J
Jianyi Lin
Researcher at Khalifa University
Publications - 29
Citations - 4224
Jianyi Lin is an academic researcher from Khalifa University. The author has contributed to research in topics: Sparse approximation & Facial recognition system. The author has an hindex of 11, co-authored 28 publications receiving 3722 citations. Previous affiliations of Jianyi Lin include Swiss Institute of Bioinformatics & University of Milan.
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STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Andrea Franceschini,Damian Szklarczyk,Sune Frankild,Michael Kuhn,Milan Simonovic,Alexander Roth,Jianyi Lin,Pablo Minguez,Peer Bork,Christian von Mering,Lars Juhl Jensen +10 more
TL;DR: The update to version 9.1 of STRING is described, introducing several improvements, including extending the automated mining of scientific texts for interaction information, to now also include full-text articles, and providing users with statistical information on any functional enrichment observed in their networks.
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SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles
Andrea Franceschini,Andrea Franceschini,Jianyi Lin,Christian von Mering,Christian von Mering,Lars Juhl Jensen +5 more
TL;DR: A phylogenetic profiling algorithm, SVD-Phy, is devised, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions.
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RANKS: a flexible tool for node label ranking and classification in biological networks.
TL;DR: UNLABELLED RANKS provides an efficient and easy-to-use implementation of kernelized score functions, a semi-supervised algorithmic scheme embedding both local and global learning strategies for the analysis of biomolecular networks.
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ECG compression retaining the best natural basis k-coefficients via sparse decomposition
TL;DR: A novel and efficient signal compression algorithm aimed at finding the sparsest representation of electrocardiogram (ECG) signals is presented and analyzed and achieves in most of the cases very high performance.
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A derivation of the statistical characteristics of forest fires.
TL;DR: In this article, a two-layer spatially extended forest model is proposed, which encapsulates the main characteristics of vegetational growth and fire ignition and propagation, and supports the empirically discovered statistical laws.