MinePhos: A Literature Mining System for Protein Phoshphorylation Information Extraction

2012 ◽  
Vol 9 (1) ◽  
pp. 311-315 ◽  
Author(s):  
Yun Xu ◽  
Da Teng ◽  
Yiming Lei
Keyword(s):  
Information Extraction ◽  
Literature Mining ◽  
Mining System

Information Extraction of Protein Phosphorylation from Biomedical Literature

2010 ◽  
pp. 163-176
Author(s):  
M. Narayanaswamy ◽  
K. E. Ravikumar ◽  
Z. Z. Hu ◽  
K. Vijay-Shanker ◽  
C. H. Wu
Keyword(s):  
Text Mining ◽  
Information Extraction ◽  
Protein Phosphorylation ◽  
Biomedical Literature ◽  
Literature Mining ◽  
Second Phase ◽  
Mining System ◽  
Rule Based ◽  
Kinase Substrate ◽  
Fundamental Biological Process

Protein posttranslational modification (PTM) is a fundamental biological process, and currently few text mining systems focus on PTM information extraction. A rule-based text mining system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was recently developed by our group to extract protein substrate, kinase and phosphorylated residue/sites from MEDLINE abstracts. This chapter covers the evaluation and benchmarking of RLIMS-P and highlights some novel and unique features of the system. The extraction patterns of RLIMS-P capture a range of lexical, syntactic and semantic constraints found in sentences expressing phosphorylation information. RLIMS-P also has a second phase that puts together information extracted from different sentences. This is an important feature since it is not common to find the kinase, substrate and site of phosphorylation to be mentioned in the same sentence. Small modifications to the rules for extraction of phosphorylation information have also allowed us to develop systems for extraction of two other PTMs, acetylation and methylation. A thorough evaluation of these two systems needs to be completed. Finally, an online version of RLIMSP with enhanced functionalities, namely, phosphorylation annotation ranking, evidence tagging, and protein entity mapping, has been developed and is publicly accessible.

scholarly journals Inhibition of the Dead Box RNA Helicase 3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation

2019 ◽  
Author(s):  
Marina Aksenova ◽  
Justin Sybrandt ◽  
Biyun Cui ◽  
Vitali Sikirzhytski ◽  
Hao Ji ◽  
...  
Keyword(s):  
Microglial Activation ◽  
Drugs Of Abuse ◽  
Viral Proteins ◽  
Rna Helicase ◽  
Literature Mining ◽  
Tat Protein ◽  
Mining System ◽  
Dead Box ◽  
Neuronal Toxicity ◽  
Hiv 1

AbstractHIV-1 Associated Neurocognitive Disorder (HAND) is commonly seen in HIV-infected patients. Viral proteins including Tat cause neuronal toxicity and is worsened by drugs of abuse. To uncover potential targets for anti-HAND therapy, we employed a literature mining system, MOLIERE. Here, we validated Dead Box RNA Helicase 3 (DDX3) as a target to treat HAND via a selective DDX3 inhibitor, RK-33. The combined neurotoxicity of Tat protein and cocaine was blocked by RK-33 in rat and mouse cortical cultures. Transcriptome analysis showed that Tat-activated transcripts include makers and regulators of microglial activation, and RK-33 blocked Tat-induced activation of these mRNAs. Elevated production of proinflammatory cytokines was also inhibited by RK-33. These findings show that DDX3 contributes to microglial activation triggered by Tat and cocaine, and DDX3 inhibition shows promise as a therapy for HAND. Moreover, DDX3 may contribute to the pathology of other neurodegenerative diseases with pathological activation of microglia.

scholarly journals A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

2013 ◽  
Vol 7 (Suppl 3) ◽  
pp. S9 ◽  
Author(s):  
Zuoshuang Xiang ◽  
Tingting Qin ◽  
Zhaohui S Qin ◽  
Yongqun He
Keyword(s):  
Literature Mining ◽  
Mining System ◽  
Genome Wide ◽  
A Genome

Information Extraction in the Medical Domain

2015 ◽  
Vol 8 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Aicha Ghoulam ◽  
Fatiha Barigou ◽  
Ghalem Belalem
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Personal Information ◽  
Recognition Task ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Literature Mining ◽  
Advantages And Disadvantages

Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyse texts written in natural language to extract structured and useful information such as named entities and semantic relations between them. Information extraction is an important task in a diverse set of applications like bio-medical literature mining, customer care, community websites, personal information management and so on. In this paper, the authors focus only on information extraction from clinical reports. The two most fundamental tasks in information extraction are discussed; namely, named entity recognition task and relation extraction task. The authors give details about the most used rule/pattern-based and machine learning techniques for each task. They also make comparisons between these techniques and summarize the advantages and disadvantages of each one.

scholarly journals deepMINE - Natural Language Processing based Automatic Literature Mining and Research Summarization for Early-Stage Comprehension in Pandemic Situations specifically for COVID-19

2020 ◽  
Author(s):  
Bhrugesh Joshi ◽  
Vishvajit Bakarola ◽  
Parth Shah ◽  
Ramar Krishnamurthy
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Early Stage ◽  
Biomedical Literature ◽  
Research Articles ◽  
Literature Mining ◽  
Critical Research ◽  
Mining System ◽  
Manual Processing ◽  
Novel Coronavirus

AbstractThe recent pandemic created due to Novel Coronavirus (nCOV-2019) from Wuhan, China demanding a large scale of a general health emergency. This demands novel research on the vaccine to fight against this pandemic situation, re-purposing of the existing drugs, phylogenetic analysis to identify the origin and determine the similarity with other known viruses, etc. The very preliminary task from the research community is to analyze the wide verities of existing related research articles, which is very much time-consuming in such situations where each minute counts for saving hundreds of human lives. The entire manual processing is even lower down the efficiency in mining the information. We have developed a complete automatic literature mining system that delivers efficient and fast mining from existing biomedical literature databases. With the help of modern-day deep learning algorithms, our system also delivers a summarization of important research articles that provides ease and fast comprehension of critical research articles. The system is currently scanning nearly 1,46,115,136 English words from 29,315 research articles in not greater than 1.5 seconds with multiple search keywords. Our research article presents the criticality of literature mining, especially in pandemic situations with the implementation and online deployment of the system.

BioTopic: a topic-driven biological literature mining system

2016 ◽  
Vol 14 (4) ◽  
pp. 373 ◽  
Author(s):  
Xi Wang ◽  
Peiyan Zhu ◽  
Tao Liu ◽  
Ke Xu
Keyword(s):  
Literature Mining ◽  
Mining System ◽  
Biological Literature

scholarly journals MBA: a literature mining system for extracting biomedical abbreviations

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Yun Xu ◽  
ZhiHao Wang ◽  
YiMing Lei ◽  
YuZhong Zhao ◽  
Yu Xue
Keyword(s):  
Literature Mining ◽  
Mining System

MPTM: A tool for mining protein post-translational modifications from literature

2017 ◽  
Vol 15 (05) ◽  
pp. 1740005 ◽  
Author(s):  
Dongdong Sun ◽  
Minghui Wang ◽  
Ao Li
Keyword(s):  
Text Mining ◽  
Large Scale ◽  
Biomedical Literature ◽  
Literature Mining ◽  
Mining System ◽  
Post Translational Modifications ◽  
Source Codes ◽  
Related Information ◽  
Protein Entry ◽  
Rapid Pace

Due to the importance of post-translational modifications (PTMs) in human health and diseases, PTMs are regularly reported in the biomedical literature. However, the continuing and rapid pace of expansion of this literature brings a huge challenge for researchers and database curators. Therefore, there is a pressing need to aid them in identifying relevant PTM information more efficiently by using a text mining system. So far, only a few web servers are available for mining information of a very limited number of PTMs, which are based on simple pattern matching or pre-defined rules. In our work, in order to help researchers and database curators easily find and retrieve PTM information from available text, we have developed a text mining tool called MPTM, which extracts and organizes valuable knowledge about 11 common PTMs from abstracts in PubMed by using relations extracted from dependency parse trees and a heuristic algorithm. It is the first web server that provides literature mining service for hydroxylation, myristoylation and GPI-anchor. The tool is also used to find new publications on PTMs from PubMed and uncovers potential PTM information by large-scale text analysis. MPTM analyzes text sentences to identify protein names including substrates and protein-interacting enzymes, and automatically associates them with the UniProtKB protein entry. To facilitate further investigation, it also retrieves PTM-related information, such as human diseases, Gene Ontology terms and organisms from the input text and related databases. In addition, an online database (MPTMDB) with extracted PTM information and a local MPTM Lite package are provided on the MPTM website. MPTM is freely available online at http://bioinformatics.ustc.edu.cn/mptm/ and the source codes are hosted on GitHub: https://github.com/USTC-HILAB/MPTM .

scholarly journals RLIMS-P 2.0: A Generalizable Rule-Based Information Extraction System for Literature Mining of Protein Phosphorylation Information

2015 ◽  
Vol 12 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Manabu Torii ◽  
Cecilia N. Arighi ◽  
Gang Li ◽  
Qinghua Wang ◽  
Cathy H. Wu ◽  
...  
Keyword(s):  
Information Extraction ◽  
Protein Phosphorylation ◽  
Extraction System ◽  
Literature Mining ◽  
Rule Based ◽  
Information Extraction System
Sign in / Sign up

Export Citation Format

Share Document