Disease Network Constructor: a Pathway Extraction and Visualization (2023)

Abstract: We present Disease Network Constructor (DNC), a system that extracts and visualizes a disease network, in which nodes are entities such as diseases, proteins, and genes, and edges represent regulation relation. We focused on the disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis (IPF). The front-end web-base user interface of DNC includes two-dimensional (2D) and 3D visualizations of the constructed disease network. The back-end system of DNC includes several natural language processing (NLP) techniques to process biomedical text including BERT-based tokenization on the basis of Bidirectional Encoder Representations from Transformers (BERT), flat and nested named entity recognition (NER), candidate generation and candidate ranking for entity linking (EL) or, relation extraction (RE), and event extraction (EE) tasks. We evaluated the end-to-end EL and end-to-end nested EE systems to determine the DNC’s back-endimplementation performance. To the best of our knowledge, this is the first attempt that addresses neural NER, EL, RE, and EE tasks in an end-to-end manner that constructs a path-way visualization from events, which we name Disease Network Constructor. The demonstration video can be accessed from https://youtu.be/rFhWwAgcXE8 . We release an online system for end users and the source code is available at https://github.com/aistairc/PRISM-APIs/ .

## introduction in the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks. the task formulation of dnc involves several natural language processinbg (nlp) techniques. dnc is mainly composed of five core models: a bidirectional encoder represenatation from transformers (bert)-based masked language model @xcite , named entity recognition (ner) model @xcite ) that enumerates all possible spans as potential entity mentions and classifies them into entity types, entity linking (el) model @xcite ) that executes candidate generation and candidate ranking, relation extraction (re) model @xcite , and event extraction (ee) model @xcite . dnc provides a web-based user interface to facilitate the end-to-end process of neural el and deep ee on the basis of these five models without any training required by end users. the interface visualizes the 2d and 3d networks on the basis of output of el to ee. ## dnc: back-end system the bert-based back-end system of dnc is built upon four layers: we employ the modeling of deep ee @xcite on the basis of entity, relation, and event over the ipf dataset @xcite , which is a manually annotated corpus of ipf-related literature. we further extend the end-to-end deep ee model by leveraging the el @xcite model to construct a disease network. figure 1 shows an overview of dnc workflow. ## disease network constructor dnc provides a graph of disease network from the event statistics of ipf. the graph is generated by first applying el and ee to each input text, then repeatedly collapsing regulation events and their consequents, marking the resultant event with the sign of the regulation event (positive or negative). the resulting graph represents entities as nodes, and regulated events as edges. we define a "regulation events" as any events with one of the following types: positive_regulation, negative_regulation, or regulation. regulation describes a regulation event for which it is not clear whether its effect is positive or negative, and a regulation sign r is defined as +1, -1, and 0, respectively. a regulation event's disorder or cause roles are considered antecedents, and denoted as a; theme roles are consequents, denoted as c. the application collapses regulation events in the following manner. for each non-negated event e 0 of type t 0 and regulation sign r 0 , for each antecedent-consequent pair (a 0 , c 0 ) where a 0 is an entity (or (null), if e 0 has no antecedents): if c 0 is also an entity, we generate a direct_regulation(a 0 , c 0 , r 0 ) edge for e 0 . if c 0 is a non-regulation event of type t 1 , for each of its theme arguments c 1 , we generate a t 1 (a 0 , c 1 , r 0 ) edge for e 0 . if c 0 is a regulation event, the data (a 0 , e 0 , c 0 ) will be remembered as an uncollapsed regulation link. after each event is processed as above, we iteratively collapse uncollapsed regulation links until it is no longer possible. we look for an uncollapsed regulation link intuitively speaking, each regulation event is "folded into" its consequent as its sign. for example, a "negative regulation" of a "gene expression" becomes a "negatively regulated gene expression" edge, connecting the cause of the "negative regulation" with the theme of the "gene expression". if there is a chain of multiple regulation events, their signs interact: a "negative regulation" of a "negative regulation" of a "gene expression", for example, becomes a "positively regulated gene expression" edge. this algorithm results in many events not being included on the graph. since nodes are entities, any regulation event the antecedents of which are not entities are ignored. similarly, any regulation events that lacks an antecedent or a consequent, as well as any events that do not have a theme, will not be included, as there cannot be an edge without both a source and a target node. any negated events, and any entities that do not participate in at least one edge are also left out. ## experimental settings in this section, we evaluate the dnc system based on the ipf dataset @xcite . ## results recent successes in neural networks have shown impressive performance gains on many nlp applications including ner @xcite @xcite @xcite , el @xcite , re @xcite , and ee @xcite . in contrast, other approaches have emphasized end-to-end el @xcite , end-to-end re @xcite or even end-to-end ee @xcite to facilitate biomedical information extraction tasks. there have been no studies on an all-in-one neural end-to-end approach to facilitate biomedical research, especially to construct disease network pathways that visualize the events along with entity normalization. we addressed this gap by introducing two end-to-end approaches: el and deep ee to construct a disease network based on ipf, hoping that the dnc can bring insights in making scientific discovery. current nlp techniques often use an event representation data format called the "standoff format" to represent their results. @xcite proposed and discussed a software scheme to convert nlp event representations to standard biomedical pathway data formats (sbml and biopax). apart from neural end-to-end modeling, we integrated brat visualization panels for event representation of the context. there are several web-based tools exist that support the retrieval of biomedical information using text mining. sohrab et al. (2020a) many works have shown considerable attention to boost the ee performances. previous neural models on flat or non-nested ee have been mainly focused on event trigger and argument de-tection @xcite @xcite . besides, deep neural networks including recurrent and convolutional neural networks (cnns) have boosted ee performance @xcite . these models show better performance than traditional hand-crafted featurebased approaches @xcite @xcite . in addition, there are a few end-to-end models @xcite to extract flat events on flat entities; none of these models can treat nested events on nested entities that may further overlap with event triggers. in contrast, @xcite introduced an end-to-end neural nested ee model which detects nested entities and triggers, roles, nested events; and achieved the new state-of-the-art performance on seven biomedical nested event extraction tasks. in our dnc, we employ the modeling of deep ee @xcite to detect the flat and nested events over the ipf dataset. ## conclusion we present disease network constructor (dnc) to address end-to-end el and end-to-end deep ee in order to identify and visualize the specific substances (such as proteins) that work differently from those in the healthy state of human bodies. dnc provides an interactive web-based user interface https://biomed-text.airc.aist. go.jp/disease_network/ for enabling realtime visualization and extracting graph information in different formats for end users. we will continue to improve dnc as well as implement new 2d and 3d graph functions to facilitate biomedical research. moreover, the applicability of this system can be extended to lung diseases such as covid-19 because some entities and events of the ipf dataset are also related to such diseases.