Torbjörn E. M. Nordling

597 total citations
38 papers, 379 citations indexed

About

Torbjörn E. M. Nordling is a scholar working on Molecular Biology, Biophysics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Torbjörn E. M. Nordling has authored 38 papers receiving a total of 379 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 5 papers in Biophysics and 5 papers in Statistics, Probability and Uncertainty. Recurrent topics in Torbjörn E. M. Nordling's work include Gene Regulatory Network Analysis (17 papers), Bioinformatics and Genomic Networks (12 papers) and Gene expression and cancer classification (6 papers). Torbjörn E. M. Nordling is often cited by papers focused on Gene Regulatory Network Analysis (17 papers), Bioinformatics and Genomic Networks (12 papers) and Gene expression and cancer classification (6 papers). Torbjörn E. M. Nordling collaborates with scholars based in Taiwan, Sweden and United States. Torbjörn E. M. Nordling's co-authors include Andreas Tjärnberg, Erik L. L. Sonnhammer, Sven Nelander, Janne Koljonen, Jarmo T. Alander, Matthew Studham, Elling W. Jacobsen, Mark A.A.M. Leenders, Rebecka Jörnsten and Keiko Funa and has published in prestigious journals such as Bioinformatics, Scientific Reports and BMC Bioinformatics.

In The Last Decade

Torbjörn E. M. Nordling

32 papers receiving 370 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Torbjörn E. M. Nordling Taiwan	12	217	48	36	34	32	38	379
Yida Huang China	13	219 1.0×	17 0.4×	90 2.5×	45 1.3×	7 0.2×	30	462
Hongxiao Li China	9	93 0.4×	94 2.0×	38 1.1×	5 0.1×	99 3.1×	26	258
Xiaoyi Lü China	10	89 0.4×	86 1.8×	87 2.4×	91 2.7×	97 3.0×	32	367
Alexandra Sala United Kingdom	10	188 0.9×	127 2.6×	82 2.3×	12 0.4×	201 6.3×	16	464
Venkatesh Natarajan United States	15	344 1.6×	25 0.5×	108 3.0×	17 0.5×	8 0.3×	28	486
Dharmakeerthi Nawarathna United States	13	108 0.5×	8 0.2×	269 7.5×	14 0.4×	6 0.2×	53	411
Bingwen Yu China	11	156 0.7×	26 0.5×	362 10.1×	16 0.5×	11 0.3×	30	520
Qiao Liu China	10	203 0.9×	7 0.1×	58 1.6×	47 1.4×	40 1.3×	19	320
Jennifer J. Klein United States	3	147 0.7×	6 0.1×	49 1.4×	116 3.4×	19 0.6×	3	365

Countries citing papers authored by Torbjörn E. M. Nordling

Since Specialization

Citations

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

Fields of papers citing papers by Torbjörn E. M. Nordling

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Torbjörn E. M. Nordling. 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 Torbjörn E. M. Nordling. The network helps show where Torbjörn E. M. Nordling may publish in the future.

Co-authorship network of co-authors of Torbjörn E. M. Nordling

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

All Works

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20 of 20 papers shown

Tjärnberg, Andreas, et al.. (2024). GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data. NAR Genomics and Bioinformatics. 6(3). lqae121–lqae121.

Hsieh, Shulan, et al.. (2024). Age Prediction Using Resting-State Functional MRI. Neuroinformatics. 22(2). 119–134. 3 indexed citations

Nordling, Torbjörn E. M., et al.. (2024). Skin feature point tracking using deep feature encodings. International Journal of Machine Learning and Cybernetics. 16(4). 2503–2521.

Nordling, Torbjörn E. M., et al.. (2024). Ideal adaptive control in biological systems: an analysis of $$\mathbb {P}$$-invariance and dynamical compensation properties. BMC Bioinformatics. 25(1). 95–95. 1 indexed citations

Nordling, Torbjörn E. M., et al.. (2024). Combining Old School Autoencoder with Cotracker for Improved Skin Feature Tracking. 1–6. 1 indexed citations

Tjärnberg, Andreas, et al.. (2022). Knowledge of the perturbation design is essential for accurate gene regulatory network inference. Scientific Reports. 12(1). 16531–16531. 7 indexed citations

Tjärnberg, Andreas, et al.. (2020). Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data. npj Systems Biology and Applications. 6(1). 37–37. 16 indexed citations

Studham, Matthew, Andreas Tjärnberg, Holger Weishaupt, et al.. (2020). Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms. Scientific Reports. 10(1). 14149–14149. 7 indexed citations

Leenders, Mark A.A.M., et al.. (2019). Case Study: Taiwan’s pathway into a circular future for buildings. IOP Conference Series Earth and Environmental Science. 225. 12060–12060. 18 indexed citations

10.

Celano, Umberto, Feng‐Chun Hsia, Danielle Vanhaeren, et al.. (2018). Mesoscopic physical removal of material using sliding nano-diamond contacts. Scientific Reports. 8(1). 2994–2994. 32 indexed citations

11.

Tjärnberg, Andreas, et al.. (2018). A generalized framework for controlling FDR in gene regulatory network inference. Bioinformatics. 35(6). 1026–1032. 10 indexed citations

12.

Tjärnberg, Andreas, et al.. (2017). GeneSPIDER – gene regulatory network inference benchmarking with controlled network and data properties. Molecular BioSystems. 13(7). 1304–1312. 16 indexed citations

13.

Padhan, Narendra, Torbjörn E. M. Nordling, Magnus Sundström, et al.. (2016). High sensitivity isoelectric focusing to establish a signaling biomarker for the diagnosis of human colorectal cancer. BMC Cancer. 16(1). 683–683. 11 indexed citations

14.

Nordling, Torbjörn E. M., Narendra Padhan, Sven Nelander, & Lena Claesson‐Welsh. (2015). Identification of Biomarkers and Signatures in Protein Data. 411–419.

15.

Tjärnberg, Andreas, Torbjörn E. M. Nordling, Matthew Studham, Sven Nelander, & Erik L. L. Sonnhammer. (2014). Avoiding pitfalls in L1-regularised inference of gene networks. Molecular BioSystems. 11(1). 287–296. 15 indexed citations

16.

Nordling, Torbjörn E. M.. (2013). Robust inference of gene regulatory networks : System properties, variable selection, subnetworks, and design of experiments. KTH Publication Database DiVA (KTH Royal Institute of Technology). 3 indexed citations

17.

Tjärnberg, Andreas, Torbjörn E. M. Nordling, Matthew Studham, & Erik L. L. Sonnhammer. (2013). Optimal Sparsity Criteria for Network Inference. Journal of Computational Biology. 20(5). 398–408. 15 indexed citations

18.

Jacobsen, Elling W. & Torbjörn E. M. Nordling. (2012). Robust Inference of Gene Regulatory Networks. International Conference on Systems. 1 indexed citations

19.

Nordling, Torbjörn E. M. & Elling W. Jacobsen. (2011). On Sparsity as a Criterion in Reconstructing Biochemical Networks. IFAC Proceedings Volumes. 44(1). 11672–11678. 3 indexed citations

20.

Nordling, Torbjörn E. M.. (2005). Issues on modelling of large-scale cellular regulatory networks.

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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