BioPREP: Deep learning-based predicate classification with SemMedDB

A hybrid algorithm for clinical decision support in precision medicine based on machine learning

PURPOSE

The objective of the manuscript is to propose a hybrid algorithm combining the improved BM25 algorithm, k-means clustering, and BioBert model to better determine biomedical articles utilizing the PubMed database so, the number of retrieved biomedical articles whose content contains much similar information regarding a query of a specific disease could grow larger.

DESIGN/METHODOLOGY/APPROACH

In the paper, a two-stage information retrieval method is proposed to conduct an improved Text-Rank algorithm. The first stage consists of employing the improved BM25 algorithm to assign scores to biomedical articles in the database and identify the 1000 publications with the highest scores. The second stage is composed of employing a method called a cluster-based abstract extraction to reduce the number of article abstracts to match the input constraints of the BioBert model, and then the BioBert-based document similarity matching method is utilized to obtain the most similar search outcomes between the document and the retrieved morphemes. To realize reproducibility, the written code is made available on https://github.com/zzc1991/TREC_Precision_Medicine_Track .

FINDINGS

The experimental study is conducted based on the data sets of TREC2017 and TREC2018 to train the proposed model and the data of TREC2019 is used as a validation set confirming the effectiveness and practicability of the proposed algorithm that would be implemented for clinical decision support in precision medicine with a generalizability feature.

ORIGINALITY/VALUE

This research integrates multiple machine learning and text processing methods to devise a hybrid method applicable to domains of specific medical literature retrieval. The proposed algorithm provides a 3% increase of P@10 than that of the state-of-the-art algorithm in TREC 2019.

An automatic hypothesis generation for plausible linkage between xanthium and diabetes

There has been a significant increase in text mining implementation for biomedical literature in recent years. Previous studies introduced the implementation of text mining and literature-based discovery to generate hypotheses of potential candidates for drug development. By conducting a hypothesis-generation step and using evidence from published journal articles or proceedings, previous studies have managed to reduce experimental time and costs. First, we applied the closed discovery approach from Swanson's ABC model to collect publications related to 36 Xanthium compounds or diabetes. Second, we extracted biomedical entities and relations using a knowledge extraction engine, the Public Knowledge Discovery Engine for Java or PKDE4J. Third, we built a knowledge graph using the obtained bio entities and relations and then generated paths with Xanthium compounds as source nodes and diabetes as the target node. Lastly, we employed graph embeddings to rank each path and evaluated the results based on domain experts' opinions and literature. Among 36 Xanthium compounds, 35 had direct paths to five diabetes-related nodes. We ranked 2,740,314 paths in total between 35 Xanthium compounds and three diabetes-related phrases: type 1 diabetes, type 2 diabetes, and diabetes mellitus. Based on the top five percentile paths, we concluded that adenosine, choline, beta-sitosterol, rhamnose, and scopoletin were potential candidates for diabetes drug development using natural products. Our framework for hypothesis generation employs a closed discovery from Swanson's ABC model that has proven very helpful in discovering biological linkages between bio entities. The PKDE4J tools we used to capture bio entities from our document collection could label entities into five categories: genes, compounds, phenotypes, biological processes, and molecular functions. Using the BioPREP model, we managed to interpret the semantic relatedness between two nodes and provided paths containing valuable hypotheses. Lastly, using a graph-embedding algorithm in our path-ranking analysis, we exploited the semantic relatedness while preserving the graph structure properties.

BertSRC: transformer-based semantic relation classification

The relationship between biomedical entities is complex, and many of them have not yet been identified. For many biomedical research areas including drug discovery, it is of paramount importance to identify the relationships that have already been established through a comprehensive literature survey. However, manually searching through literature is difficult as the amount of biomedical publications continues to increase. Therefore, the relation classification task, which automatically mines meaningful relations from the literature, is spotlighted in the field of biomedical text mining. By applying relation classification techniques to the accumulated biomedical literature, existing semantic relations between biomedical entities that can help to infer previously unknown relationships are efficiently grasped. To develop semantic relation classification models, which is a type of supervised machine learning, it is essential to construct a training dataset that is manually annotated by biomedical experts with semantic relations among biomedical entities. Any advanced model must be trained on a dataset with reliable quality and meaningful scale to be deployed in the real world and can assist biologists in their research. In addition, as the number of such public datasets increases, the performance of machine learning algorithms can be accurately revealed and compared by using those datasets as a benchmark for model development and improvement. In this paper, we aim to build such a dataset. Along with that, to validate the usability of the dataset as training data for relation classification models and to improve the performance of the relation extraction task, we built a relation classification model based on Bidirectional Encoder Representations from Transformers (BERT) trained on our dataset, applying our newly proposed fine-tuning methodology. In experiments comparing performance among several models based on different deep learning algorithms, our model with the proposed fine-tuning methodology showed the best performance. The experimental results show that the constructed training dataset is an important information resource for the development and evaluation of semantic relation extraction models. Furthermore, relation extraction performance can be improved by integrating our proposed fine-tuning methodology. Therefore, this can lead to the promotion of future text mining research in the biomedical field.