An Analysis of Relation Extraction within Sentences from Wet Lab Protocols

Author(s):  
Xianjun Yang ◽  
Xinlu Zhang ◽  
Julia Zuo ◽  
Stephen Wilson ◽  
Linda Petzold
Keyword(s):  
Author(s):  
Prachi Jain ◽  
Shikhar Murty ◽  
Mausam . ◽  
Soumen Chakrabarti

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.


2014 ◽  
Author(s):  
Miao Fan ◽  
Deli Zhao ◽  
Qiang Zhou ◽  
Zhiyuan Liu ◽  
Thomas Fang Zheng ◽  
...  

2009 ◽  
Vol 19 (11) ◽  
pp. 2843-2852 ◽  
Author(s):  
Jin-Xiu CHEN ◽  
Dong-Hong JI
Keyword(s):  

2012 ◽  
Vol 23 (10) ◽  
pp. 2572-2585 ◽  
Author(s):  
Yu CHEN ◽  
De-Quan ZHENG ◽  
Tie-Jun ZHAO
Keyword(s):  

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1483
Author(s):  
Ivan Antonov ◽  
Yulia Medvedeva

Although thousands of mammalian long non-coding RNAs (lncRNAs) have been reported in the last decade, their functional annotation remains limited. A wet-lab approach to detect functions of a novel lncRNA usually includes its knockdown followed by RNA sequencing and identification of the deferentially expressed genes. However, identification of the molecular mechanism(s) used by the lncRNA to regulate its targets frequently becomes a challenge. Previously, we developed the ASSA algorithm that detects statistically significant inter-molecular RNA-RNA interactions. Here we designed a workflow that uses ASSA predictions to estimate the ability of an lncRNA to function via direct base pairing with the target transcripts (co- or post-transcriptionally). The workflow was applied to 300+ lncRNA knockdown experiments from the FANTOM6 pilot project producing statistically significant predictions for 71 unique lncRNAs (104 knockdowns). Surprisingly, the majority of these lncRNAs were likely to function co-transcriptionally, i.e., hybridize with the nascent transcripts of the target genes. Moreover, a number of the obtained predictions were supported by independent iMARGI experimental data on co-localization of lncRNA and chromatin. We detected an evolutionarily conserved lncRNA CHASERR (AC013394.2 or LINC01578) that could regulate target genes co-transcriptionally via interaction with a nascent transcript by directing CHD2 helicase. The obtained results suggested that this nuclear lncRNA may be able to activate expression of the target genes in trans by base-pairing with the nascent transcripts and directing the CHD2 helicase to the regulated promoters leading to open the chromatin and active transcription. Our study highlights the possible importance of base-pairing between nuclear lncRNAs and nascent transcripts for the regulation of gene expression.


2010 ◽  
Vol 18 (2) ◽  
pp. 414-429 ◽  
Author(s):  
Kristin Andersson ◽  
Johan Richardsson ◽  
Bengt Lennartson ◽  
Martin Fabian

Sign in / Sign up

Export Citation Format

Share Document