BGI Webinar June 6, 2014 "Genomic Big Data Analysis and Customised Analysis w...kazuoishii20
This document discusses RNA-Seq analysis and custom analysis of large-scale genomic information. It introduces RNA-Seq, its applications and challenges, and solutions to those challenges. It also describes building a data analysis system using big data environments, including using Monte Carlo simulations, high-performance computing, distributed processing systems with Hadoop, and distributed processing with shell scripts.
BGI Webinar June 6, 2014 "Genomic Big Data Analysis and Customised Analysis w...kazuoishii20
This document discusses RNA-Seq analysis and custom analysis of large-scale genomic information. It introduces RNA-Seq, its applications and challenges, and solutions to those challenges. It also describes building a data analysis system using big data environments, including using Monte Carlo simulations, high-performance computing, distributed processing systems with Hadoop, and distributed processing with shell scripts.
Tensor decomposition based and principal component analysis based unsupervise...Y-h Taguchi
The document summarizes a presentation that was planned to be given at APBC2018 but was canceled. The presentation aimed to identify genes associated with differences in gene expression and methylation profiles between queens and workers in two social insect species (Dinoponera quadriceps and Polistes canadensis) using unsupervised feature extraction methods like principal component analysis and tensor decomposition. The analysis identified genes that were distinctly methylated and expressed between castes for each species, which were validated through gene ontology enrichment.
Tensor decomposition based unsupervised feature extraction applied to matrix...Y-h Taguchi
The document proposes using tensor decomposition on matrix products of multi-view data to perform unsupervised feature extraction. It applies this method to synthetic data containing embedded correlated variables and multi-omics data involving mRNA and miRNA. For the synthetic data, the method recovers the original orthogonal vectors after identifying correlations. For the multi-omics data, a few inter-correlated miRNAs and mRNAs are identified as statistically significant among many features.
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...Y-h Taguchi
This document describes a tensor decomposition-based unsupervised feature extraction method for identifying candidate drugs for heart failure using integrated analysis of gene expression data from heart failure patients and drugs. The method involves generating a tensor from the mathematical product of gene expression data between human diseases and drug-treated model animals. Tensor decomposition is applied to extract features without supervised training. The method successfully identified genes affected by drug treatments that could be biologically reasonable candidate targets. Further applications combining additional drug and target gene data are underway.
microRNA-mRNA interaction identification in Wilms tumor using principal compo...Y-h Taguchi
This document describes a study that uses principal component analysis (PCA) based unsupervised feature extraction to identify microRNA-mRNA interactions in Wilms tumor. PCA is applied to gene expression data to select outlier mRNAs and miRNAs without using predetermined significance thresholds. Selected mRNA-miRNA pairs are validated using existing miRNA-mRNA interaction databases and survival analysis. The approach identifies more biologically feasible mRNAs than only considering differential gene expression between normal and tumor samples.
Tensor decomposition based and principal component analysis based unsupervise...Y-h Taguchi
The document summarizes a presentation that was planned to be given at APBC2018 but was canceled. The presentation aimed to identify genes associated with differences in gene expression and methylation profiles between queens and workers in two social insect species (Dinoponera quadriceps and Polistes canadensis) using unsupervised feature extraction methods like principal component analysis and tensor decomposition. The analysis identified genes that were distinctly methylated and expressed between castes for each species, which were validated through gene ontology enrichment.
Tensor decomposition based unsupervised feature extraction applied to matrix...Y-h Taguchi
The document proposes using tensor decomposition on matrix products of multi-view data to perform unsupervised feature extraction. It applies this method to synthetic data containing embedded correlated variables and multi-omics data involving mRNA and miRNA. For the synthetic data, the method recovers the original orthogonal vectors after identifying correlations. For the multi-omics data, a few inter-correlated miRNAs and mRNAs are identified as statistically significant among many features.
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...Y-h Taguchi
This document describes a tensor decomposition-based unsupervised feature extraction method for identifying candidate drugs for heart failure using integrated analysis of gene expression data from heart failure patients and drugs. The method involves generating a tensor from the mathematical product of gene expression data between human diseases and drug-treated model animals. Tensor decomposition is applied to extract features without supervised training. The method successfully identified genes affected by drug treatments that could be biologically reasonable candidate targets. Further applications combining additional drug and target gene data are underway.
microRNA-mRNA interaction identification in Wilms tumor using principal compo...Y-h Taguchi
This document describes a study that uses principal component analysis (PCA) based unsupervised feature extraction to identify microRNA-mRNA interactions in Wilms tumor. PCA is applied to gene expression data to select outlier mRNAs and miRNAs without using predetermined significance thresholds. Selected mRNA-miRNA pairs are validated using existing miRNA-mRNA interaction databases and survival analysis. The approach identifies more biologically feasible mRNAs than only considering differential gene expression between normal and tumor samples.
5. 細胞細胞核核
DNADNA
AA
TT
GG CC AA TT
GG
CC
TT AA
CC
GG
TT
AA CC GG
DNADNAとは何か?とは何か?(⇒DNA(⇒DNAムービー)ムービー)
AATTGGCCAATTGGC.C.........
||||||||||||||
TTAACCGGTTAACCGG..........
二重らせん二重らせん
・・AA、、TT、、GG、、CCの4種類の分子がの4種類の分子が
連結した高分子連結した高分子 → 情報→ 情報
・・AAとT,CとGが組になった「相とT,CとGが組になった「相
補鎖」どうしがペアになる補鎖」どうしがペアになる
・立体構造は二重らせん・立体構造は二重らせん