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文献-算法

基于PPI网络的靶点群距离预测药物组合

Network-based prediction of drug combinations Nature communication 2019 / 03 IF=15 https://doi.org/10.1038/s41467-019-09186-x 文章使用数据 1、PPI high-quality protein-protein interactions (PPIs) 原文说有243,603 PPIs connecting 16,677 unique proteins ,但是根据文章附件链接只有217160 PPIs, 涉及15970个蛋白质 https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-019-09186-x/MediaObjects/41467_2019_9186_MOESM3_ESM.xlsx 2、药物数据 (1)药物组方:DCDB ...

Create:&nbsp;<span title='2022-04-29 00:00:00 +0000 UTC'>2022-04-29</span>&nbsp;|&nbsp;Update:&nbsp;2022-04-29&nbsp;|&nbsp;Words:&nbsp;980&nbsp;|&nbsp;2 min&nbsp;|&nbsp;Lishensuo

矩阵相似性计算样本通路一致性

本次的两篇文章属于同一团队,第一篇文章侧重提出计算方法;第二篇文章侧重于应用方法,发现生物学规律 Paper1:提出方法 SIGN: similarity identification in gene expression Bioinformatics 2019 / 2 IF = 7 1.1 TSC score 文章根据2009年学者提出的modified RV coefficient,使用transcriptional similarity coefficient(TSC)分数,用以表征两个矩阵的相似性。计算公式如下:其中P1矩阵与P2矩阵的纵轴(Gene Row)需要保持一致,而两个矩阵的样本数(Sample Column)没有要求。 $$ TSC(P_1,P_2) = \frac{\sum_{i}(P_{10}×P_{20}){ij}} {{\sqrt{\sum{ij}(P_{10}){ij}^2}}× {\sqrt{\sum{ij}(P_{20})_{ij}^2}}} $$ ...

Create:&nbsp;<span title='2022-05-13 00:00:00 +0000 UTC'>2022-05-13</span>&nbsp;|&nbsp;Update:&nbsp;2022-05-17&nbsp;|&nbsp;Words:&nbsp;1807&nbsp;|&nbsp;4 min&nbsp;|&nbsp;Lishensuo

文献--从相关性网络中鉴定节点间的直接调控关系

1、算法简介 1.1 关系拆解 通过计算方法( 例如相关性Pearson correlation, 互信息mutual information等)构建的相互关系网络中,节点两两之间的关系通常包括直接关系与间接关系两部分,即如下图所示Total = Direct + Indirect ...

Create:&nbsp;<span title='2022-05-19 00:00:00 +0000 UTC'>2022-05-19</span>&nbsp;|&nbsp;Update:&nbsp;2022-05-20&nbsp;|&nbsp;Words:&nbsp;2155&nbsp;|&nbsp;5 min&nbsp;|&nbsp;Lishensuo

文献--基于通路富集的药物重定向(以AD药物为例)

(1)Computational Drug Repurposing for Alzheimer’s Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies https://www.frontiersin.org/articles/10.3389/fphar.2021.617537/full Front Pharmacol, 2021/06, IF 5.81 (2)gene2drug: a computational tool for pathway-based rational drug repositioning ...

Create:&nbsp;<span title='2022-05-31 00:00:00 +0000 UTC'>2022-05-31</span>&nbsp;|&nbsp;Update:&nbsp;2022-05-31&nbsp;|&nbsp;Words:&nbsp;2095&nbsp;|&nbsp;5 min&nbsp;|&nbsp;Lishensuo

文献--基于药物干扰转录组建立距离网络用于药物重定向

Discovery of drug mode of action and drug repositioning from transcriptional responses August 2, 2010 | PNAS | IF=11.2 10.1073/pnas.1000138107 1、合并PRL cMap 1309种化合物作用于5种细胞系的6100个干扰转录组的差异基因结果。对于同一种化合物由于不同浓度,或者作用于不同细胞系会得到多个差异基因列表(下面简称list)。 ...

Create:&nbsp;<span title='2022-06-18 00:00:00 +0000 UTC'>2022-06-18</span>&nbsp;|&nbsp;Update:&nbsp;2022-06-18&nbsp;|&nbsp;Words:&nbsp;1647&nbsp;|&nbsp;4 min&nbsp;|&nbsp;Lishensuo

文献--synergyfinder包计算协同评价指标

DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy Nucleic Acids Research | 2020 | IF=17 1、数据收集 (1)HTS(high-throughput screening)高通量筛选技术可用于快速测得药物作用于癌症细胞系的不同剂量(dose concentrations)下的反应(Response)。其中,反应(Response)的指标常是细胞活力(cell viability)。 ...

Create:&nbsp;<span title='2022-05-27 00:00:00 +0000 UTC'>2022-05-27</span>&nbsp;|&nbsp;Update:&nbsp;2022-05-27&nbsp;|&nbsp;Words:&nbsp;2797&nbsp;|&nbsp;6 min&nbsp;|&nbsp;Lishensuo

文献--机器学习模型预测药物肾毒性-1

题目:In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity 期刊 | 日期:05 January 2022 DOI: 10.3389/fphar.2021.793332 简括:建立机器学习模型的经典流程,值得注意的是包括化合物特征提取,模型建立/评价都是在online chemical database and modeling environment (OCHEM)平台完成的。文章建立的模型也上传到该平台中:https://ochem.eu/article/140251 ...

Create:&nbsp;<span title='2023-02-10 00:00:00 +0000 UTC'>2023-02-10</span>&nbsp;|&nbsp;Update:&nbsp;2022-02-10&nbsp;|&nbsp;Words:&nbsp;737&nbsp;|&nbsp;2 min&nbsp;|&nbsp;Lishensuo

文献--机器学习模型预测药物肾毒性-2

题目:In silico prediction of potential drug-induced nephrotoxicity with machine learning methods 期刊 | 日期 : Journal of Applied Toxicology | 11 April 2022 简介:标准的机器学习模型分析流程,从数据收集到数据整理,从训练模型到评价模型。 1、数据收集 1.1 标签数据 (1)从SIDER、DrugBank、ChEMBL以及TCM@taiwan等4个数据库收集了1366个标签化合物 ...

Create:&nbsp;<span title='2023-02-10 00:00:00 +0000 UTC'>2023-02-10</span>&nbsp;|&nbsp;Update:&nbsp;2022-02-10&nbsp;|&nbsp;Words:&nbsp;1073&nbsp;|&nbsp;3 min&nbsp;|&nbsp;Lishensuo

文献--单细胞组学大模型之scGPT

文献: scGPT: toward building a foundation model for single-cell multi-omics using generative AI 时间:2024 Feb. (Published) 期刊:Nature Method DOI:https://doi.org/10.1038/s41592-024-02201-0 ...

Create:&nbsp;<span title='2024-09-29 00:00:00 +0000 UTC'>2024-09-29</span>&nbsp;|&nbsp;Update:&nbsp;2024-09-29&nbsp;|&nbsp;Words:&nbsp;6652&nbsp;|&nbsp;14 min&nbsp;|&nbsp;Lishensuo

文献--单细胞组学大模型之scBERT

标题: scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data 期刊|日期:nature machine intelligence, 2022/09 DOI:https://doi.org/10.1038/s42256-022-00534-z ...

Create:&nbsp;<span title='2024-11-09 00:00:00 +0000 UTC'>2024-11-09</span>&nbsp;|&nbsp;Update:&nbsp;2024-11-09&nbsp;|&nbsp;Words:&nbsp;1150&nbsp;|&nbsp;3 min&nbsp;|&nbsp;Lishensuo
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