MACE2K - Molecular And Clinical Extraction: A Natural Language Processing Tool for Personalized Medicine

MACE2K - 分子和临床提取：个性化医疗的自然语言处理工具

• 批准号: 9282279
• 负责人: Subha Madhavan
• 金额: $ 45.55万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-22 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9282279
• 关键词: Address; Algorithms; Big Data; Big Data to Knowledge; Biological; Biomedical Research; Cancer Center; Clinical; Clinical Decision Support Systems; Clinical Trials; Computer software; Computing Methodologies; Custom; Data; Data Aggregation; Databases; Dictionary; Disease; Exclusion Criteria; Gene Expression; Gene Mutation; Genome; Goals; Gold; Informatics; Information Retrieval; Investments; Letters; Literature; Malignant Neoplasms; Manuals; Maps; Meta-Analysis; Methods; Molecular; Molecular Profiling; Molecular Target; Mutation; National Cancer Institute; Natural Language Processing; Oncologist; Online Systems; Outcome; Patients; Peer Review; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Phosphorylation; Process; PubMed; Publications; Recording of previous events; Reporting; Research; Research Design; Research Personnel; Source; Standardization; Structure; System; Systems Biology; Testing; Therapeutic; Time; United States National Institutes of Health; base; crowdsourcing; data to knowledge; data wrangling; design; improved; inclusion criteria; innovation; interest; knowledge base; novel; novel strategies; personalized cancer care; personalized cancer therapy; personalized medicine; programs; protein expression; public health relevance; search engine; software development; symposium; targeted treatment; tool; user friendly software; verification and validation

 DESCRIPTION (provided by applicant): The velocity, variety, volume and veracity of data from relevant information sources make it extremely challenging for oncologists to collect and review pertinent data that can support routine personalized treatment for their patients. There is an urgent need to develop data wrangling approaches including Natural Language Processing and information retrieval methods to extract and curate personalized-therapy related publications and clinical trials. Once curated, the structured data can be used by biomedical researchers to generate novel scientific hypotheses, design new studies, obtain a better understanding of biological mechanisms of disease, perform meta-analyses, and create clinical decision support systems. There is an urgent need to develop improved search interfaces specific to the field of personalized therapy, including ways to display, rank, and save results by end users. While several database and web-based keyword search engine algorithms exist, there is a lack of tools that meet the unique challenges of personalized medicine. There is also an urgent need to develop software that allows for verification and validation of information extracted and ranked through computational methods using subject matter expertise to improve the gold standard corpus that can be used for biomedical research into personalized therapies. To address these issues, we will build an innovative software stack (MACE2K) to adapt and extend widely tested Biocreative natural language processing (NLP) tools to automatically retrieve and pre-process targeted therapy information from clinicaltrials.gov, PubMed abstracts as well as open access articles, and conference proceedings. We will build an entity extraction cartridge to accurately parse gene mutations, translocations, gene expression, protein expression, and protein phosphorylation. A marker disambiguation cartridge will be built to assess for trial inclusion or exclusion criteria and to determine marker-related primary endpoints. We will include a ranking cartridge that uses the disambiguated information on markers, drugs and trials to provide a rigorous scoring of trials and studies according to their relevance for personalized medicine. A novel gamification cartridge will be built to allow subject matter experts to verify and validate the information corpus. Our research leverages National Cancer Institute's investments in several programs (many of which we are involved in) including the NCI drug dictionary, National Cancer Informatics Program (NCIP), I-SPY trials, and Center for cancer systems biology (CCSB) to efficiently accomplish our aims.



项目成果

Platform for Personalized Oncology: Integrative analyses reveal novel molecular signatures associated with colorectal cancer relapse.

个性化肿瘤学平台：综合分析揭示了与结直肠癌复发相关的新分子特征。

• 发表时间: 2013
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Madhavan,Subha;Gauba,Robinder;Song,Lei;Bhuvaneshwar,Krithika;Gusev,Yuriy;Byers,Steve;Juhl,Hartmut;Weiner,Louis
• 通讯作者: Weiner,Louis

eGARD: Extracting associations between genomic anomalies and drug responses from text.

• DOI: 10.1371/journal.pone.0189663
• 发表时间: 2017
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Mahmood ASMA;Rao S;McGarvey P;Wu C;Madhavan S;Vijay-Shanker K
• 通讯作者: Vijay-Shanker K