Bio-Medical Text Mining with Machine Learning
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1 Sumit Madan Department of Bioinformatics - Fraunhofer SCAI Textual Knowledge PubMed Journals, Books Patents EHRs
2 What is Bio-Medical Text Mining? Phosphorylation of glycogen synthase kinase 3 beta at Threonine, 668 increases the degradation of amyloid precursor protein Biological Expression Language (BEL) BEL Functions Namespace Identifiers Relationship Entity Definitions 2
3 Biological Entity Classes Protein / Gene - HGNC - ENTREZGENE - UNIPROT - MGI - RGD - Chemical - MESH - ChEBI - ChEMBL - Small RNA - MIRBASE - pirnabank - Disease - MESH - DO - ICD10 - Further Classes - Organism (NCBITaxonomy) - Anatomy (UBERON) - Phenotype (HP) - Biological Process (GO) - Cell (CL) - Cell lines (CLO) - Several clinical features - Bold: Entity Class Normal: Controlled Vocabulary 3
4 Several Spelling Variants (Synonyms) for One Single Concept AD Over 28 Synonyms available just in Experimental Factor Ontology (EFO). Presenile Dementia Alzheimer s Dementia Alzheimer s Disease (EFO: ) Alzheimer s Disease (EFO: ) DAT Alzheimer s disorder Source: 4
5 Biological Relations (Example) Source: PMID: Interestingly, BLP also activates caspase 1 through TLR2, resulting in proteolysis and secretion of mature IL-1beta. Entity Entity Entity p(hgnc:tlr2) increases cat(p(hgnc:casp1)) cat(p(hgnc:casp1)) increases p(hgnc:il1b) Subject Relationship Type Object 5
6 Relation Extraction Workflow based on Convolutional Neural Network Interestingly, BLP also activates caspase 1 through TLR2, resulting in proteolysis and secretion of mature IL-1beta Named Entity Recognition (NER) Interestingly, BLP also activates caspase 1 through TLR2, resulting in proteolysis and secretion of mature IL-1beta Pre-processing Interestingly, BLP also activates ENTITY-1 through ENTITY-2, resulting in proteolysis and secretion of mature IL-1beta (Source: PMID: ) Sentence-matrix creation (usage of Word2Vec model) Subject/object detection model Get Associations Merge Relation Association detection model Relationship type detection model Ali, M., Madan, S., Fischer, A., et al. (2017) Automatic Extraction of BEL-Statements based on Neural Networks, Proc. BioCreative VI Chall. Work. 6
7 Multichannel Convolutional Neural Network (CNN) Architecture Interestingly, BLP also activates ENTITY-1 through ENTITY-2, resulting in proteolysis and secretion of mature IL-1beta (PMID: ) Embedding layer Convolution and max-pooling Max-pooling Fully-connected and classification X^x^ Predictions Ali, M., Madan, S., Fischer, A., et al. (2017) Automatic Extraction of BEL-Statements based on Neural Networks, Proc. BioCreative VI Chall. Work. 7
8 Larger Workflow for Relation Extraction (Project BELIEF) Natural Language Processing Named Entity Recognition Rule-based & Machine Learning (CRF, NN) TXT SD SD etc. etc. SD, POS Tagger etc. ProMiner ProMiner JProMiner Entity Unifier Relations Relations Merger & Writer ProMiner ProMiner SimpleRE, TEES etc. Relation extraction Machine Learning (SVM, NN) Madan, S., Hodapp, S., Senger, P., et al. (2016) The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track, Database, 2016, baw
9 BELIEF Dashboard Functionalities of Web-based Curation Interface Project and Document Management Export curated document User Authentication Document Curation View (visualizes text mining results and also integrates several semantic search tools) 9
10 Outlook Usage of bidirectional recurrent neural networks (BiLSTM) Use more training data from different tasks (such as BioNLP, and also BioCreative) Create further models to predict biological functions Automate hyper-parameter optimization Train new optimized word2vec models (use PubMed, PMC & ontologies) Build hybrid systems: machine learning + rules-based system (UIMA Ruta) 10
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