Wiesław Paja

711 total citations
55 papers, 354 citations indexed

About

Wiesław Paja is a scholar working on Biophysics, Artificial Intelligence and Analytical Chemistry. According to data from OpenAlex, Wiesław Paja has authored 55 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Biophysics, 15 papers in Artificial Intelligence and 12 papers in Analytical Chemistry. Recurrent topics in Wiesław Paja's work include Spectroscopy Techniques in Biomedical and Chemical Research (21 papers), Spectroscopy and Chemometric Analyses (12 papers) and Rough Sets and Fuzzy Logic (10 papers). Wiesław Paja is often cited by papers focused on Spectroscopy Techniques in Biomedical and Chemical Research (21 papers), Spectroscopy and Chemometric Analyses (12 papers) and Rough Sets and Fuzzy Logic (10 papers). Wiesław Paja collaborates with scholars based in Poland, Türkiye and Romania. Wiesław Paja's co-authors include Joanna Depciuch, Krzysztof Pancerz, Zozan Güleken, Paweł Jakubczyk, Ruxandra Stoean, Cătălin Stoean, Huri Bulut, J. Cebulski, Nevzat Tarhan and Witold R. Rudnicki and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Analytical Biochemistry.

In The Last Decade

Wiesław Paja

43 papers receiving 347 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wiesław Paja Poland 9 126 62 61 60 37 55 354
Jeffrey L. Andrews Canada 12 103 0.8× 261 4.2× 79 1.3× 91 1.5× 15 0.4× 33 441
Yiming Zhou China 5 32 0.3× 141 2.3× 10 0.2× 104 1.7× 100 2.7× 5 464
Sabyasachi Mukhopadhyay India 10 40 0.3× 92 1.5× 54 0.9× 24 0.4× 63 1.7× 45 351
Ch. Madhu Babu India 5 29 0.2× 56 0.9× 8 0.1× 196 3.3× 24 0.6× 15 446
Christian Pellegrini Switzerland 12 27 0.2× 79 1.3× 25 0.4× 341 5.7× 20 0.5× 24 620
Matthew T. Moores Australia 8 31 0.2× 67 1.1× 17 0.3× 78 1.3× 23 0.6× 21 284
Ladislav Rampášek Canada 6 52 0.4× 71 1.1× 10 0.2× 257 4.3× 50 1.4× 8 532
Tian Bai China 9 13 0.1× 97 1.6× 14 0.2× 99 1.6× 28 0.8× 47 277
Xingye Qiao United States 9 6 0.0× 120 1.9× 14 0.2× 76 1.3× 19 0.5× 33 340

Countries citing papers authored by Wiesław Paja

Since Specialization
Citations

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

Fields of papers citing papers by Wiesław Paja

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wiesław Paja

This figure shows the co-authorship network connecting the top 25 collaborators of Wiesław Paja. A scholar is included among the top collaborators of Wiesław Paja 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 Wiesław Paja. Wiesław Paja 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.
Paja, Wiesław, et al.. (2025). Biochemical Heterogeneity of Endometriosis Phenotypes Revealed by FTIR Analysis. Journal of Biophotonics. 19(3). e202500511–e202500511.
3.
Mitura, Przemysław, et al.. (2025). Urine-based Raman markers for prostate cancer diagnosis: A machine learning approach using fingerprint and lipid spectral region. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 344(Pt 1). 126661–126661.
4.
Mitura, Przemysław, et al.. (2025). Investigation of importance Raman shifts in liquid biopsy diagnostics of prostate cancer. Scientific Reports. 15(1). 37602–37602.
5.
Mitura, Przemysław, et al.. (2025). FTIR Markers of Prostate Cancer Tissue and Their Correlation With Medical Parameters of Tumor Aggressiveness. Journal of Biophotonics. 18(9). e70046–e70046. 1 indexed citations
6.
Depciuch, Joanna, et al.. (2025). Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease. Nanomedicine Nanotechnology Biology and Medicine. 64. 102804–102804. 1 indexed citations
7.
Mitura, Przemysław, et al.. (2024). Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 327. 125305–125305. 1 indexed citations
8.
Kluz, Tomasz, Wiesław Paja, Edyta Barnaś, et al.. (2024). Determination of platinum-resistance of women with ovarian cancer by FTIR spectroscopy combined with multivariate analyses and machine learning methods. Scientific Reports. 14(1). 24923–24923. 4 indexed citations
9.
Mitura, Przemysław, et al.. (2024). Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine. Journal of Biophotonics. 18(1). e202400278–e202400278. 1 indexed citations
10.
Paja, Wiesław, et al.. (2024). Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses. Nanomedicine Nanotechnology Biology and Medicine. 62. 102788–102788.
11.
Güleken, Zozan, Paweł Jakubczyk, Wiesław Paja, et al.. (2023). An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker. Computer Methods and Programs in Biomedicine. 234. 107523–107523. 33 indexed citations
12.
Paja, Wiesław. (2023). Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges. Entropy. 25(8). 1223–1223. 1 indexed citations
13.
Güleken, Zozan, Huri Bulut, Krzysztof Pancerz, et al.. (2023). FTIR, RAMAN and biochemical tools to detect reveal of oxidative Stress-Related lipid and protein changes in fibromyalgia. Infrared Physics & Technology. 133. 104793–104793. 5 indexed citations
14.
Depciuch, Joanna, Paweł Jakubczyk, Wiesław Paja, et al.. (2023). Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer. Nanomedicine Nanotechnology Biology and Medicine. 48. 102657–102657. 10 indexed citations
15.
Güleken, Zozan, Paweł Jakubczyk, Wiesław Paja, et al.. (2021). Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications. Talanta. 237. 122916–122916. 33 indexed citations
16.
Stoean, Cătălin, et al.. (2019). Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. PLoS ONE. 14(10). e0223593–e0223593. 43 indexed citations
17.
Paja, Wiesław, et al.. (2016). Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models. Geoscientific model development. 9(3). 1065–1072. 6 indexed citations
18.
Pancerz, Krzysztof, et al.. (2012). Classification of Voice Signals through Mining Unique Episodes in Temporal Information Systems: A Rough Set Approach.. 280–291. 4 indexed citations
19.
Knap, Małgorzata, et al.. (2005). Infoscience technology: the impact of internet accessible melanoid data on health issues. Data Science Journal. 4. 77–81. 5 indexed citations
20.
Grzymala‐Busse, Jerzy W., et al.. (2003). Diagnosing skin melanoma: current versus future directions. SHILAP Revista de lepidopterología. 289–293. 4 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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