# A Biomedical Knowledge Graph for Biomarker Discovery in Cancer

arXiv: https://arxiv.org/abs/2302.04737 · HTML: https://mlsanity.com/paper/2302.04737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Explaining Recurrent Neural Network Predictions in Sentiment Analysis](https://mlsanity.com/paper/1706.07206/) (2017)
- [Explicit Knowledge-based Reasoning for Visual Question Answering](https://mlsanity.com/paper/1511.02570/) (2015)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network](https://mlsanity.com/paper/1908.01288/) (2019)
- [DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language](https://mlsanity.com/paper/2012.14353/) (2020)
- [Neuro-symbolic representation learning on biological knowledge graphs](https://mlsanity.com/paper/1612.04256/) (2016)
- [Biomedical named entity recognition using BERT in the machine reading comprehension framework](https://mlsanity.com/paper/2009.01560/) (2020)
- [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://mlsanity.com/paper/1908.07721/) (2019)
- [Explainable AI for Bioinformatics: Methods, Tools, and Applications](https://mlsanity.com/paper/2212.13261/) (2022)
- [Ontology Guided Information Extraction from Unstructured Text](https://mlsanity.com/paper/1302.1335/) (2013)
- [OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data](https://mlsanity.com/paper/1909.04169/) (2019)
- [Interpreting Black-box Machine Learning Models for High Dimensional Datasets](https://mlsanity.com/paper/2208.13405/) (2022)
- [Question Answering Over Biological Knowledge Graph via Amazon Alexa](https://mlsanity.com/paper/2210.06040/) (2022)
- [Fine-Grained Named Entity Recognition using ELMo and Wikidata](https://mlsanity.com/paper/1904.10503/) (2019)
- [A Scalable Framework for Quality Assessment of RDF Datasets](https://mlsanity.com/paper/2001.11100/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023)
- [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025)
- [Quantitative Evidence Mining for Plausibility-Aware Biomedical AI: A Narrative Review and Conceptual Framework](https://mlsanity.com/paper/2608.30393/) (2026)

## Abstract (arXiv)

Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A domain-specific knowledge graph~(KG) is an explicit conceptualization of a specific subject-matter domain represented w.r.t semantically interrelated entities and relations. A KG can be constructed by integrating such facts and data and be used for data integration, exploration, and federated queries. However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies. Such a KG will not only allow deducing new knowledge and question answering(QA) but also allows domain experts to explore. Since cross-disciplinary explanations are important for accurate diagnosis, it is important to query the KG to provide interactive explanations about learned biomarkers. Inspired by these, we construct a domain-specific KG, particularly for cancer-specific biomarker discovery. The KG is constructed by integrating cancer-related knowledge and facts from multiple sources. First, we construct a domain-specific ontology, which we call OncoNet Ontology (ONO). The ONO ontology is developed to enable semantic reasoning for verification of the predictions for relations between diseases and genes. The KG is then developed and enriched by harmonizing the ONO, additional metadata schemas, ontologies, controlled vocabularies, and additional concepts from external sources using a BERT-based information extraction method. BioBERT and SciBERT are finetuned with the selected articles crawled from PubMed. We listed down some queries and some examples of QA and deducing knowledge based on the KG.