Kenneth Langlands

1.3k total citations
39 papers, 977 citations indexed

About

Kenneth Langlands is a scholar working on Molecular Biology, Public Health, Environmental and Occupational Health and Hematology. According to data from OpenAlex, Kenneth Langlands has authored 39 papers receiving a total of 977 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 12 papers in Public Health, Environmental and Occupational Health and 8 papers in Hematology. Recurrent topics in Kenneth Langlands's work include Acute Lymphoblastic Leukemia research (10 papers), Gene expression and cancer classification (5 papers) and Acute Myeloid Leukemia Research (5 papers). Kenneth Langlands is often cited by papers focused on Acute Lymphoblastic Leukemia research (10 papers), Gene expression and cancer classification (5 papers) and Acute Myeloid Leukemia Research (5 papers). Kenneth Langlands collaborates with scholars based in United Kingdom, United States and Ireland. Kenneth Langlands's co-authors include Terence Kealey, Edward V. Prochownik, Geetha Anand, Xiaoying Yin, Colin G. Steward, A Oakhill, Sabah Jassim, Michael A. Cawthorne, Michael Potter and F Katz and has published in prestigious journals such as Journal of Biological Chemistry, Blood and Analytical Biochemistry.

In The Last Decade

Kenneth Langlands

36 papers receiving 953 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kenneth Langlands United Kingdom 16 413 289 244 127 91 39 977
Seiji Kojima Japan 20 308 0.7× 414 1.4× 145 0.6× 213 1.7× 220 2.4× 40 1.1k
Shuwei Jiang United States 15 439 1.1× 151 0.5× 68 0.3× 211 1.7× 284 3.1× 18 1.1k
Alessandra Marini Italy 16 437 1.1× 90 0.3× 60 0.2× 268 2.1× 172 1.9× 56 1.0k
A. A. Sandberg United States 18 471 1.1× 209 0.7× 182 0.7× 337 2.7× 125 1.4× 26 1.3k
Mara Dominis Croatia 15 541 1.3× 88 0.3× 63 0.3× 183 1.4× 142 1.6× 65 1.1k
Catherine M. Sawai United States 13 632 1.5× 398 1.4× 211 0.9× 266 2.1× 768 8.4× 19 1.6k
Anastasia N. Tikhonova United States 15 328 0.8× 320 1.1× 73 0.3× 222 1.7× 547 6.0× 20 1.1k
S Sekiguchi Japan 17 300 0.7× 354 1.2× 26 0.1× 81 0.6× 167 1.8× 51 935
A. Knopp Germany 12 326 0.8× 162 0.6× 92 0.4× 77 0.6× 31 0.3× 15 660
Yang Du United States 18 473 1.1× 149 0.5× 43 0.2× 174 1.4× 127 1.4× 37 879

Countries citing papers authored by Kenneth Langlands

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth Langlands

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kenneth Langlands

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth Langlands. A scholar is included among the top collaborators of Kenneth Langlands 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 Kenneth Langlands. Kenneth Langlands 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.
Langlands, Kenneth, et al.. (2025). Pharmacogenomic-guided opioid therapy for pain: a systematic review and meta-analysis of randomised controlled trials. The Pharmacogenomics Journal. 25(4). 20–20.
2.
Chu, Anthony C., et al.. (2017). Genetic homogeneity of adult Langerhans cell histiocytosis lesions: Insights from BRAFV600E mutations in adult populations. Oncology Letters. 14(4). 4449–4454. 9 indexed citations
3.
Du, Hongbo, et al.. (2014). Automatic Ribs Segmentation and Counting From Mouse X-ray Images.. 143–148. 1 indexed citations
4.
Ibrahim, M, Sabah Jassim, Michael A. Cawthorne, & Kenneth Langlands. (2014). A MATLAB tool for pathway enrichment using a topology-based pathway regulation score. BMC Bioinformatics. 15(1). 358–358. 6 indexed citations
5.
Kępczyńska, Małgorzata A., Claire J. Stocker, Jacqueline O’Dowd, et al.. (2013). A novel automated image analysis method for accurate adipocyte quantification. Adipocyte. 2(3). 160–164. 58 indexed citations
6.
Kealey, Terence, et al.. (2013). Toll-like receptor 2 activation and comedogenesis: implications for the pathogenesis of acne. BMC Dermatology. 13(1). 10–10. 65 indexed citations
7.
Stocker, Claire J., et al.. (2013). A novel method to assess collagen architecture in skin. BMC Bioinformatics. 14(1). 260–260. 56 indexed citations
8.
Ibrahim, M, Sabah Jassim, Michael A. Cawthorne, & Kenneth Langlands. (2012). A Topology-Based Score for Pathway Enrichment. Journal of Computational Biology. 19(5). 563–573. 33 indexed citations
9.
Ibrahim, M, Sabah Jassim, Michael A. Cawthorne, & Kenneth Langlands. (2011). Pathway-based gene selection for disease classification. 360–365. 3 indexed citations
10.
McLellan, Andrew, Terence Kealey, & Kenneth Langlands. (2006). An E box in the exon 1 promoter regulates insulin-like growth factor-I expression in differentiating muscle cells. American Journal of Physiology-Cell Physiology. 291(2). C300–C307. 22 indexed citations
11.
Kealey, Terence, et al.. (2005). Gene expression profiling of the ageing rat vibrissa follicle. British Journal of Dermatology. 153(1). 22–28. 5 indexed citations
12.
Goulden, Nick, et al.. (2003). PCR of Gene Rearrangements for the Detection of Minimal Residual Disease in Childhood ALL. Humana Press eBooks. 6. 3–24.
13.
McLellan, Andrew, Kenneth Langlands, & Terence Kealey. (2002). Exhaustive identification of human class II basic helix–loop–helix proteins by virtual library screening. Mechanisms of Development. 119. S285–S291. 14 indexed citations
14.
Goulden, Nicholas J., C. Knechtli, Russell J. Garland, et al.. (1998). Minimal residual disease analysis for the prediction of relapse in children with standard‐risk acute lymphoblastic leukaemia. British Journal of Haematology. 100(1). 235–244. 77 indexed citations
15.
Langlands, Kenneth, Xiaoying Yin, Geetha Anand, & Edward V. Prochownik. (1997). Differential Interactions of Id Proteins with Basic-Helix-Loop-Helix Transcription Factors. Journal of Biological Chemistry. 272(32). 19785–19793. 190 indexed citations
16.
Knechtli, C., Nick Goulden, Kenneth Langlands, & Michael Potter. (1995). The study of minimal residual disease in acute lymphoblastic leukaemia. Molecular Pathology. 48(2). M65–M73. 7 indexed citations
17.
18.
Langlands, Kenneth, et al.. (1993). Direct sequence analysis of TCR Vδ2–Dδ3 rearrangements in common acute lymphoblastic leukaemia and application to detection of minimal residual disease. British Journal of Haematology. 84(4). 648–655. 5 indexed citations
19.
Langlands, Kenneth, et al.. (1992). Polymerase chain reaction analysis of tumour contamination in peripheral blood stem cell harvests. Stem Cells. 10(S1). 95–97. 9 indexed citations
20.
Turner, M. L., et al.. (1992). An HIV positive haemophiliac with acute lymphoblastic leukaemia successfully treated with intensive chemotherapy and syngeneic bone marrow transplantation.. PubMed. 9(5). 387–9. 16 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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