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Jiadong Lin

Researcher at Xi'an Jiaotong University

Publications -  21
Citations -  429

Jiadong Lin is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 5, co-authored 14 publications receiving 121 citations. Previous affiliations of Jiadong Lin include Leiden University.

Papers
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Haplotype-resolved diverse human genomes and integrated analysis of structural variation.

Peter Ebert, +73 more
- 02 Apr 2021 - 
TL;DR: In this article, the authors present 64 assembled haplotypes from 32 diverse human genomes, which integrate all forms of genetic variation, even across complex loci, and identify 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing.
Journal ArticleDOI

MSIsensor-pro: Fast, Accurate, and Matched-normal-sample-free Detection of Microsatellite Instability

TL;DR: It is demonstrated that MSIsensor-pro is an ultrafast, accurate, and robust MSI calling method that significantly outperformed the current leading methods in both accuracy and computational cost.
Journal ArticleDOI

Three chromosome-scale Papaver genomes reveal punctuated patchwork evolution of the morphinan and noscapine biosynthesis pathway

TL;DR: In this article, a burst of structural variants involving fusions, translocations and duplications within 7.7 million years have assembled nine genes into the benzylisoquinoline alkaloids gene cluster, following a punctuated patchwork model.
Patent

Wearable device and method for intelligent monitoring and early warning of chronic diseases

TL;DR: In this paper, a wearable device and method for intelligent monitoring and early warning of chronic diseases is presented, which includes a wearable equipment terminal, a user mobile terminal and a cloud server.
Journal ArticleDOI

SVision: a deep learning approach to resolve complex structural variants

TL;DR: In this article , a deep learning-based multi-object recognition framework is proposed to automatically detect and characterize complex structural variants (CSVs) from long-read sequencing data, which can detect both common and previously uncharacterized complex rearrangements.