6 Protein-protein Interaction Extraction
7.5 General discussion
7.6.4 Standardization of the annotations in literature
The bulk of challenges we had to go through to extract all this information from the literature also affected our perception regarding the way the information is provided to the scientific community.
On one hand, we realized that automatic methods are still far away from being able to manage all the problems involved in the PPI process as accurately as the human can do. On the other hand, we also noticed that concentrating all the curation work at the level of the database curators is not a scalable solution, given the amount of literature that must be treated (Baumgartner, Cohen et al.
2007). Consequently, it is worth thinking about alternative ways to resolve the annotation problem, for instance, by shifting many efforts at the level of the authors by giving them some curation responsibilities. Indeed, there is no other person more suitable than the authors themselves to identify and annotate the information of interest in their papers. Unfortunately, involving the authors in the integration of controlled information in their articles cannot be done so easily. It requires adopting strong standards regarding the way the information is structured and stored in the documents. Some researchers have already proposed some solutions (Rebholz-Schuhmann, Kirsch et al. 2006; Seringhaus and Gerstein 2007; Ceol, Chatr-Aryamontri et al. 2008; Rebholz-Schuhmann and Nenadic 2008), but they all require to increase the cooperation among all the actors related to publication microcosm.
Figures
FIGURE 1 COMPLETE PROTEIN-PROTEIN EXTRACTION PROCESS ... 8
FIGURE 2PROPORTION OF POSITIVE AND NEGATIVE INSTANCES IN THE TWO DATA SETS ... 42
FIGURE 3DISTRIBUTION OF THE TWO CLASSES GIVEN NUMBER OF MESH TERMS FOR THE TRAINING SET ... 49
FIGURE 4DISTRIBUTION OF THE NUMBER OF MESH TERMS IN THE TWO CLASSES FOR THE TEST SET ... 50
FIGURE 5COVERAGE AND AVERAGE ACCURACY OBTAINED BY CLASSIFYING DOCUMENTS OF THE TRAINING SET USING THE MOST REPRESENTATIVE CLASS OF THE MESH TERMS ... 50
FIGURE 6COVERAGE AND AVERAGE ACCURACY OBTAINED BY CLASSIFYING THE DOCUMENTS OF THE TEST SET USING THE MOST REPRESENTATIVE CLASS OF THE MESH TERMS ON THE TEST SET ... 51
FIGURE 7COVERAGE AND AVERAGE ACCURACY OBTAINED BY CLASSIFYING THE DOCUMENTS OF THE TEST SET USING THE MOST REPRESENTATIVE CLASS OF THE MESH TERMS ON THE TRAINING SET ... 51
FIGURE 8PROPORTION OF POSITIVE AND NEGATIVE INSTANCES GIVEN PROTEINS COUNT ... 53
FIGURE 9PROPORTION OF POSITIVE AND NEGATIVE INSTANCES GIVEN THE INTERACTORS COUNT ... 54
FIGURE 10ACCURACY GIVEN C AND EPSILON PARAMETER VARIATION ON THE TRAINING SET ... 57
FIGURE 11RECALL AND PRECISION GRAPH OF THE DIFFERENT APPORACHES USED AT BIOCREATIVEII ... 66
FIGURE 12ACCURACY OBTAIN BY THE DIFFERENT TEAM IN BIOCREATIVEII ... 66
FIGURE 13NUMBER OF GENES PER DOCUMENTS ... 73
FIGURE 14NUMBER OF WORDS COMPOSING GENE NAMES IN THE TRAINING SET ... 74
FIGURE 15COMMON AND EXCLUSIVE WORDS IN THE TRAINING AND TEST SET OF THE GENE MENTION TASK ... 75
FIGURE 16NUMBER OF TERMS PER UNIQUE ID IN GPSDB ... 76
FIGURE 17FREQUENCIES OF THE WORDS IN THE EXPERT CURATORS AND WALL STREET JOURNAL LEXICON ... 77
FIGURE 18PROPORTION OF WORDS IN THE LEXICON GIVEN THE PERCENTAGE OF UPPERCASE LETTERS ... 81
FIGURE 19PROPORTION OF WORDS IN THE LEXICON GIVEN THE PERCENTAGE OF DIGIT ... 81
FIGURE 20PERFORMANCES VARIATION GIVEN DIFFERENT TEMPLATES PARAMETERS ... 90
FIGURE 21F-MEASURE EVOLUTION GIVEN THE VARIATION OF COST,ETA AND MAX ITER ... 91
FIGURE 22COMPARISON OF THE DIFFERENT APPROACHES ... 94
FIGURE 23RECALL AND PRECISION OBTAINED FOR THE RUNS OF THE DIFFERENT GROUPS ... 95
FIGURE 24F-MEASURE OBTAINED BY THE DIFFERENT GROUPS DURING BIOCREATIVEII ... 96
FIGURE 25NUMBER OF TERMS RELATED TO AN ID ... 102
FIGURE 26NUMBER OF WORD PER TERM CONTAINED IN ENTREZGENE ... 104
FIGURE 27NUMBER OF WORDS COMBINATIONS PRODUCED GIVEN THE NUMBER OF SELECTED WORDS FOUND IN SEQUENCE ... 105
FIGURE 28 RELATIVE FREQUENCIES OF THE SHARED TERMS BETWEEN THE ENTREZGENE AND THE WALL STREET JOURNAL CORPUSES ... 107
FIGURE 29PRODUCED WORDS AND TERMS BY THE DIFFERENT VARIANT GENERATION TECHNIQUES... 110
FIGURE 30LEVENSHTEIN DISTANCE COMPUTED BETWEEN IFI-17 AND IFI16 ... 111
FIGURE 31PERFORMANCE OBTAINED GIVEN THE MINIMUM RELATIVE FREQUENCY OF THE SHARED WORDS ... 116
FIGURE 32 EVOLUTION OF THE PERFORMANCE GIVEN THE MINIMUM SIMILARITY LEVEL REQUIRED TO PERFORM A MATCH ... 117
FIGURE 33COMPARISON OF THE PERFORMANCE OF THE DIFFERENT APPROACHES IN BIOCREATIVEII... 119
FIGURE 34COMPONENTS OF THE PROTEIN-PROTEIN INTERACTIONS EXTRACTION PROCESS ... 124
FIGURE 35NUMBER OF INTERACTIONS PER DOCUMENT IN THE TRAINING SET ... 125
FIGURE 36PROPORTION OF ENTREZGENE TERMS FOUND IN GPSDB ... 127
FIGURE 37NUMBER OF TERMS PER ID GIVEN THE GPSDB AND ENTREZGENE LEXICON ... 127
FIGURE 38CHI2FEATURE SELECTION SCORE OF THE TRANSITIVE VERBS BASED ON THE DATA OF THE BINARY CLASSIFICATION TASK . 129 FIGURE 39CUMULATE FREQUENCY OF THE MOST COMMON SPECIE IN GPSDB ... 130
FIGURE 40 NUMBER OF SPECIES INVOLVED IN ONE INTERACTION... 131 FIGURE 41DISTRIBUTION OF THE NUMBER OF RETURNED PROTEIN MENTIONS PER DOCUMENTS GIVEN THE EXTRACTION METHOD . 134
FIGURE 42DISTRIBUTION OF THE INITIAL SET OF PROTEIN .VS. THE PROTEINS BELONGING TO AN INTERACTION PATTERN GIVEN THE SELECTION USING FUZZY METRIC ... 134 FIGURE 43NUMBER OF SPECIES FOUND IN DOCUMENTS ... 135 FIGURE 44NUMBER OF POSSIBLE SPECIES PER DOCUMENT REGARDING THE IDENTIFIED PROTEIN NAMES ... 136 FIGURE 45PROPORTION OF POSITIVE AND NEGATIVE INTERACTIONS CREATED DEPENDING OF THE TRANSITIVE VERB CHOSEN AS
TRIGGER ... 138 FIGURE 46PERFORMANCE ON SPECIES RETRIEVAL GIVEN THE NUMBER OF RETURNED SPECIES ... 139 FIGURE 47PERFORMANCE ON PROTEIN MENTIONS IDENTIFICATION GIVEN THE NUMBER OF RETURNED SPECIES ... 140 FIGURE 48DIFFERENCE IN RECALL DEPENDING IF THE CANDIDATE PROTEINS ARE FILTERED GIVEN THEIR BELONGING TO AN INTERACTION PATTERN ... 141 FIGURE 49COMPARISON OF THE RECALL AND PRECISION ON PROTEIN NORMALIZATION GIVEN THE NUMBER OF RETURNED SPECIES
BETWEEN THE DICTIONARY METHOD AND THE USE OF FUZZY MAPPING ... 141 FIGURE 50PERFORMANCE COMPARISONS OF THE DIFFERENT APPROACHES IN BIOCREATIVEII ... 143
Tables
TABLE 1UMLS SEMANTIC TYPES ... 45
TABLE 2DATABASES EMPLOYED TO POPULATE GPSDB ... 46
TABLE 3SIZE OF THE FEATURE SPACE OF THE TRAINING SET WITH AND WITHOUT STEMMING ... 48
TABLE 4 THE 68 WORDS CONSIDERED AS INTERACTOR ... 53
TABLE 5NUMBER OF FEATURES USED TO REPRESENT THE DOCUMENTS IN THE SPECIFIC FEATURE SPACE ... 55
TABLE 6PERFORMANCE OBTAIN BY USING OR NOT A COST MATRIX FOR THE CLASSIFICATION ... 58
TABLE 7NUMBER OF SELECTED FEATURE ON THE TRAINING AND TEST SET FOLLOWING THE FEATURE SELECTION PROCESS ... 59
TABLE 8ACCURACY AND F-MEASURE OBTAIN BY A CLASSIFICATION ON THE FEATURE SPACE OBTAINED FOLLOWING THE FEATURE SELECTION PROCESS ... 59
TABLE 9ACCURACY AND F-MEASURE OBTAIN BY A CLASSIFICATION (LIBSVMC=3,GAMMA=0.009) ON THE FEATURE WEIGHTED USING THE GIVEN WEIGHTING SCHEMA ... 60
TABLE 10SUMMARY OF THE MACRO AVERAGED PERFORMANCE ON THE TRAINING SET ... 61
TABLE 11SUMMARY OF THE MACRO AVERAGED PERFORMANCE ON THE TEST SET ... 61
TABLE 12SUMMARY OF THE ACCURACY ON TRAINING AND TEST SET USING THE DIFFERENT FEATURES FOR THE MODEL... 61
TABLE 13CLASSIFICATION PERFORMANCE ON THE TRAINING SET BASED ON THE PROTEIN COUNT AND INTERACTORS IN THE DOCUMENTS ... 62
TABLE 14CLASSIFICATION PERFORMANCE ON TEST SET BASED ON THE PROTEIN COUNT AND INTERACTORS IN THE DOCUMENTS ... 62
TABLE 15SUMMARY OF THE ACCURACY ON TRAINING AND TEST SET USING THE DIFFERENT FEATURES FOR THE MODEL... 62
TABLE 16CLASSIFICATION PERFORMANCES ON THE TRAINING SET BASED ON THE MIXTURE OF MESH TERM AND SEMANTIC TYPE FEATURES... 63
TABLE 17CLASSIFICATION PERFORMANCES ON TEST SET BASED ON THE MIXTURE OF MESH TERM AND SEMANTIC TYPE FEATURES ... 63
TABLE 18SUMMARY OF THE ACCURACY ON TRAINING AND TEST SET USING THE DIFFERENT FEATURES FOR THE MODEL... 63
TABLE 19CLASSIFICATION PERFORMANCES ON THE TRAINING SET BASED ON THE MIXTURE OF MESH TERM AND INTERACTORS FEATURES... 63
TABLE 20CLASSIFICATION PERFORMANCES ON TEST SET BASED ON THE MIXTURE OF MESH TERM AND INTERACTORS FEATURES ... 63
TABLE 21SUMMARY OF THE ACCURACY ON TRAINING AND TEST SET USING THE DIFFERENT FEATURES FOR THE MODEL... 64
TABLE 22CLASSIFICATION PERFORMANCES ON THE TRAINING SET BASED ON THE MIXTURE OF MESH TERM AND TEXTUAL FEATURES 64 TABLE 23CLASSIFICATION PERFORMANCES ON TEST SET BASED ON THE MIXTURE OF MESH TERM AND TEXTUAL FEATURES ... 64
TABLE 24SUMMARY OF THE ACCURACY ON TRAINING AND TEST SET USING THE DIFFERENT FEATURES FOR THE MODEL... 64
TABLE 25CLASSIFICATION PERFORMANCES ON THE TRAINING SET BASED ON THE MIXTURE OF PROTEIN COUNT AND TEXTUAL FEATURES ... 65
TABLE 26CLASSIFICATION PERFORMANCES ON TEST SET BASED ON THE MIXTURE OF PROTEIN COUNT AND TEXTUAL FEATURES ... 65
TABLE 27SUMMARY OF THE ACCURACY ON TRAINING AND TEST SET USING THE DIFFERENT FEATURES FOR THE MODEL... 65
TABLE 28ORTHOGRAPHIC FEATURES ... 80
TABLE 29SYNTACTICAL FEATURES ... 83
TABLE 30LEXICAL FEATURES ... 84
TABLE 31CLASSIFICATION OF THE SENTENCES OF THE TEST SET USING CROSS VALIDATION ... 86
TABLE 32PERFORMANCE ON THE TRUE INSTANCES GIVEN DIFFERENT MISCLASSIFICATION-COST USING CROSS VALIDATION ... 86
TABLE 33CLASSIFICATION OF THE SENTENCES OF THE TEST SET USING THE MODEL BUILD ON THE TRAINING SET ... 86
TABLE 34PERFORMANCE ON THE TRUE INSTANCES GIVEN DIFFERENT MISCLASSIFICATION-COST USING ON THE TEST SET... 87
TABLE 35COMPOSITION OF THE DIFFERENT SENTENCE SETS ... 87
TABLE 36PERFORMANCE MEASURE OBTAIN BY EXTRACTING THE NON "COMMON"ENGLISH WORDS... 88
TABLE 37PERFORMANCE MEASURES OBTAIN BY EXTRACTING ONLY THE "UNCOMMON"ENGLISH TERMS ... 88
TABLE 38CLASSIFICATIONS RESULTS USING ORTHOGRAPHIC FEATURES ... 92
TABLE 39PERFORMANCE MEASUREMENT WITH THE USE OF SYNTACTICAL FEATURES ... 92
TABLE 40CLASSIFICATION PERFORMANCE ON THE TEST SET USING DIFFERENT CONTROLLED VOCABULARY ... 93
TABLE 41 CLASSIFICATION PERFORMANCE OBTAINED BY MIXING THE FEATURES OF ALL CORPUSES ... 93
TABLE 42SUMMARY OF THE PERFORMANCE OBTAINED ON THE TEST SET GIVEN THE DIFFERENT APPROACHES... 94
TABLE 43PERFORMANCE OBTAINED IN PROTEIN/GENE RECOGNITION USING CRF WITH MORPHOLOGICAL FEATURES ... 107
TABLE 44NUMBER OF CANDIDATE WORD GIVEN THE DIFFERENT CANDIDATE GENERATION APPROACHES ... 107
TABLE 45FREQUENCY OF THE DIFFERENT PATTERN IN THE ENTREZGENE LEXICON ... 110
TABLE 46NUMBER OF GENERATED TERM AND WORDS GIVEN THE VOCABULARY VARIATION ... 110
TABLE 47PERFORMANCE OBTAINED ON THE TRAINING SET DEPENDING OF THE METHOD EMPLOYED TO EXTRACT THE CANDIDATE FROM THE ARTICLES ... 113
TABLE 48PERFORMANCE OBTAINED ON THE TEST SET DEPENDING OF THE METHOD EMPLOYED TO EXTRACT THE CANDIDATE FROM THE ARTICLES ... 113
TABLE 49PERFORMANCE OBTAINED ON THE TRAINING SET GIVEN DIFFERENT VARIANT LEXICON ... 114
TABLE 50RESULT BY KEEPING ALL CANDIDATES ... 115
TABLE 51 SELECTED VERBS REQUIRED TO TRIGGER AN INTERACTION ... 128
TABLE 52PERFORMANCE ON PPI EXTRACTION GIVEN THE TECHNIQUES ... 142
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