Hash Subgraph Pairwise Kernel for Protein-Protein Interaction Extraction

Abstract

Extracting protein-protein interaction (PPI) from biomedical literature is an important task in biomedical text mining (BioTM). In this paper, we propose a hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the novel kernel is to use the hierarchical hash labels to express the structural information of subgraphs in a linear time. We apply the graph kernel to compute dependency graphs representing the sentence structure for protein-protein interaction extraction task, which can efficiently make use of full graph structural information, and particularly capture the contiguous topological and label information ignored before. We evaluate the proposed approach on five publicly available PPI corpora. The experimental results show that our approach significantly outperforms all-path kernel approach on all five corpora and achieves state-of-the-art performance.

Systematic identification of latent disease-gene associations from PubMed articles

“…Also, by including complementary gene-disease association data resources, we anticipate to increase the prediction accuracy in future research. We expect to extend our work in several related research topics, including (1) integration of additional supervised information (e.g., key words for PubMed abstracts) to make LDA generate more controllable and interpretable topics [ 62 – 64 ]; (2) integration of more comprehensive association databases among disease, drug, and gene (e.g., HPRD [ 65 ] and DrugBank [ 66 ]) to construct more complete base association networks; (3) a framework to automatically extract such disease-specific association network so that such analysis can be extended to each disease topic; (4) additional network-based investigation of the relationships among disease, drug, and gene at other network levels such as module subnetwork identification; and (5) investigation on possible ways to improve the network by assigning weights or confidence values to different types of associations or associations from different sources.…”

Integrating experimental and literature protein-protein interaction data for protein complex prediction

“…The biomedical literature contains lots of potentially valuable PPI data, and extraction of this data is an important research topic in the field of biomedical natural language processing [ 15 , 16 ].…”

“…Instead, publicly accessible annotated PPI corpora such as GENIA [ 18 ] and AImed [ 19 ] allow automatic extraction of PPI data using machine learning methods. Recent studies [ 15 , 16 ] have established the power of machine learning methods, which handle PPI extraction as a classification problem. The major challenge is in supplying the learner with the semantic/syntactic information-containing features in order to distinguish between interactions and non-interactions.…”

“…Kernel methods can efficiently compute the similarity between structural data in a recursive manner without explicitly enumerating with feature vectors, avoiding complex feature construction and selection processes. We use the hash subgraph pairwise (HSP) kernel method to extract PPI data from biomedical literature, as proposed in our previous work [ 15 ]. HSP kernel methods compute hierarchical hash labels of syntactic structure based on hash operations in a linear time.…”

“…Basic labels represent the lexical features and hash labels represent the complex syntactic features. In our previous work, we have demonstrated the advantages of the HSP kernel method over other popular machine learning methods [ 15 ].…”