WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation

• URL: http://arxiv.org/abs/2503.23673v1
• Date: Mon, 31 Mar 2025 02:36:30 GMT
• Title: WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation
• Authors: Zhengyi Zhao, Shubo Zhang, Bin Liang, Binyang Li, Kam-Fai Wong,
• Abstract summary: This paper proposes a biomedical-dedicated rationale-based synthetic data augmentation method.<n>Specific bio-relation similarity is measured to hold the augmented instance having a strong correlation with bio-relation.<n>We evaluate our method on the BLURB and BigBIO benchmark, which includes 9 common datasets spanning four major BioNLP tasks.
• Score: 17.956185532113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Biomedical Natural Language Processing (BioNLP) tasks, such as Relation Extraction, Named Entity Recognition, and Text Classification, the scarcity of high-quality data remains a significant challenge. This limitation poisons large language models to correctly understand relationships between biological entities, such as molecules and diseases, or drug interactions, and further results in potential misinterpretation of biomedical documents. To address this issue, current approaches generally adopt the Synthetic Data Augmentation method which involves similarity computation followed by word replacement, but counterfactual data are usually generated. As a result, these methods disrupt meaningful word sets or produce sentences with meanings that deviate substantially from the original context, rendering them ineffective in improving model performance. To this end, this paper proposes a biomedical-dedicated rationale-based synthetic data augmentation method. Beyond the naive lexicon similarity, specific bio-relation similarity is measured to hold the augmented instance having a strong correlation with bio-relation instead of simply increasing the diversity of augmented data. Moreover, a multi-agents-involved reflection mechanism helps the model iteratively distinguish different usage of similar entities to escape falling into the mis-replace trap. We evaluate our method on the BLURB and BigBIO benchmark, which includes 9 common datasets spanning four major BioNLP tasks. Our experimental results demonstrate consistent performance improvements across all tasks, highlighting the effectiveness of our approach in addressing the challenges associated with data scarcity and enhancing the overall performance of biomedical NLP models.

Related papers

• Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier [35.79876359248485]
  Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts. Previous methods often overlook the incompleteness of documents and lack the integration of external knowledge. Recent advancements in large language models (LLMs) have inspired us to explore all the above issues for document-level Bio-RE.
  arXiv  Detail & Related papers  (2025-01-09T11:19:40Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery [56.622854875204645]
  We present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth gene-gene interactions. A novel weighted diversified sampling algorithm computes the diversity score of each data sample in just two passes of the dataset.
  arXiv  Detail & Related papers  (2024-10-21T03:35:23Z)
• BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers [48.21255861863282]
  BMRetriever is a series of dense retrievers for enhancing biomedical retrieval. BMRetriever exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger.
  arXiv  Detail & Related papers  (2024-04-29T05:40:08Z)
• Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information [5.456047952635665]
  This work presents a unified, multi-source PPI corpora with vetted interaction definitions augmented by binary interaction type labels. A Transformer-based deep learning method exploits entities' relational context information for relation representation to improve relation classification performance. The model's performance is evaluated on four widely studied biomedical relation extraction datasets.
  arXiv  Detail & Related papers  (2024-03-08T01:43:21Z)
• Improving Biomedical Entity Linking with Retrieval-enhanced Learning [53.24726622142558]
  $k$NN-BioEL provides a BioEL model with the ability to reference similar instances from the entire training corpus as clues for prediction. We show that $k$NN-BioEL outperforms state-of-the-art baselines on several datasets.
  arXiv  Detail & Related papers  (2023-12-15T14:04:23Z)
• Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions [0.276240219662896]
  Predicting the bioactivity of a ligand is one of the hardest and most important challenges in computer-aided drug discovery. Despite years of data collection and curation efforts, bioactivity data remains sparse and heterogeneous. We present a hierarchical meta-learning framework that exploits the information synergy across disparate assays.
  arXiv  Detail & Related papers  (2023-08-17T16:26:58Z)
• Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge [6.244840529371179]
  understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems. Existing databases provide curated biological data from literature and other sources, but their maintenance is labor-intensive. We propose to harness the capabilities of large language models to address these issues by automatically extracting such knowledge from the relevant scientific literature.
  arXiv  Detail & Related papers  (2023-07-17T20:01:11Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• An Empirical Study on Relation Extraction in the Biomedical Domain [0.0]
  We consider both sentence-level and document-level relation extraction, and run a few state-of-the-art methods on several benchmark datasets. Our results show that (1) current document-level relation extraction methods have strong generalization ability; (2) existing methods require a large amount of labeled data for model fine-tuning in biomedicine.
  arXiv  Detail & Related papers  (2021-12-11T03:36:38Z)
• BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network [9.227487525657901]
  We propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports. We evaluate our model on two major biomedical relationship extraction tasks, chemical-disease relation and chemical-protein interaction, and a cross-hospital pan-cancer pathology report corpus.
  arXiv  Detail & Related papers  (2021-10-26T13:19:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.