| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T1 |
0-99 |
Sentence |
denotes |
ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins. |
| T1 |
0-99 |
Sentence |
denotes |
ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins. |
| T2 |
100-111 |
Sentence |
denotes |
BACKGROUND: |
| T2 |
100-111 |
Sentence |
denotes |
BACKGROUND: |
| T3 |
112-294 |
Sentence |
denotes |
Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. |
| T3 |
112-294 |
Sentence |
denotes |
Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. |
| T4 |
295-434 |
Sentence |
denotes |
It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. |
| T4 |
295-434 |
Sentence |
denotes |
It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. |
| T5 |
435-672 |
Sentence |
denotes |
On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. |
| T5 |
435-672 |
Sentence |
denotes |
On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. |
| T6 |
673-807 |
Sentence |
denotes |
In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response. |
| T6 |
673-807 |
Sentence |
denotes |
In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response. |
| T7 |
808-816 |
Sentence |
denotes |
RESULTS: |
| T7 |
808-816 |
Sentence |
denotes |
RESULTS: |
| T8 |
817-943 |
Sentence |
denotes |
A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. |
| T8 |
817-943 |
Sentence |
denotes |
A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. |
| T9 |
944-1079 |
Sentence |
denotes |
The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. |
| T9 |
944-1079 |
Sentence |
denotes |
The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. |
| T10 |
1080-1285 |
Sentence |
denotes |
Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. |
| T10 |
1080-1285 |
Sentence |
denotes |
Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. |
| T11 |
1286-1405 |
Sentence |
denotes |
The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %. |
| T11 |
1286-1405 |
Sentence |
denotes |
The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %. |
| T12 |
1406-1417 |
Sentence |
denotes |
CONCLUSION: |
| T12 |
1406-1417 |
Sentence |
denotes |
CONCLUSION: |
| T13 |
1418-1578 |
Sentence |
denotes |
The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. |
| T13 |
1418-1578 |
Sentence |
denotes |
The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. |
| T14 |
1579-1732 |
Sentence |
denotes |
This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. |
| T14 |
1579-1732 |
Sentence |
denotes |
This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. |
| T15 |
1733-1969 |
Sentence |
denotes |
The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/ . |
| T15 |
1733-1969 |
Sentence |
denotes |
The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/ . |