INT211717

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Context Info
Confidence 0.37
First Reported 2007
Last Reported 2010
Negated 0
Speculated 0
Reported most in Body
Documents 13
Total Number 13
Disease Relevance 2.13
Pain Relevance 0.10

This is a graph with borders and nodes. Maybe there is an Imagemap used so the nodes may be linking to some Pages.

nucleoplasm (CHEK2) nucleus (CHEK2) cell cycle (CHEK2)
cell division (CHEK2)
Anatomy Link Frequency
corpus 1
astrocytes 1
CHEK2 (Homo sapiens)
Pain Link Frequency Relevance Heat
cva 22 73.20 Quite High
Hippocampus 8 18.64 Low Low
Serotonin 22 5.00 Very Low Very Low Very Low
Paracetamol 11 5.00 Very Low Very Low Very Low
imagery 3 5.00 Very Low Very Low Very Low
Central nervous system 1 5.00 Very Low Very Low Very Low
Pain 1 5.00 Very Low Very Low Very Low
Pyramidal cell 1 5.00 Very Low Very Low Very Low
pain pelvic 1 5.00 Very Low Very Low Very Low
Disease Link Frequency Relevance Heat
Congenital Anomalies 3 96.48 Very High Very High Very High
Disease 114 93.84 High High
Stress 7 87.44 High High
Skin Cancer 14 85.84 High High
Epstein-barr Virus 55 84.44 Quite High
Carcinoma 5 83.00 Quite High
Hypersensitivity 4 80.24 Quite High
Cv General 2 Under Development 11 75.08 Quite High
Cv General 3 Under Development 22 73.20 Quite High
Disease Progression 1 72.40 Quite High

Sentences Mentioned In

Key: Protein Mutation Event Anatomy Negation Speculation Pain term Disease term
The nuclei were diffusely positive for Chk2 and p27, which is considered to be the normal staining pattern for these markers.
Gene_expression (positive) of Chk2
1) Confidence 0.37 Published 2007 Journal Diagn Pathol Section Body Doc Link PMC1947947 Disease Relevance 0.62 Pain Relevance 0
These cells were most likely activated astrocytes as they could be double labeled with GFAP and P-Chk2 (Figure 2F, inset).


Gene_expression (labeled) of P-Chk2 in astrocytes
2) Confidence 0.34 Published 2010 Journal Mol Neurodegener Section Body Doc Link PMC3018387 Disease Relevance 0.89 Pain Relevance 0
One advantage of collocation-based approaches is the correct detection of LFs for SFs that are created through symbols/synonyms substitution/initialization.
Gene_expression (detection) of LFs
3) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0 Pain Relevance 0
We conducted a comparison study to evaluate their performance in detecting LFs.
Gene_expression (detecting) of LFs
4) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0 Pain Relevance 0
The study was designed to answer the following questions; i) how well a system performs in detecting LFs from novel text, ii) what the coverage is for various terminological knowledge bases in including SFs as synonyms of their LFs, and iii) how to combine results from various SF knowledge bases.


Gene_expression (detecting) of LFs
5) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Abstract Doc Link PMC2217663 Disease Relevance 0 Pain Relevance 0
The second step is to define common criteria to select candidate sentences for detecting LFs.
Gene_expression (detecting) of LFs
6) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0 Pain Relevance 0
First, how do various systems perform in detecting LFs for SFs from parenthetical expressions given a large collection of novel text?
Gene_expression (detecting) of LFs
7) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0.19 Pain Relevance 0
The basic assumption of the alignment-based approach is that LFs can be found in neighboring phrases that subsume all or almost all the letters of the corresponding SF (in the same order).
Gene_expression (found) of LFs
8) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0.11 Pain Relevance 0.03
In the following, we provide background information about SFs in the biomedical domain, review studies published relevant to detecting LFs for SFs in text, and summarize two terminological knowledge bases in biomedicine.
Gene_expression (detecting) of LFs
9) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0.13 Pain Relevance 0.04
Higher scores indicate more confidence in detecting LFs.
Gene_expression (detecting) of LFs
10) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0.05 Pain Relevance 0
Existing methods for detecting LFs in text or assembling SF knowledge bases can be categorized into one or combination of the following four types: (i) alignment-based approach, (ii) machine learning approach, (iii) template/rule-based approach, and (iv) collocation-based approach.
Gene_expression (detecting) of LFs
11) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0.14 Pain Relevance 0.03
Note that we did not include the collocation-based approach in the comparison study since it is not suitable for detecting LFs in text but for assembling SF knowledge bases from a large corpus.
Gene_expression (detecting) of LFs in corpus
12) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0 Pain Relevance 0
As we have discussed, there are four types of approaches for detecting LFs for SFs.
Gene_expression (detecting) of LFs
13) Confidence 0.02 Published 2007 Journal BMC Bioinformatics Section Body Doc Link PMC2217663 Disease Relevance 0 Pain Relevance 0

General Comments

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