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Torbjörn E. M. Nordling

Researcher at National Cheng Kung University

Publications -  50
Citations -  475

Torbjörn E. M. Nordling is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Gene regulatory network & Inference. The author has an hindex of 10, co-authored 45 publications receiving 401 citations. Previous affiliations of Torbjörn E. M. Nordling include University of Vaasa & Royal Institute of Technology.

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Journal ArticleDOI

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

TL;DR: A modeling framework to detect disease‐driving CNAs and their effect on target mRNA expression, and to stratify cancer patients into long‐ and short‐term survivors is developed, concluding that large‐scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer.
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A review of genetic algorithms in near infrared spectroscopy and chemometrics: past and future

TL;DR: To conclude, optimisation gives good opportunities to simultaneously develop an accurate calibration model and to regulate model complexity and prediction ability within a considered validation framework.
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Functional association networks as priors for gene regulatory network inference

TL;DR: The potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference is explored, and it is indicated that using FunCoup and STRING as priors can give a small improvement inGRN inference accuracy with biological data.
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Mesoscopic physical removal of material using sliding nano-diamond contacts

TL;DR: Nanosized sliding contacts made of diamond in the regime whereby thousands of nm3 are removed are studied and a controllable removal rate below 5 nm/scan for all the materials is demonstrated, thus opening to future development of 3D tomographic AFM.
Proceedings Article

Robust Inference of Gene Regulatory Networks

TL;DR: In this article, the authors proposed a robust rank-based approach to determine the existence of a specific biological network edge under uncertainty, based on a matrix constructed from available perturbation and response data.