Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction
Alberto Bartoli, Andrea De Lorenzo, Eric Medvet, Fabiano Tarlao, Marco Virgolin
UNIVERSITÀ DEGLI STUDI DI TRIESTE DIPARTIMENTO DI INGEGNERIA E ARCHITETTURA
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Problem
➔ Identifying sentences that contain interactions between genes and proteins ◆ from biomedical literature ➔ Available data: ◆ dictionary of genes, proteins and interactors ◆ example sentences
2
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Why? ➔ Biomedical literature is: ◆ vast ◆ rapidly growing
➔ Challenging problem: automatic extraction of knowledge from a text in natural language ◆ informations are “diluted” in the text ◆ very challenging problem: discover relations between entities
3
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Goal ➔ Generation of a classifier C in order to identify sentences containing interactions between genes and proteins ◆ automatically ◆ based on recurring syntactic patterns
4
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Our approach ➔ Classifier C is a set of regular expressions (regex)
C={r1,r2,...} ➔ Each regex is a sentence classifier (“accepts” or “does not accept”) ◆ C accepts sentences accepted by at least one regex ➔ Regex applied on a semantical representation of the text
5
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Our approach (II) ➔ Regex generated automatically ◆ by means of Genetic Programming (GP) ◆ starting from examples ● strings which must be accepted ● strings which must not be accepted
6
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Sentences preprocessing Mapping of a sentence s in a ɸ-string x a. substitution of words in s with “annotations” i. gene, protein, interactor or ii. Part-Of-Speech b. mapping of annotations in Unicode characters c. concatenation
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Evolutionary Learning of Syntax Patterns
DIA - UniTs
Sentences preprocessing (II) Example: s = YfhP may act as a negative regulator for the transcription of yfhQ ↓ [YfhP] [may] [act] [as] [a] [negative] [regulator] [for] [the] [transcription] [of] [yfhQ]
Generation of C: GP ➔ We used a Tree-based GP ➔ In this work candidate solution = regex
9
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Key aspects ➔ Multi-objective fitness: ◆ f=(Accuracy, FPR, Regex length) ◆ we purposefully avoided to include any problemspecific knowledge (gene/protein/…)
➔ Problem handled by mean of separate-andconquer ➔ Final output: set of regular expressions C={r1, r2,...} 10
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Separate-and-conquer ➔ Each regex ri ∈ C makes an independent and parallel classification ➔ Each regex is tailored for a sub-problem ◆ the problem is solved “step-by-step”
➔ Final output = logic OR of classifications
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Evolutionary Learning of Syntax Patterns
DIA - UniTs
Separate-and-conquer ● C=∅ ● we execute a GP search over the examples obtaining r* ● if FPR ＜ threshold ○ C = C ∪ {r*} ● else ○ terminate
● remove from the positive examples those which were classified correctly by r* 12
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Classifier example C = {r1, r2} r1 = GENEPTN[ˆRB][^NNS VBN GENEPTN]++ r2 = . INOUN IN GENEPTN . [ˆDT NN]
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Evolutionary Learning of Syntax Patterns
DIA - UniTs
Experimental evaluation: the data ➔ Dataset: 456 sentences from biomedical papers ◆ ½ with interactions e ½ without ◆ manually labelled by experts
➔ Dataset splitted in Learning e Testing ◆ ≈80% examples in Learning ◆ ≈20% examples in Testing
➔ 5 fold randomly generated ◆ with Testingi≠Testingj 14
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Baseline 1, 2: problem specific knowledge ➔ Annotations-Co-Occurrence ◆ it is tightly tailored to this specific problem ◆ sentence is positive if contains ● at least 2 genes/proteins ● at least 1 interactor
➔ Annotations-LLL05-Patterns ◆ 10 pattern generated in “LLL'05 Challenge: Genic Interaction Extraction with Alignments and Finite State Automata”
- J. Hakenberg et alia ◆ built over >90% of the dataset (also testing!)
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Evolutionary Learning of Syntax Patterns
DIA - UniTs
Baseline 3: ɸ-SSLEA ➔ Based on Smart State Labeling Algorithm ◆ algorithm for DFA learning ◆ works well in presence of noise
➔ Hill-Climbing ➔ Generates DFA which accepts or refuse a ɸstring x ◆ if x accepted ⇒ x contains an interaction between gene/protein ◆ otherwise, no 16
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Baseline 4, 5: Words-NaiveBayes e Words-SVM ➔ Standard for text classification ◆ Supervised Machine Learning methods
➔ Feature based on word occurrences ➔ Preprocessing ◆ stemming ◆ features selection
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Evolutionary Learning of Syntax Patterns
DIA - UniTs
Results Averaged over the 5 folds Classifier
Accuracy
FPR
FNR
Annotations-Co-Occurrence
77.8
40.0
4.5
Annotations-LLL05-Patterns
82.3
25.0
10.5
Words-NaiveBayes
51.3
25.0
95.0
Words-SVM
73.8
29.0
23.5
ɸ-SSLEA
59.8
44.0
33.5
C
73.7
23.5
22.5 18
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Results (II) ➔ C performs as well as Word-SVM and better than other learning approaches ➔ accuracies of C and Annotations-Co-Occurrence (which exploits domain knowledge of an expert) are very close ◆ Pro: C is composed by patterns (regex) readable ◆ Con: time to generate C (hours) ≫ time to generate other methods (minutes) ● but ≈ time taken for classifying (seconds)
19
Evolutionary Learning of Syntax Patterns
DIA - UniTs
Conclusions We proposed: ➔ a method for the automatic synthesis of a classifier for natural language sentences ◆ based on syntactic pattern ◆ by mean of GP ◆ separate-and-conquer ➔ results are highly promising
classroom use is granted without fee provided that copies are not made or distributed for profit or commercial ... GECCO '15, July 11â15, 2015, Madrid, Spain.
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