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Advanced Natural Language Processing for Gene and Protein Relationship Extraction

This framework leverages sophisticated natural language processing techniques to automate the inference of biological entities such as genes, proteins, and malignancies. Utilizing a customized probabilistic inference tokenizer, it analyzes sentence structures to extract and clarify relationships—like transduction and activation—using nominalization tagging and semantic mapping. The system is built upon the PennBioTagger and incorporates tools like Sleator & Temperley’s LinkParser to enhance relationship extraction accuracy, ultimately facilitating deeper biological understanding through automated data processing.

miroslav-solovyov
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Advanced Natural Language Processing for Gene and Protein Relationship Extraction

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  1. Natural Language Processing for Automated Inference

  2. Tokenizer The pipeline Gene, Protein, and Malignancy Tagger Nominalization Tagger Sentence Extractor Semantic Mapper Probabilistic Inference

  3. Tokenizer Adapted from PennBioTagger Gene, Protein, and Malignancy Tagger Nominalization Tagger Sentence Extractor Semantic Mapper Probabilistic Inference

  4. Tokenizer Gene, Protein, and Malignancy Tagger Nominalization Tagger Sentence Extractor Semantic Mapper Probabilistic Inference

  5. Tokenizer Customized tags “transduction” “activation" Gene, Protein, and Malignancy Tagger Nominalization Tagger Sentence Extractor Semantic Mapper Probabilistic Inference

  6. Tokenizer Sleator & Temperley LinkParser + Relationship Extractor Gene, Protein, and Malignancy Tagger Nominalization Tagger Sentence Extractor Semantic Mapper Probabilistic Inference

  7. Tokenizer Abstracts Relex output from syntactical origins Gene, Protein, and Malignancy Tagger Nominalization Tagger Sentence Extractor Semantic Mapper Probabilistic Inference

  8. Tokenizer PLN Novamente AI Engine Gene, Protein, and Malignancy Tagger Nominalization Tagger Sentence Extractor Semantic Mapper Probabilistic Inference

  9. What it does Any of the sentences Kim kissed Pat. Pat was kissed by Kim. Is mapped into the set of relationships subj(kiss_0, Kim) obj(kiss_0, Pat) inheritance(kiss_0, kiss)

  10. How the semantic mapping rules look like The rule by($x, $y) & inheritance($x, transitive_event)  subj($x, $y) Maps the relex-produced relationship by(prevention, inhibition) Into the abstract conceptual relationship subj(prevention, inhibition) Which is suitable for inference by PLN.

  11. Example (Bioliterate)

  12. Background knowledge utilized Implication AND inh $x causal_event inh $y causal_event subj($y, $x) subj($x, $z) subj($y,$z)

  13. Abduction Inh inhib1, inhib Inh inhib2, inhib |- Inh inhib1, inhib2 Similarity Substitution Eval subj (prev1, inhib1) Inh inhib1, inhib2 |- Eval subj (prev1, inhib2) Deduction Inh inhib2, inhib Inh inhib,causal_event |- Inh inhib2, causal_event Probabilistic Inference

  14. And Inh inhib2, causal_event Inh prev1, causal_event Eval subj (prev1, inhib2) Eval subj (inhib2, DLC) |- AND Inh inhib2, causal_event Inh prev1, causal_event Eval subj (prev1, inhib2) Eval subj (inhib2, DLC) Unification ForAll ($x, $y, $z) Imp AND Inh $x, causal_event Inh $y, causal_event Eval subj ($y, $x) Eval subj ($x, $z) Eval subj ($y, $z) AND Inh inhib2, causal_event Inh prev1, causal_event Eval subj (prev1, inhib2) Eval subj (inhib2, DLC) |- Eval subj (prev1, inhib2) Probabilistic Inference

  15. Implication Breakdown (Modus Ponens) Imp AND Inh inhib2, causal_event Inh prev1, causal_event Eval subj (prev1, inhib2) Eval subj (inhib2, DLC) Eval subj (prev1, DLC) |- Eval subj (prev1, DLC) Probabilistic Inference

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