Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration

• URL: http://arxiv.org/abs/2409.15612v1
• Date: Mon, 23 Sep 2024 23:36:30 GMT
• Title: Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration
• Authors: Wangyang Ying, Dongjie Wang, Xuanming Hu, Ji Qiu, Jin Park, Yanjie Fu,
• Abstract summary: We propose a new biomarker identification framework with two important modules: training data preparation and embedding-optimization-generation. The first module uses a multi-agent system to automatically collect pairs of biomarker subsets and their corresponding prediction accuracy as training data. The second module employs an encoder-evaluator-decoder learning paradigm to compress the knowledge of the collected data into a continuous space.
• Score: 20.419747013569268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomarker discovery is vital in advancing personalized medicine, offering insights into disease diagnosis, prognosis, and therapeutic efficacy. Traditionally, the identification and validation of biomarkers heavily depend on extensive experiments and statistical analyses. These approaches are time-consuming, demand extensive domain expertise, and are constrained by the complexity of biological systems. These limitations motivate us to ask: Can we automatically identify the effective biomarker subset without substantial human efforts? Inspired by the success of generative AI, we think that the intricate knowledge of biomarker identification can be compressed into a continuous embedding space, thus enhancing the search for better biomarkers. Thus, we propose a new biomarker identification framework with two important modules:1) training data preparation and 2) embedding-optimization-generation. The first module uses a multi-agent system to automatically collect pairs of biomarker subsets and their corresponding prediction accuracy as training data. These data establish a strong knowledge base for biomarker identification. The second module employs an encoder-evaluator-decoder learning paradigm to compress the knowledge of the collected data into a continuous space. Then, it utilizes gradient-based search techniques and autoregressive-based reconstruction to efficiently identify the optimal subset of biomarkers. Finally, we conduct extensive experiments on three real-world datasets to show the efficiency, robustness, and effectiveness of our method.

Related papers

• BioMNER: A Dataset for Biomedical Method Entity Recognition [25.403593761614424]
  We propose a novel dataset for biomedical method entity recognition. We employ an automated BioMethod entity recognition and information retrieval system to assist human annotation. Our empirical findings reveal that the large parameter counts of language models surprisingly inhibit the effective assimilation of entity extraction patterns.
  arXiv  Detail & Related papers  (2024-06-28T16:34:24Z)
• 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)
• Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling [3.8599767910528917]
  This paper proposes a hybrid approach that integrates the strengths of multiple models. BERT provides contextualized word embeddings, a pre-trained multi-channel CNN for character-level information capture, and following by a BiLSTM + CRF for sequence labelling and modelling dependencies between the words in the text. We evaluate our model on the benchmark i2b2/2010 dataset, achieving an F1-score of 90.11.
  arXiv  Detail & Related papers  (2023-12-24T21:45:36Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)
• AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning [7.427654811697884]
  We present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning.
  arXiv  Detail & Related papers  (2022-11-30T12:35:00Z)
• Discovering Drug-Target Interaction Knowledge from Biomedical Literature [107.98712673387031]
  The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from literature becomes an urgent demand in the industry. We explore the first end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
  arXiv  Detail & Related papers  (2021-09-27T17:00:14Z)
• Preventing dataset shift from breaking machine-learning biomarkers [0.6138671548064355]
  A good biomarker is one that gives reliable detection of the corresponding condition. Biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals.
  arXiv  Detail & Related papers  (2021-07-21T08:54:23Z)
• Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection [49.78612417406883]
  We propose a novel semi-supervised deep metric learning method for cervical cancer cell detection. Our model learns an embedding metric space and conducts dual alignment of semantic features on both the proposal and prototype levels. We construct a large-scale dataset for semi-supervised cervical cancer cell detection for the first time, consisting of 240,860 cervical cell images.
  arXiv  Detail & Related papers  (2021-04-07T17:11:27Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

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.