- Open Access
Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
- Yaoyun Zhang†1,
- Heng-Yi Wu†2,
- Jun Xu1,
- Jingqi Wang1,
- Ergin Soysal1,
- Lang Li2Email author and
- Hua Xu1Email author
© The Author(s). 2016
- Published: 26 August 2016
Information about drug–drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs.
When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively.
We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure.
- Semantic Information
- Dependency Graph
- Syntactic Structure
- Semantic Class
- Drug Pair
Drug–drug interaction (DDI) is a condition where one drug alters the effect of another drug in a clinically meaningful way . It is well documented to be one of the major causes of adverse drug reaction (ADR) and is thus, a demonstrated threat to public health [2–4]. With increasing rates of polypharmacy , the incidence of DDIs is likely to increase as well. Hence, collecting information about DDIs in a timely manner is critical for reducing ADR and the costs associated with therapy [6, 7]. Although significant efforts have been invested to incorporate DDIs into various data sources, such as DiDB , DrugBank , and pharmacy clinical decision support systems , existing sources suffer from the problems of low coverage , low accuracy  and low agreement .
Under such circumstances, scientific evidence detailing the mechanism/s behind the drug interactions are necessary to provide support for reliable DDI information . To this end, FDA requires in vivo and in vitro DDI studies during new drug development [15, 16]. Since new reports of DDIs are rapidly accumulating in the huge archive of scientific literature , text mining techniques are needed to automatically extract DDIs with support from literature-derived scientific evidence .
A major type of DDI, PK DDI, is a situation wherein one drug affects (inhibits or induces) the absorption, distribution, metabolism, and/or excretion of another drug. Although mechanistic information regarding PK DDI provides important evidence by describing how the interaction between drugs occurs, very few studies have been conducted so far to extract PK DDIs from scientific literature. Currently, most DDI systems are built on the corpus that was used in the two DDI extraction challenges in 2011 and 2013 [18, 19]. However, a large part of this corpus is based on DrugBank. Only 86 DDI relations of PK mechanisms were annotated from Medline. In addition,  attempted to identify PK DDIs from drug package inserts. The texts taken from DrugBank and drug package inserts were manually curated with short and concise sentences, thus providing a brief description of DDIs . In contrast, the scientific language used in literature typically contains long and complex sentences, expressing detailed PK information. Moreover, the content of scientific literature does not necessarily talk about DDIs, making DDI extraction from scientific literature significantly more difficult . Other groups extracted the relation between drugs and enzymes based on properties of drug metabolism; here, potential DDIs were detected by inference and reasoning [22, 23]. The only DDI corpus dedicated to PK evidence derived from literature was built by Wu, Karnik et al. , covering both in vivo and in vitri PK DDI studies.
Promoted by the two DDI extraction challenges in 2011 and 2013 [18, 19], many approaches have been proposed to extract DDIs from biomedical text. The DDI extraction tasks are usually modeled as a classification problem. Machine learning (ML) methods were employed to classify whether the relation between each candidate DDI pair was a true interaction or not. In the existing ML-based systems, two types of methods have been mainly used: feature-based methods and kernel-based methods .
In feature-based methods, each data instance is represented as a feature vector in n-dimensional space. Features are defined to informatively represent the data characteristics of different relation types. Heterogeneous features of different linguistic levels have been employed in DDI extraction systems, including lexical features like negative words, syntactic patterns, semantic types of two drugs and ontology-based concepts [26–30]. In kernel-based methods, data instances are first represented by syntactic structures, using either the syntactic parse tree  or the dependency graph . The similarity between the syntactic structural representations is then computed, as a representative of the similarity between the two instances. Various syntactic representations, similarity functions, and combinations are exploited in existing kernel-based DDI extraction systems [24, 26, 33, 34]. Bui Q-C et al.  leveraged both the syntactic structures and features of sentences, by using different feature lists according to different syntactic structures and achieved the best results on the challenge datasets. Currently, kernel-based methods are dominant and achieved state-of-the-art results for DDI [18, 19]. However, since scientific literature has many long and complex sentences, such approaches are likely to suffer from the sparseness problem of deep syntactic structures .
Also, sophisticated semantic information is rarely explored and employed for DDI. Semantic representations bearing more “compact” and generalized information could potentially normalize the surface form variations of syntactic structures. One important type of semantic information is predicate-argument-structures (PASs) . PAS is a unified form of shallow semantic representation of the sentence, which is generated on the basis of variant syntactic structures . PASs have already been used in various information extraction tasks and have shown promising results [38–40]. Another important type of semantic information is semantic class . Based on the sublanguage theory [42, 43], semantic class is defined as the generic class of essential semantic information in the language of closed domains such as PK DDI, which is independent of the surface syntactic structures . Sematic classes are different from the relatively high level semantic types defined in UMLS , which are currently used for DDI extraction. They are more granular, describing semantic information specific to a closed domain. For example, the word “strongly” in the sentence “Drug1 strongly increases plasma concentrations of oral drug2.” is an instance of the “Degree” semantic class and serves as a potential indication of the degree of PK DDI. However, it is not covered by UMLS as a concept. Many existing systems in different biomedical sub-domains used semantic class for relation extraction via rule-based semantic patterns [45, 46]. Nevertheless, semantic class hasn’t yet been examined for PK DDI extraction using statistical methods.
In this article, we examined the following two types of semantic information for PK DDI extraction from the biomedical text: shallow semantic representation and fine-granular semantic classes based on the sublanguage of PK DDI. All-path graph kernel was employed to statistically integrate different linguistic levels of information, syntactic, shallow semantic and fine-granular semantic class. Our approach differs from existing approaches in two ways. First, we propose a novel all-path graph kernel algorithm using shallow semantic graph, i.e. PAS graph kernel. Second, we statistically incorporate fine-grained semantic classes into dependency graph kernel and PAS graph kernel. Our evaluation results using the PK DDI corpus  demonstrates that our proposed approach significantly improves the performance of the original all-path graph kernel based on dependency structure.
Performance of in vivo PK DDI extraction
Performance for PK DDI extraction on the in vivo dataset
Performance of in vitro PK DDI extraction
Performance for PK DDI extraction on the in vitro dataset
In this study, we examined the contribution of two types of semantic information for PK DDI extraction from literature. The shallow semantic representation, i.e., PAS of one sentence was employed as a novel alternative to dependency based syntactic structural representation in all-path graph kernel. Moreover, fine-granular semantic classes specifically designed as the sub-language for the closed domain of PK DDI were incorporated into dependency graph kernel and PAS graph kernel. Our results showed that both the types of semantic information improved the PK DDI extraction performance. PAS graph kernel outperformed the baseline of dependency graph kernel (in vivo: 77.88 % vs. 75.91 %; in vitro: 67.68 % vs. 51.50 %). Furthermore, integrating semantic classes into graph kernels achieved the optimal performance: dependency graph kernel got the optimal F1 (in vivo 81.94 %; in vitro 69.34 %), and PAS graph kernel yielded the highest precision (in vivo 84.88 %; in vitro 74.83 %). To the best of our knowledge, this is the first study that combines syntactic, shallow semantic and semantic class information into the graph kernel for PK DDI relation extraction.
Performance variations between in vivo and in vitro datasets
Description of refined mechanism semantic classes for literature on PK DDI
The action of a drug on an enzyme
The action of an enzyme on a drug
The action converting a drug to its metabolite
Performance variations of different methods
As illustrated in Table 1 and 2, the PAS graph kernel achieved higher performance than the dependency graph kernel. Specifically in the in vitro dataset, the precision increased from 43.43 % to 73.03 % and F1 increased from 51.50 % to 67.68 %. This validated our assumption that more information to distinguish “DDI” from “NDDI” is covered by the paths of PAS graph kernel. With semantic class integration, the performance of both graph kernels increased. Nevertheless, the performance of dependency graph kernel was increased more sharply than PAS (in vivo: dependency 75.91 % vs. 81.62 %, PAS 77.88 % vs. 80.10 %; in vitro: dependency 51.50 % vs. 65.86 %, PAS 67.68 % vs. 67.83 %). On one hand, semantic information demonstrated its generalization ability to resolve the sparcity problem in syntactic paths. On the other hand, it also indicated that there was a relatively small gap between shallow semantic and semantic class representations of sentences.
As illustrated in Table 1, for the in vivo dataset, the improvement of DEP_ReSC was statistically significant over DEP. The performance of PAS_ReSC was comparable with DEP without statistically significant difference; whereas it dropped significantly from PAS. Moreover, as illustrated in Table 2, for the in vitro dataset, the improvement of DEP_ReSC was statistically significant over DEP; the improvement of PAS_ReSC was also statistically significant over DEP and PAS, respectively. Thus, refining semantic classes of “Mechanism” further enhanced the performance of dependency graph kernel. In contrast, the precision of PAS graph kernel was enhanced significantly by the refined semantic classes (in vivo: 82.91 % vs. 84.88 %; in vitro: 69.23 % vs. 74.83 %), with a severe drop in recall (in vivo: 77.46 % vs. 68.54 %; in vitro: 66.48 % vs. 62.50 %). One possible reason is that PAS graph kernel with refined semantic classes imposed strict constraints to patterns of positive DDIs, resulting in significantly increased precision at the cost of decreased recall.
False positive error analysis of PK DDI extraction
Preincubation of human liver microsomes with dihydralazine in the presence of NADPH resulted in decreases in phenacetin O-deethylase activity (an indicator of P450 1A2 activity) and testosterone 6beta-hydroxylase activity (P450 3A4), but not in diclofenac 4′-hydroxylase activity (P450 2C9), an indication of inactivation of P450s 1A2 and 3A4 during the dihydralazine metabolism.
Relation between drug and its metabolites
In HLMs, cisapride was N-dealkylated to norcisapride (NORCIS) and hydroxylated to 3-fluoro-4-hydroxycisapride (3-F-4-OHCIS) and to 4-fluoro-2-hydroxycisapride (4-F-2-OHCIS).
Because HMR1766 is an inhibitor and warfarin a substrate of CYP2C9, the authors studied whether warfarin pharmacokinetics and pharmacodynamics are influenced by HMR1766.
The inductive effect of CBZ was about 46 % higher than that of OXCZ, a difference that may be of clinical relevance.
Cross-clause in long sentences
Coadministration with ketoconazole (which inhibits CYP3A4) decreased the mean apparent oral clearance of quinine significantly (P < .001) by 31 %, whereas coadministration with fluvoxamine (which inhibits CYP1A2 and to some extent CYP2C19) had no significant effect (P > .05) on the mean apparent oral clearance of quinine.
False negative error analysis of PK DDI extraction
Relations failed to be covered by the shortest path of the graph
… suggesting that the degree of induction of methadone metabolism by nevirapine is similar for both dosing regimens…
Zafirlukast inhibited the hydroxylation of tolbutamide (CYP2C9; mean IC(50) = 7.0 microM), triazolam (CYP3A; IC(50) = 20.9 microM) and S-mephenytoin (CYP2C19; IC(50) = 32.7 microM).
Although erythromycin only modestly decreases lignocaine clearance, it causes a concomitant elevation of the concentrations of its pharmacologically active metabolite MEGX.
Need numerical calculation
Mean CYP2D6 dextromethorphan metabolic ratios before and after fluoxetine therapy were 0.028 +/-0.031 and 0.080 +/- 0.058, respectively (P = .001)…
Rare relation pattern
The estimated K(i) values for CYP2D6-catalyzing dextrorphan formation were ranked in the following order: perphenazine (0.8 microM), thioridazine (1.4 microM), chlorpromazine (6.4 microM), haloperidol (7.2 microM), fluphenazine (9.4 microM), risperidone (21.9 microM), clozapine (39.0 microM), and cis-thiothixene (65.0 microM).
Limitations and future work
A limitation of this work is that currently the employed semantic classes are designed for PK DDIs, which may not be fully generalizable to other types of DDIs. Another limitation is that the current work focuses on recognizing interaction between two drugs. DDI may also be related to other important factors. For example, the existence of the protein NADPH is related to the interaction between dihydralazine and phenacetin in the sentence “Preincubation of human liver microsomes with dihydralazine in the presence of NADPH resulted in decreases in phenacetin O-deethylase activity”. Another important factor is the interaction between the drug and the enzyme, from which DDI relations not expressed explicitly in literature could be inferred. Moreover, drug targets interactions is also an important factor to consider for DDI extraction. If both of two drugs have interactions with the same target, they may have potential synergistic, additive or antagonistic interactions. Such factors would be considered for DDI relation extraction in our next step.
To further improve the performance of DDI relation extraction, a more accurate recognition of the negation expressions needs to be conducted. Whether those negations are modifying the DDI relations also need to be determined. To collect more trigger words for drug enzyme interaction and uncertainty, comprehensive semantic lexicons need to be built by leveraging existing knowledge resources such as UMLS and wordNet. Besides, specific strategy to handle different types of syntactic structures such as cross clauses relations, prepositional/conjunctive structures, and co-reference should be designed. One possible solution may be a hybrid way to combine statistical graph-kernel based methods with heuristic rules-based features, so that to consider simultaneouly the generalizability and specificity of the method.
What’s more, in the original annotation of the PK DDI corpus, DDIs can be further split into two types: certain DDIs with strong evidence and ambiguous DDIs with weak evidence . Refinement of PK DDI relations according to different degrees of evidence will be carried out in our future work, to further leverage information from evidence for DDI recognition.
In this study, two types of semantic information, shallow semantic representation and fine-grained semantic classes, were exploited for PK DDI extraction from biomedical text. All-path graph kernel was employed to statistically integrate different linguistic levels of information, i.e., syntactic, shallow semantic and fine-granular semantic class. Experimental results showed that our proposed approach significantly en-hanced the performance of the original all-path graph kernel based on dependency structure. The F-measure was improved from 75.91 % to 81.94 % on the in vivo dataset and from 51.50 % to 69.34 % on the in vitro dataset, respectively, demonstrating the potential of semantic information for effective PK DDI extraction.
Two PK DDI datasets, consisting of in vivo and in vitro studies respectively, were used in this study. Our method consists of three steps. First, we represent sentences with syntactic structures, shallow semantic relation structures and semantic classes and their combinations. Second, all-path graph kernels describing the syntactic and semantic connections within the sentences are generated from those representations. In the last step, an SVM classifier is trained based on the graph kernels to generate a predictive model, which is used to classify candidate DDI pairs of the test dataset.
The corpus of PK DDI relations built by Wu, Karnik et al.  was employed in this study. The PK DDI relations was manually curated using 428 PK-DDI related abstracts from MedLine . When searching for DDI studies from MedLine, the query “drug-drug interactions” was used by the DDI challenge corpus developers. In contrast, the PK DDI corpus of Wu, Karnik et al.  used additional keywords of probe substrate/inhibitor/inducers for specific metabolic enzymes in queries. The abstracts for annotation were randomly selected from the search results. In comparison with the PK DDIs (i.e., the “mechanism” relation) in the Challenge corpus, the PK DDI corpus is more focused on the co-occurrence of supportive evidence with a true positive DDI relation, such as drug enzyme mechanisms and changes in PK parameters. Furthermore, the abstracts in this corpus were categorized into two datasets for in vivo and in vitro studies, respectively, to accommodate the differences between the two study types. The datasets are described in detail below:
Example sentences with PK DDI from literature
Sentence with DDI
Both erythromycin and itraconazole increased the area under the lignocaine plasma concentration-time curve [AUC(0-infinity)] and lignocaine peak concentrations by 40-70 % (P<0.05).
Rifalazil-25-deacetylation in microsomes was completely inhibited by diisopropyl fluorophosphate, diethyl p-nitrophenyl phosphate and eserine, but not by p-chloromercuribenzoate or 5,5′-dithio-bis(2-nitrobenzoic acid), indicating that the enzyme responsible for the rifalazil-25-deacetylation is a B-esterase.
In vitro PK DDI dataset: 210 abstracts of in vitro PK DDI studies are included in the dataset. Different from in vivo studies, the conduct of in vitro DDI studies is used for determining whether a drug is a substrate, inhibitor, or inducer of metabolizing enzymes. By using in vitro technologies, it can qualitatively provide insight into the potential DDI based on the observation of enzyme kinetics parameters. Along with those PK data, a modeling or simulation approach is applied to describe the mechanism of drug interaction. An example sentence of in vitro PK DDI is displayed in Table 6, in which the metabolism of drug “Rifalazil” is inhibited by “diisopropyl fluorophosphates”, “diethyl p-nitrophenyl phosphate” and “eserine”, respectively.
Statistics of PK DDI datasets
S 1 : The inhibition of P-glycoprotein-mediated tubular secretion of Quinidine by Itraconazole.
The drug names “Quinidine” and “Itraconazole” are replaced with “drug1” and “drug2” before sentence representation.
Dependency graph of a sentence is constructed on its dependency-based syntactic parse structure. It is a directed graph that includes two types of vertices: a word vertex contains its lemma and part-of-speech tags (POS), and a dependency vertex contains the dependency relation between words. In addition, both types of vertices contain their positions, which differentiate them from other vertices. Figure 3a illustrates the dependency graph of S 1 . Since the words connecting the candidate entities in a syntactic representation are particularly likely to carry information regarding their relationship , the labels of the vertexes on the shortest undirected paths connecting drug1 and drug2 are differentiated from the labels outside the paths using a special tag “IP”. Further, the edges are assigned weights; all edges on the shortest paths receive a weight of 0.9 and other edges receive a weight of 0.3 as in . Thus, the shortest path is emphasized while also considering the other words outside the path as potentially relevant.
Shallow semantic graph
Shallow semantic graph uses predicate-argument structures (PASs) as shallow semantic representation of the sentence . A predicate usually refers to a word indicating a relation or an attribute, and arguments refer to syntactic constituents with different semantic relations to the predicate . For example, the preposition “by” in S 1 is one predicate, “the inhibition of P-glycoprotein-mediated tubular secretion of drug1” is ARG1, representing the action being executed (denoted as byarg1), and “drug2” is ARG2, representing the executor of the inhibition (denoted as byarg2). Normalized PAS can be extracted from different surface textual forms by shallow semantic parsing .
The PAS employed in this study is defined by the Sign-based Construction Grammar . The PAS graph is generated in the similar way as the dependency graph, except that the dependency vertex is replaced with a PAS vertex containing the relation between a predicate and its argument. If an argument is a phrase, an edge is connected from the predicate to the headword of the argument phrase. The PAS graph of S 1 is illustrated in Fig. 3b. The shortest PAS path connecting drug1 and durg2 is “Inhibition of secretion of drug1 by drug2”; while the shortest dependency path is “drug1 by drug2” as shown in Fig. 3a. Dependency graph fails to include this critical information regarding DDI’s shortest path, when two drugs are connected by prepositional structures. In contrast, PAS graph can cover such information more comprehensively.
Semantic class annotation
Semantic class description for literature of PK DDI
Dose, sample size, values of PK parameters
Trigger words related to DDI mechanisms
Change of PK parameters
Severity of PK parameter change
S 2 : Drug1 inhibits the CYP2C19 -catalyzed 4-hydroxylation of drug2.
Here, “inhibits”, “catalyzed” and “4-hydroxylation”can be categorized into mechanisms of “Drug-enzyme”, “Enzyme-drug” and “Drug-metabolite”, respectively.
All-path graph kernel
A graph kernel calculates the similarity between two input graphs by comparing the relations between common vertices. The weights of the relations are calculated using all possible paths between each pair of vertices. Our method follows the all-paths graph kernel proposed by Airola et al. . The kernel represents the target pair using graph matrices based on two sub-graphs. The first sub-graph represents the structure of a sentence. Dependent on the type of structure representations of a sentence, two types of all-path graph kernels are employed in this study: (1) Dependency graph kernel, which is employed in the original all-path graph kernel, uses the dependency graph to represent sentence structure in the syntactic level; (2) PAS graph kernel, is a novel graph kernel defined in this study and uses the PAS graph to represent sentence structure at the shallow semantic level. Furthermore, semantic classes, representing the sentence content at a fine-grained semantic level, can be integrated into both dependency and PAS graph kernels by replacing the word vertices with semantic class vertices. As an illustration, Fig. 3c displays the dependency graph integrated with semantic classes of S 1 . The second sub-graph represents the word sequence in the sentence, and each of its word vertices contains its lemma, its relative position to the target pair and its POS; all edges receive a weight of 0.9 as in  (see Fig. 3d).
Machine learning algorithm
Support vector machine (SVM) algorithms are the dominant ML methods (Segura-Bedmar et al., 2013) among the existing DDI systems. Our study used the sparse version of RLS, also known as the least squares SVM, to learn the DDI prediction model based on the all-path graph kernel .
POS-tags and dependency trees of the datasets were generated using the Stanford parser ; PASs were generated by Enju , a deep parser based on a wide-coverage probabilistic HPSG grammar . The semantic classes were annotated using pre-built lexicons and regular expressions . Candidate drug pairs with two identical drugs were removed from the training and test datasets.
We used the standard evaluation measures (Precision, Recall and F-measure) proposed by the DDI extraction challenge  and employed previously on the same PK DDI dataset used in our study by  to evaluate the performance of our system.
The package of the all-path graph kernel algorithm provided in  was employed in our experiments. Built on the lease squares SVM, this package provides configuration options for some SVM parameters, as well as graph kernel related parameters. In addition, to find the optimal threshold for prediction in the generated model, a leave-one-document-out cross validation function is provided. Thus, cross-validations were first conducted on the training datasets. Relation extraction models were then built on the training datasets, using the optimal thresholds for prediction. The performance on test datasets was evaluated using those models and reported. Currently, data vectors were created without normalization, which dropped the performance in our pilot study; 500 basis vectors were used for model building. For graph kernels, all edges on the shortest paths received a weight of 0.9 and other edges received a weight of 0.3. For the word sequence based kernel, all edges received a weight of 0.9.
Graph kernels of syntactic and shallow semantic representations: dependency graph kernel (DEP) and shallow semantic graph kernel, i.e., PAS graph kernel were employed in this study, as described in the METHODS section. The dependency graph kernel, which was used in the original all-path graph kernel , served as the baseline in this study. The difference in performance between the syntactic and shallow semantic graphs was examined.
The combination of graph kernels with semantic class: To evaluate the effect of semantic class (SC), it was incorporated into each graph kernel, as described in the METHODS section.
Different granularities of the “Mechanism” semantic class: In order to check whether differentiating among distinct mechanisms would influence the performance, the refined semantic classes of “Mechanism” as defined in Table 3 were incorporated into graph kernels, along with other semantic classes (ReSC).
For systematic analysis, pairwise t-tests were conducted between the results of all proposed methods and the baseline method (DEP). Besides, pairwise t-tests were also conducted between the results of PAS_SC/PAS_ReSC and PAS, to examine the improvement of incorporating semantic class information with PAS. The statistical significance (p-value < 0.05) of the proposed methods was evaluated both on the in vivo and in vitro datasets. Furthermore, using scores output by the prediction models as thresholds, ROC curves of the implemented methods were also constructed for the in vivo and in vitro datasets, respectively.
This work was supported by Cancer Prevention & Research Institute of Texas [R1307]; GM10448301, and LM011945
The publication costs for this article were funded by the corresponding author.
This article has been published as part of BMC Systems Biology Volume 10 Supplement 3, 2016: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM) 2015: systems biology. The full contents of the supplement are available online at http://bmcsystbiol.biomedcentral.com/articles/supplements/volume-10-supplement-3.
Availability of data and materials
The data and source code of DDI relation extraction are freely available at https://sbmi.uth.edu/ccb/resources/ddi.htm.
YZ, HW, LL and HX were responsible for the overall design, development, and evaluation of this study. LL, YZ and ES developed the annotation guidelines and annotated the data set used for this study. JX and JW worked with YZ on the algorithm development. YZ and HX did the bulk of the writing, and LL also contributed to writing and editing of this manuscript. All authors reviewed the manuscript critically for scientific content, and all authors gave final approval of the manuscript for publication.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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