Antibody Watch: Text mining antibody specificity from the literature.
Antibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable research results. While many prop...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-05-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008967 |
| _version_ | 1818582244149690368 |
|---|---|
| author | Chun-Nan Hsu Chia-Hui Chang Thamolwan Poopradubsil Amanda Lo Karen A William Ko-Wei Lin Anita Bandrowski Ibrahim Burak Ozyurt Jeffrey S Grethe Maryann E Martone |
| author_facet | Chun-Nan Hsu Chia-Hui Chang Thamolwan Poopradubsil Amanda Lo Karen A William Ko-Wei Lin Anita Bandrowski Ibrahim Burak Ozyurt Jeffrey S Grethe Maryann E Martone |
| author_sort | Chun-Nan Hsu |
| collection | DOAJ |
| description | Antibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable research results. While many proposals have been developed to deal with the problem of antibody specificity, it is still challenging to cover the millions of antibodies that are available to researchers. In this study, we investigate the feasibility of automatically generating alerts to users of problematic antibodies by extracting statements about antibody specificity reported in the literature. The extracted alerts can be used to construct an "Antibody Watch" knowledge base containing supporting statements of problematic antibodies. We developed a deep neural network system and tested its performance with a corpus of more than two thousand articles that reported uses of antibodies. We divided the problem into two tasks. Given an input article, the first task is to identify snippets about antibody specificity and classify if the snippets report that any antibody exhibits non-specificity, and thus is problematic. The second task is to link each of these snippets to one or more antibodies mentioned in the snippet. The experimental evaluation shows that our system can accurately perform the classification task with 0.925 weighted F1-score, linking with 0.962 accuracy, and 0.914 weighted F1 when combined to complete the joint task. We leveraged Research Resource Identifiers (RRID) to precisely identify antibodies linked to the extracted specificity snippets. The result shows that it is feasible to construct a reliable knowledge base about problematic antibodies by text mining. |
| first_indexed | 2024-12-16T07:46:18Z |
| format | Article |
| id | doaj.art-49f35c7493f8451bb6251edd6758aa3f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T07:46:18Z |
| publishDate | 2021-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-49f35c7493f8451bb6251edd6758aa3f2022-12-21T22:38:58ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-05-01175e100896710.1371/journal.pcbi.1008967Antibody Watch: Text mining antibody specificity from the literature.Chun-Nan HsuChia-Hui ChangThamolwan PoopradubsilAmanda LoKaren A WilliamKo-Wei LinAnita BandrowskiIbrahim Burak OzyurtJeffrey S GretheMaryann E MartoneAntibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable research results. While many proposals have been developed to deal with the problem of antibody specificity, it is still challenging to cover the millions of antibodies that are available to researchers. In this study, we investigate the feasibility of automatically generating alerts to users of problematic antibodies by extracting statements about antibody specificity reported in the literature. The extracted alerts can be used to construct an "Antibody Watch" knowledge base containing supporting statements of problematic antibodies. We developed a deep neural network system and tested its performance with a corpus of more than two thousand articles that reported uses of antibodies. We divided the problem into two tasks. Given an input article, the first task is to identify snippets about antibody specificity and classify if the snippets report that any antibody exhibits non-specificity, and thus is problematic. The second task is to link each of these snippets to one or more antibodies mentioned in the snippet. The experimental evaluation shows that our system can accurately perform the classification task with 0.925 weighted F1-score, linking with 0.962 accuracy, and 0.914 weighted F1 when combined to complete the joint task. We leveraged Research Resource Identifiers (RRID) to precisely identify antibodies linked to the extracted specificity snippets. The result shows that it is feasible to construct a reliable knowledge base about problematic antibodies by text mining.https://doi.org/10.1371/journal.pcbi.1008967 |
| spellingShingle | Chun-Nan Hsu Chia-Hui Chang Thamolwan Poopradubsil Amanda Lo Karen A William Ko-Wei Lin Anita Bandrowski Ibrahim Burak Ozyurt Jeffrey S Grethe Maryann E Martone Antibody Watch: Text mining antibody specificity from the literature. PLoS Computational Biology |
| title | Antibody Watch: Text mining antibody specificity from the literature. |
| title_full | Antibody Watch: Text mining antibody specificity from the literature. |
| title_fullStr | Antibody Watch: Text mining antibody specificity from the literature. |
| title_full_unstemmed | Antibody Watch: Text mining antibody specificity from the literature. |
| title_short | Antibody Watch: Text mining antibody specificity from the literature. |
| title_sort | antibody watch text mining antibody specificity from the literature |
| url | https://doi.org/10.1371/journal.pcbi.1008967 |
| work_keys_str_mv | AT chunnanhsu antibodywatchtextminingantibodyspecificityfromtheliterature AT chiahuichang antibodywatchtextminingantibodyspecificityfromtheliterature AT thamolwanpoopradubsil antibodywatchtextminingantibodyspecificityfromtheliterature AT amandalo antibodywatchtextminingantibodyspecificityfromtheliterature AT karenawilliam antibodywatchtextminingantibodyspecificityfromtheliterature AT koweilin antibodywatchtextminingantibodyspecificityfromtheliterature AT anitabandrowski antibodywatchtextminingantibodyspecificityfromtheliterature AT ibrahimburakozyurt antibodywatchtextminingantibodyspecificityfromtheliterature AT jeffreysgrethe antibodywatchtextminingantibodyspecificityfromtheliterature AT maryannemartone antibodywatchtextminingantibodyspecificityfromtheliterature |