Savvas Kinalis

758 total citations
16 papers, 465 citations indexed

About

Savvas Kinalis is a scholar working on Molecular Biology, Pathology and Forensic Medicine and Cancer Research. According to data from OpenAlex, Savvas Kinalis has authored 16 papers receiving a total of 465 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Pathology and Forensic Medicine and 7 papers in Cancer Research. Recurrent topics in Savvas Kinalis's work include Cancer Genomics and Diagnostics (5 papers), Single-cell and spatial transcriptomics (3 papers) and Acute Myeloid Leukemia Research (2 papers). Savvas Kinalis is often cited by papers focused on Cancer Genomics and Diagnostics (5 papers), Single-cell and spatial transcriptomics (3 papers) and Acute Myeloid Leukemia Research (2 papers). Savvas Kinalis collaborates with scholars based in Denmark, Switzerland and Sweden. Savvas Kinalis's co-authors include Frederik Otzen Bagger, Nicolas Rapin, Finn Cilius Nielsen, Christina W. Yde, Oľga Østrup, Maria Rossing, Ole Winther, Miglė Gabrielaitė, Ulrik Lassen and Benjamin H. Durham and has published in prestigious journals such as Nucleic Acids Research, Blood and Clinical Cancer Research.

In The Last Decade

Savvas Kinalis

16 papers receiving 462 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Savvas Kinalis Denmark 11 238 155 125 90 88 16 465
Nicola D. Roberts United Kingdom 7 238 1.0× 163 1.1× 195 1.6× 86 1.0× 80 0.9× 7 554
Cristina Largo Spain 9 268 1.1× 119 0.8× 99 0.8× 91 1.0× 94 1.1× 12 481
Hélène Dreau United Kingdom 11 153 0.6× 103 0.7× 125 1.0× 64 0.7× 91 1.0× 26 495
Rizi Ai United States 12 332 1.4× 85 0.5× 126 1.0× 79 0.9× 43 0.5× 17 587
Romina Royo Spain 8 386 1.6× 55 0.4× 159 1.3× 49 0.5× 70 0.8× 15 522
Manuela Puopolo Italy 9 272 1.1× 70 0.5× 120 1.0× 164 1.8× 95 1.1× 11 521
Kevin A. Link United States 11 493 2.1× 247 1.6× 80 0.6× 79 0.9× 100 1.1× 18 650
Christopher F. Bassil United States 7 602 2.5× 196 1.3× 109 0.9× 121 1.3× 47 0.5× 11 754
Koorosh Korfi United Kingdom 8 228 1.0× 241 1.6× 81 0.6× 148 1.6× 32 0.4× 18 549
Armon Azizi United States 8 267 1.1× 84 0.5× 116 0.9× 182 2.0× 25 0.3× 16 487

Countries citing papers authored by Savvas Kinalis

Since Specialization
Citations

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

Fields of papers citing papers by Savvas Kinalis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Savvas Kinalis

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

All Works

16 of 16 papers shown
1.
Benada, Jan, Savvas Kinalis, Bent Ejlertsen, et al.. (2021). A catalog of curated breast cancer genes. Breast Cancer Research and Treatment. 191(2). 431–441. 6 indexed citations
2.
Gabrielaitė, Miglė, Malthe Sebro Rasmussen, Sergio Andreu‐Sánchez, et al.. (2021). A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data. Cancers. 13(24). 6283–6283. 55 indexed citations
3.
Kinalis, Savvas, et al.. (2021). Interpretable Autoencoders Trained on Single Cell Sequencing Data Can Transfer Directly to Data from Unseen Tissues. Cells. 11(1). 85–85. 3 indexed citations
4.
Brieghel, Christian, Caspar da Cunha‐Bang, Christina W. Yde, et al.. (2020). The Number of Signaling Pathways Altered by Driver Mutations in Chronic Lymphocytic Leukemia Impacts Disease Outcome. Clinical Cancer Research. 26(6). 1507–1515. 13 indexed citations
5.
Yde, Christina W., Oľga Østrup, Signe Regner Michaelsen, et al.. (2020). Genomic profiling of newly diagnosed glioblastoma patients and its potential for clinical utility – a prospective, translational study. Molecular Oncology. 14(11). 2727–2743. 15 indexed citations
6.
Loizou, Evangelia, Ana Banito, Geulah Livshits, et al.. (2019). A Gain-of-Function p53-Mutant Oncogene Promotes Cell Fate Plasticity and Myeloid Leukemia through the Pluripotency Factor FOXH1. Cancer Discovery. 9(7). 962–979. 59 indexed citations
7.
Kinalis, Savvas, Finn Cilius Nielsen, Ole Winther, & Frederik Otzen Bagger. (2019). Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data. BMC Bioinformatics. 20(1). 379–379. 20 indexed citations
8.
Bertelsen, Birgitte, Christina W. Yde, Miglė Gabrielaitė, et al.. (2019). High frequency of pathogenic germline variants within homologous recombination repair in patients with advanced cancer. npj Genomic Medicine. 4(1). 13–13. 54 indexed citations
9.
Brieghel, Christian, Savvas Kinalis, Christina W. Yde, et al.. (2018). Deep targeted sequencing of TP53 in chronic lymphocytic leukemia: clinical impact at diagnosis and at time of treatment. Haematologica. 104(4). 789–796. 15 indexed citations
10.
Bagger, Frederik Otzen, Savvas Kinalis, & Nicolas Rapin. (2018). BloodSpot: a database of healthy and malignant haematopoiesis updated with purified and single cell mRNA sequencing profiles. Nucleic Acids Research. 47(D1). D881–D885. 150 indexed citations
11.
Ahlborn, Lise Barlebo, Florent Moulière, Savvas Kinalis, et al.. (2018). Circulating tumor DNA as a marker of treatment response in BRAF V600E mutated non-melanoma solid tumors. Oncotarget. 9(66). 32570–32579. 14 indexed citations
12.
Skjøth‐Rasmussen, Jane, Jannick Brennum, Oľga Østrup, et al.. (2017). GENE-50. GENOMIC PROFILING AND PRECISION MEDICINE IN GLIOBLASTOMA - A PROSPECTIVE STUDY. Neuro-Oncology. 19(suppl_6). vi103–vi103. 2 indexed citations
13.
Leinøe, Eva, Eva Zetterberg, Savvas Kinalis, et al.. (2017). Application of whole‐exome sequencing to direct the specific functional testing and diagnosis of rare inherited bleeding disorders in patients from the Öresund Region, Scandinavia. British Journal of Haematology. 179(2). 308–322. 44 indexed citations
14.
Rossing, Maria, Oľga Østrup, Savvas Kinalis, et al.. (2017). Molecular subtyping of breast cancer improves identification of both high and low risk patients. Acta Oncologica. 57(1). 58–66. 13 indexed citations
15.
Kinalis, Savvas, Finn Cilius Nielsen, Maj‐Lis Talman, Bent Ejlertsen, & Maria Rossing. (2017). Characterization of basal-like subtype in a Danish consecutive primary breast cancer cohort. Acta Oncologica. 57(1). 51–57. 1 indexed citations
16.
Leinøe, Eva, Eva Zetterberg, Peter Kampmann, et al.. (2016). Targeted Whole Exome Sequencing of 87 Predisposition Genes in 156 Patients from the Oresund Region with Bleeding Disorders. Blood. 128(22). 361–361. 1 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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