AD-LitMiner is a pathology evidence-based web service based on the mining of medical literature on Alzheimer's disease.
AD-LitMiner filters out the most valuable statements from the vast amount of biomedical literature for the users and provides drug recommendations for them, hopefully contributing some help to Alzheimer's disease research.
AD-LitMiner 是一个基于数据挖掘关于阿尔茨海默病的医学文献的病理证据的网络服务,能够从大量的生物医学文献中筛选出最有价值的声明给用户，并为他们提供药物推荐，希望能够为阿尔茨海默病的研究提供一些帮助。
Read more about each of our focus areas below:
请阅读下面关于我们每个重点领域的更多信息：
Alzheimer's disease (阿尔茨海默病)
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive and behavioral impairment, which significantly impacts social and occupational functioning. It is most commonly diagnosed in older adults, and its etiology remains unclear. Research on Alzheimer's Disease focuses on understanding its pathogenesis, identifying therapeutic targets, and improving early diagnosis accuracy. Several hypotheses have been proposed to explain the onset of Alzheimer's Disease, including the accumulation of beta-amyloid plaques, abnormal phosphorylation of Tau protein, and inflammatory responses. Additionally, there are specific databases available for storing and sharing genetic, protein, and clinical data related to Alzheimer's Disease, which serve as valuable resources for researchers.
阿尔茨海默病（AD）是一种进行性的神经退行性疾病，其特征是认知和行为损害，严重影响社会和职业功能。它最常在老年人中被诊断，其病因尚不清楚。关于阿尔茨海默病的研究集中在理解其发病机制、确定治疗靶点和提高早期诊断的准确性上。已有几种假设被提出来解释阿尔茨海默病的发病，包括β-淀粉样蛋白斑块的积累、Tau蛋白的异常磷酸化和炎症反应。此外，还有一些特定的数据库可供存储和共享与阿尔茨海默病相关的遗传、蛋白质和临床数据，这些对研究人员来说是宝贵的资源。
Biomedical Literature Mining (生物医学文献挖掘)
Biomedical Literature Mining is a method that utilizes natural language processing and machine learning techniques to extract and analyze biomedical entities (such as genes, proteins, and diseases) and their relationships from a large volume of biomedical literature. The goal of biomedical text mining is to assist researchers in efficiently accessing and organizing biomedical information to support basic research and drug development. Common methods in biomedical text mining include named entity recognition, relationship extraction, text classification, and text clustering. Biomedical text mining has important applications in drug discovery, gene function research, and clinical decision support.
生物医学文献挖掘是一种利用自然语言处理和机器学习技术从大量生物医学文献中提取和分析生物医学实体（如基因、蛋白质和疾病）及其关系的方法。生物医学文本挖掘的目标是帮助研究人员高效地获取和组织生物医学信息，以支持基础研究和药物开发。生物医学文本挖掘的常见方法包括命名实体识别、关系提取、文本分类和文本聚类。生物医学文本挖掘在药物发现、基因功能研究和临床决策支持中有重要的应用。
Drug Repurposing (药物再利用)
Drug repurposing, also known as drug repositioning, refers to the process of using existing drugs that have been approved for one disease to treat different diseases. Compared to traditional drug development, drug repurposing offers advantages in terms of time and cost, as these drugs have already undergone clinical trials and safety evaluations. Research on drug repurposing involves screening existing drugs for new indications, studying the mechanisms of action, and optimizing dosage and administration methods. Drug repurposing holds promise as a more efficient and cost-effective approach to drug development.
药物再利用，也称为药物重新定位，指的是使用已经批准用于一种疾病的现有药物来治疗不同疾病的过程。与传统的药物开发相比，药物再利用在时间和成本方面具有优势，因为这些药物已经通过了临床试验和安全性评估。药物再利用的研究涉及筛选现有药物用于新的适应症、研究作用机制以及优化剂量和给药方法。药物再利用被视为一种更高效、成本效益更高的药物开发方法。
Hypothesis Generation (假设生成)
Hypothesis Generation refers to the process of proposing new drug repurposing hypotheses or predicting new indications for drugs through the analysis and mining of large-scale biomedical data. Research methods for hypothesis generation include knowledge graph-based reasoning, text mining, and machine learning techniques. Through hypothesis generation, researchers can discover potential associations between existing drugs and other diseases, providing new directions and possibilities for drug repurposing.
假设生成是指通过分析和挖掘大量生物医学数据，提出新的药物再利用假设或预测药物新适应症的过程。假设生成的研究方向包括基于知识图谱的推理、文本挖掘和机器学习技术。通过假设生成，研究人员可以发现现有药物与其他疾病之间的潜在联系，为药物再利用提供新的方向和可能性。
LOF/GOF (LOF/GOF突变)
LOF/GOF mutations refer to mutations in genes that result in loss or gain of function. In the field of drug repurposing, researchers focus on the relationship between LOF/GOF mutations and drug treatment outcomes. By analyzing response data of drugs in the context of LOF/GOF mutations, researchers can identify which drugs are effective for specific mutation types, providing guidance for drug repurposing research and clinical applications.
LOF/GOF突变指的是导致基因功能丧失或功能增强的基因突变。在药物再利用领域，研究人员关注LOF/GOF突变与药物治疗结果之间的关系。通过在LOF/GOF突变的背景下分析药物的反应数据，研究人员可以识别出对特定突变类型有效的药物，为药物再利用研究和临床应用提供指导。