相关实验视频

Updated: Jul 27, 2026

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

基于机器学习的枢纽基因和与急性髓性白血病中药物敏感性相关的潜在分子机制的识别

Boyu Zhang¹, Haiyan Liu¹, Fengxia Wu¹

¹Department of Hematology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, China.

Frontiers in pharmacology

|April 23, 2024

概括

研究人员使用机器学习在急性髓性白血病 (AML) 中确定了80个高风险基因. 这些基因可以预测患者的存活率,并且可能是新药的目标,改善AML治疗结果.

科学领域:

血液学血液学血液学
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

急性髓性白血病 (AML) 是最常见的成人白血病,老年患者的预后不佳.
目前的治疗方法在老年AML群体中有效性有限,需要新的治疗策略.

研究的目的:

在AML中使用计算和机器学习 (ML) 方法识别和表征差异性高风险基因 (DHRGs).
开发和验证基于ML的预后模型,用于AML风险分层.
探索潜在的治疗点和AML的候选药物.

主要方法:

通过基因组,功能和生存分析利用计算方法和ML来识别DHRG.
使用生存分析,免疫透,miRNA联合表达和干性特征分析来确定DHRG的预后重要性.
使用ML算法构建和验证的预后模型,包括Ridge和plsRcox,用于药物发现的分子对接.

主要成果:

确定了80个与副本数变化和甲基化状态相关的DHRG,显著影响了AML患者的整体存活率 (OS).
开发了一种经过验证的ML模型,将AML患者分为低风险和高风险组,在低风险队列中显示明显更长的OS.
揭示了免疫透,miRNA联合表达,茎性和风险组之间的标志性途径的显著差异;确定了5种潜在药物,包括碳烯.

结论:

确定了一组具有潜力作为AML的预后和治疗标志物的DHRG.
证明了ML算法的有效性,用于构建和验证AML的预后模型.
强调需要对在AML中发现的枢纽基因进行实验验证.

关键词:

拉姆尔 (Laml) 是一个著名的传奇人物.机器学习是机器学习.分子对接的分子对接.预测药物的药物预测.预测模型的预测模型.

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Published on: June 17, 2022

相关概念视频

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...