Atminter

##The @MInter microbial interaction identification system

###1. Core components

Pretrained SVMs

data/SVMs/core_trained_svm.p pickled SVM object for use in svm_scanner.py. Trained on only interactions involving Escherichia coli and Lactobacillus acidophilus data/SVMs/full_trained_svm.p pickled SVM object for use in svm_scanner.py. Trained on entire corpus

SVM Tools

SVM/svm_scanner.py

SVM script. Takes in a directory of JSON files (format in 2.) returns a tagged JSON ("ABHT" field) for each file. 1 == interaction detected, 0 == no interaction detected. Refer to script help for more details.

SVM/svm_train.py

Training script. Takes in an annotated corpus file (.ann), produces a trained, pickled SVM from that corpus.

Pattern Scanner

patternScan/patternScan.py

Pattern Scanner script. Takes in a directory of JSON files (format in 2.) returns a tagged JSON ("ABHT" field) for each file. If an interaction is detected, a list of all patterns signifying the interaction is inserted into the "ABHT" field. Refer to script help for more details.

Annotated Corpus

Annotated abstracts/annotations for @SVM training

/data/train_test_data/lactobacillus_acidophilus#escherichia_coli.ann Core dataset

/data/train_test_data/collated_train.ann Extended dataset: training ready

/data/train_test_data/collated_annotations.tar.gz Extended dataset

###2. Quickstart

####2.0 Data acquisiton (currently undocumented)

#####2.0.1 Acquiring data for processing

Provide a TSV of species 2-tuples of the following format and save.

Species_1	Species_2
Species_1	Species_2

Execute /src/main/scripts/pubcrawl.py on TSV

Sample command

./src/main/python/scripts/pubcrawl.py <filepath> <your_email> -c <corecount> -o <output directory>

####2.1 SVM use

#####2.1.1 Using a pretrained SVM

Sample command

./src/main/python/SVM/svm_scanner.py data/svms/full_trained_svm.p data/example/svm_test -o data/example/svm_test_output/

svm_scanner.py uses a pretrained SVM (full_trained_svm.p) to analyze data in data/example/svm_test. Output results to data/example/svm_test_output/ as JSON files.

#####2.1.2 Training an SVM for @MInter

./src/main/python/SVM/svm_train.py data/example/svm_train/lactobacillus_acidophilus#escherichia_coli.ann -o data/example/svm_train_output/core_svm.p

svm_train.py uses annotated data (lactobacillus_acidophilus#escherichia_coli.ann) to train an SVM. Outputs SVM as data/example/svm_train_output/core_svm.p.

####2.2 Pattern Scanner

#####2.2.1 Pattern Scanner use

./src/main/python/patternScan/pattern_scan.py data/example/pattern_test -o data/example/pattern_test_output/

pattern_scan.py analyzes data in data/example/pattern_test using precompiled patterns and outputs to data/example/pattern_test_output/

###3. Input format

The @MInter system, as input, uses the following files:

####JSON

filename:

Species_1#Species_2.json

Contents:

A sample file is included in data/train_test_data/lactobacillus_acidophilus#escherichia_coli.json

{"SUMMARY":
	{
		"INT": <bool>, #Interaction between the two species
		"NEG": <bool>, #Negative interaction between the two species
		"POS": <bool>, #Positive interaction between the two species
}
{"PAPERS":[			#List of paper dictionaries
	{
		"PMID":<str>,	#Paper PMID
		"TI":<str>,		#Paper Title
		"AB":<str>,		#Paper Abstract
		"TIHT":<str>,	#Depricated
		"ABHT":<str>,	#Sentence detected if pattern found (Patternscan); Numeric value depending on interaction (SVM)
		}, 
]}

}

####Annotation files

filename:

Filename.ann

contents

File containing annotated abstracts for ML training. Consists of line triplets for each paper with truth value, title text and abstract text on lines i, i+1 and i+2 respectively.

A sample file is included in data/train_test_data/lactobacillus_acidophilus#escherichia_coli.ann

###4. Dependencies

• Python 3 (>=3.4.1)
• segtok (>=1.3.0.0)
• scipy (>=0.15.1)
• nltk (>=3.0.2)
• scikit-learn (>=0.16.1)
• numpy (>=1.10.4)
• Biopython (>=1.65)