 638–638. 419 indexed citations breakdown →
14.
Mirjalili, Vahid, Sebastian Raschka, & Arun Ross. (2018). Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers. 1–10. 32 indexed citations
15.
Raschka, Sebastian, et al.. (2018). Automated Inference of Chemical Discriminants of Biological Activity. Methods in molecular biology. 1762. 307–338. 5 indexed citations
16.
Raschka, Sebastian, Nan Liu, Santosh Gunturu, et al.. (2018). Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control. Journal of Computer-Aided Molecular Design. 32(3). 415–433. 8 indexed citations
17.
Raschka, Sebastian & Vahid Mirjalili. (2017). Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow. CERN Document Server (European Organization for Nuclear Research). 141 indexed citations
18.
Raschka, Sebastian. (2017). BioPandas: Working with molecular structures in pandas DataFrames. The Journal of Open Source Software. 2(14). 279–279. 42 indexed citations
19.
Raschka, Sebastian. (2015). Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics. CERN Document Server (European Organization for Nuclear Research). 31 indexed citations
20.
Raschka, Sebastian. (2015). Python Machine Learning. 421 indexed citations breakdown →

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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