A Biomedical Knowledge Graph for Biomarker Discovery in Cancer
RWTH Aachen University（亚琛工业大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；ZBMED - Information Center for Life Sciences（ZB MED——生命科学信息中心）；University of Cologne（科隆大学）
Md. Rezaul Karim, Lina Molinas Comet, Oya Beyan, Dietrich Rebholz-Schuhmann, Stefan Decker
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.
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Georgia Institute of Technology（佐治亚理工学院）；Mayo Clinic Arizona（妙佑医疗国际亚利桑那州院区）；Arizona State University（亚利桑那州立大学）；Columbia University Medical Center（哥伦比亚大学医学中心）
Potential of Artificial Intelligence Algorithms for Identification of Relevant Diagnostic and Prognostic Biomarkers of Early-Stage Liver Cancer
University of Sharjah（沙迦大学）；University Medical Center Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医学中心）；University of Lubeck（吕贝克大学）；University of Birmingham（伯明翰大学）
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67
Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院）；University of São Paulo（圣保罗大学）
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Else Kroener Fresenius Center for Digital Health（埃尔瑟·克勒纳·弗雷森纽斯数字健康中心）；TUD Dresden University of Technology（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院及大学医院）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州））；IBM Research Europe（IBM欧洲研究院）；Centre de Recherche des Cordeliers（科尔得利研究所）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎城市大学）；Assistance Publique-Hôpitaux de Paris（巴黎公共医疗援助中心）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；German Cancer Consortium (DKTK)（德国癌症联盟）；University Hospital Heidelberg（海德堡大学医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Leeds（利兹大学）
R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
Department of Computer Science and Engineering, Mississippi State University（密苏里州立大学计算机科学与工程系）；Department of Information Technology, Universiti Tenaga Nasional（国家技术大学信息科技系）；School of Electrical and Mechanical Engineering, University of Portsmouth（普利茅斯大学电气与机械工程学院）；Department of Computer Science, The University of Alabama（阿拉巴马大学计算机科学系）
Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization
National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆）；National Cancer Institute (NCI), National Institutes of Health (NIH)（美国国立卫生研究院国家癌症研究所）；Dartmouth Health（达特茅斯医疗系统）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection
Department of Computer Science, New Jersey Institute of Technology（计算机科学系，新泽西理工学院）；Department of Data Science, New Jersey Institute of Technology（数据科学系，新泽西理工学院）；Department of Biostatistics（生物统计学系）；Division of Cardiology, Brown University Health（心脏病学部，布朗大学健康中心）；Department of Medicine, Warren Alpert Medical School of Brown University（医学系，布朗大学沃伦·阿尔珀特医学院）
The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center（西德尼·基梅尔综合癌症中心）；The Johns Hopkins University（约翰霍普金斯大学）；University of Kentucky（肯塔基大学）；University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院）
A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the radiologist in defining disease. For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images. The MPDL network was constructed from stacked sparse autoencoders with inputs from mpMRI. Evaluation of MPD…
Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and Tracking
Dentofacial Deformities Research Center（牙颌面畸形研究中心）；Research Institute of Dental Sciences（牙科科学研究所）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）
Imperial College London（帝国理工学院）；University College London（伦敦大学学院）；Hong Kong Baptist University（香港浸会大学）
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonst…
Multimodal Cancer Modeling in the Age of Foundation Model Embeddings
Center for Translational Data Science（转化数据科学中心）；Department of Computer Science（计算机科学系）；University of Chicago（芝加哥大学）；Medical Scientist Training Program（医学科学家培训计划）；Brown University（布朗大学）；Section of Biomedical Data Science（生物医学数据科学部门）；Department of Medicine（医学系）
Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language Models
University of North Dakota School of Medicine and Health Sciences（北达科他大学医学与健康科学学院）；University of North Dakota（北达科他大学）；University of Michigan（密歇根大学）；Bogazici University（博阿齐奇大学）
