Predicting Drug Responses by Propagating Interactions Through Text-Enhanced Drug-Gene Networks
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Predicting Drug Responses by Propagating Interactions through Text-Enhanced Drug-Gene Networks Shiyin Wang Massachusetts Institute of Technology shiyinw@mit.edu ABSTRACT response analysis. At that time, researchers have already cast inter- Personalized drug response has received public awareness in recent ests in the variation of personal drug response. However, because years. How to combine gene test result and drug sensitivity records of the limitation of data collection and analysis models, precision is regarded as essential in the real-world implementation. Research medicine did not arouse widespread concern until these decades[12]. articles are good sources to train machine predicting, inference, Recent years, integrating big data, including clinical data, genetic reasoning, etc. In this project, we combine the patterns mined from data, genomic data, intervention history, etc., has received expecta- biological research articles and categorical data to construct a drug- tions for facilitating characterization of side effects and predicting gene interaction network. Then we use the cell line experimental drug resistance. records on gene and drug sensitivity to estimate the edge embed- Network Science results are applied to the data of gene expres- dings in the network. Our model provides white-box explainable sion profiles by integrating personalized data to predictive records predictions of drug response based on gene records, which achieve by similarity analysis, which reveal the hidden correlations of un- 94.74% accuracy in binary drug sensitivity prediction task. observed patterns[11, 24]. Intuitive speaking, people sharing sim- ilar genetic patterns and clinical treatments tend to show similar CCS CONCEPTS drug responses. Network-based approaches reveal potential drug- biomarker correlations effectively for some diseases. However, cur- • Information systems → Retrieval tasks and goals; Informa- rent methods in this aspect are limited in the correlation modeling tion extraction; of the network, and the data accessed is limited in the structured data. KEYWORDS Data Mining communities have been trying to discover, identify, network science, data mining, drug response structure, and summary relationships between biological entities through various text mining techniques across open-access bio- 1 INTRODUCTION logical research publications[17, 19, 20, 22]. One of the drawbacks Precision medicine depends on discovering the correlations and ca- of this approach is the accuracy of the results is not high enough sualties between observed data(gene, past clinical records, etc) and to play a deterministic role in the decision process. Instead, they unobserved future performance(side effects, drug effects, immune are provided to the human experts as an additional reference to response, etc). Categorical relations have been collected from experi- fasten the knowledge discovery process. On the other hand, Re- ments, while more complex meta-pattern information is mined from cursive Neural Networks has shown its power to deal with text scientific publications. To bridge the gap between these two types data[9], demonstrating considerable success in numerous tasks such of data collecting sources, we propose a relation embedding method as image captioning[7, 14, 26] and machine translation[2, 5, 10]. to expand the representation space of the relationships between This technique can be applied to convert text meta-patterns into genes and drug responses. To the best of my knowledge, this is the trainable embeddings to represent the relations among entities. first approach to combine text patterns into the existing network. Nodes in the network represent to drug responses(sensitivities, side 3 MODEL effects, etc.) and genes. In this section, we make definitions and formalize the problem. The arXiv:1906.08089v1 [cs.SI] 19 Jun 2019 The model pipeline consists of two parts. First, we construct a whole project is consist of two parts: constructing network skeleton relation network of gene and drug response from existing format- and inference edge embedding. ted data sources. In the same time, we mine data from scientific In the first part, we first collected all the entities Definition. 1 publications automatically, retrieving meta-patterns indicating the from multiple data sources. Then we mined the categorical rela- relations between gene and drug responses. Finally, a novel graph tionships and descriptive relationships Definition.2. Patterns are convolutional neural network based method is applied to balance extracted based on the discovered entities and relationships. After the information collected in the two types of data sources. that, meta-patterns are extracted from descriptive patterns to be a high-level representation of entities relations. Regarding entities 2 RELATED WORKS as nodes in the network, we add edges between two nodes if they Drug discovery plays an essential role in the improvement of hu- appear to have either structured or descriptive relationships. man life[8] as the pharmacology had become a well-defined and Definition 1 (Entity). Entities, denoted by e, refer to the names respective scientific discipline. Dating back to the 20th century, of chemicals, genes, or diseases which can be indexed by a MESH id or researchers across distinct disciplinary areas, including analytical Entrez id. For example, SAH is a disease entity with MESH id D013345. chemistry and biochemistry, demonstrated the values for the drug Definition 2 (Relation). Relationships, denoted by r, represent Algorithm 1 Representation Learning the latent interactions between two entities ei , ej . Relations can be for Epoch 1; 2;:::; N do either categorical(such as “blocker", “binder") or descriptive(such as for Iteration 1; 2;:::;T do “With the increase of ei , there is a significantly drop of ej expression Calculate vˆi = G¹vi ;bi ; λi ; Rei;ej º by Equation. 1 level"). Calculate L by Equation. 5 Update v v − α @L Definition 3 (Pattern). The pattern, denoted by ¹r; ei ; ej º, is i i @vi @L a tuple of one relationship and two entities. Patterns are categorical Update bi bi − α @bi or descriptive concerning the type of the corresponding relationships. @L Update λi vi − α Generally, patterns are accessed by directly parsing datasets. Meta- @λi end for Patterns are the simplified version of descriptive patterns. Calculate vˆi = G¹vi ;bi ; λi ; Rei;ej º by Equation. 1 In the second part, we estimate the representations of nodes Calculate L by Equation. 5 @L and edges Definition. 4. We model the estimated representation of Update R ; v − α ei ej i @R ; v is calculated by the average neighbor entities to multiple edge ei ej i end for representations as Equation. 1. Definition 4 (Edge Representation and Node Representa- tion). Edge representation, denoted as Rei;ej 2 M, is the quantified version of relationship defined on a function family. In this project, and gene druggability information from 30 different sources. Figure ?? shows the frequency of drug-gene interaction types in DGIdb, because of the computation limitation, we set M = Rk×k to be a k showing a long-tailed distribution. The inhibitor occurs 9982 times matrix of size k × k. Then nodes are represented as a vector vi 2 R , in DGIdb, which is 40.79% of the whole interaction records. The bias bi , and scale λi . The estimated value of vi is given by: second most frequent interaction is agonist, which occurs 5333 λ i Õ times and accounts for 21.79%. There are 30 types of relationships. vˆi = Rei;ej vj + λibi (1) jN ¹ei ºj ej 2N ¹ei º We assume the founded structured relations are correct and precise. Having built the network skeleton, we then need to learn the 4.2 Mining Descriptive Relationship representation Rei;ej , λi , bi , vi of edges and nodes. In the datasets, researchers record TPM (transcripts per million) PubMed abstracts are a good source to obtain descriptive patterns. to quantify genes and use intensity to measure drug sensitivity. We investigated a subset of annotated PubMed data by PubTator[23]. To convert representations into a numeric format, we deploy a We selected all the sentences that contain two annotated chemicals fully connected neural network with one hidden layer of size 2 and or genes. The total size of the file is approximate 2 gigabyte, which sigmoid activation. is too complex to take the original long sentences into the model. The first reason is that many patterns may occur a few timesif ui = S¹vi º (2) we use sentences to model the relations directly. Secondly, our We define the loss function as: computation power does not have enough memory to process large 1 Õ graphs. Therefore, we further process our descriptive patterns to L = ¹uˆ − u º2 (3) дene N i i short representations, which we call meta-patterns. дene i 2Gene 1 Õ 2 Lchem = ¹uˆi − ui º (4) 4.3 Extract Meta-Patterns Nдene i 2Chemical For decades, numerous biological researchers describe their find- L = Lдene + βLchem (5) ings on interactions between chemicals, drugs, genes, etc., by natu- ral languages. The adequate biological research literature provide 4 IMPLEMENTATION a good source to extract the information using information re- 4.1 Collecting Structured Data trieval methods[1, 16, 18]. We collected 3614796 abstracts from PubMed and used the PubTator dataset to discover all the named Drug-Gene interactions are well studied in the past biological re- entities in chemicals, diseases, and genes. Meta pattern[1, 21] is searches, such as CancerCommons, CF:Biomarkers, CGI, NCI, Drug- defined as a frequent, informative, and precise subsequence pat- 1 Bank, PharmGKB, DoCM, etc. SIDER provides adverse drug re- tern in certain context. The first step of meta-pattern discovery is actions (ADRs) representing drug responses, which contains 1430 context-aware segmentation, which estimates the contextual bound- drugs, 5880 ADRs and 140,064 drug-ADR pairs. The Cancer Thera- aries of meta-patterns. We use the method developed by Wang[21], 2 peutics Response Portal (CTRP) links genetic, lineage, and other which extracted relationships from a subset of the abstracts on the cellular features of cancer cell lines to small-molecule sensitivity PubMed[6] with the semi-supervise from CTD database3.