第２回 ASHBi 数理生物研究集会 プログラム

日 時: 2020 年 9 月 4 日（金） 9:30-17:00
場 所: Zoom オンラインミーティングにて開催

9:30-10:30

川上 英良（理化学研究所）“Data-driven medical research using machine learning”

10:45-11:45

小井土 大（東京大学）“Learn genomics from AI for interpreting the roles of non-coding RNAs in complextraits”(休憩 11:45–13:30)

13:30-14:30

玉田 嘉紀（京都大学）“Personalized gene network analysis with large scale Bayesian network estimation”

14:45-15:45

中戸 隆一郎（東京大学）“Large-scale analysis of multi-omics data toward the elucidation of epigenomic
diversity”

16:00-17:00

フリーディスカッション主 催：
京都大学高等研究院 ヒト生物学高等研究拠点（WPI-ASHBi）
田﨑 創平、井元 佑介、平岡 裕章、岩見 真吾、斎藤 通紀

川上 英良（理化学研究所）

タイトル:
Data-driven medical research using machine learning
要旨:
In recent years, artificial intelligence technology and data science have rapidly
spread in medical research. In medical research, it is often difficult to directly apply
a basic biological model due to differences in time scale and hierarchy. Instead, adata-driven approach has been employed, which observe target disease without
specific hypothesis. Machine learning, which can extract potential patterns andmake highly accurate predictions while considering various types of variables andcomplex dependencies between variables, is a major tool in the data-driven medicalresearch. We conduct research using machine learning for the purpose of stratifying
and visualizing health and disease states and predicting the onset and progressionof disease. In this seminar, I would like to introduce an explainable scheme forstratifying and visualizing health and disease states using machine learning anddiscuss the prospects for preventive and personalized medicine.

小井土 大（東京大学）

タイトル:Learn genomics from AI for interpreting the roles of non-coding RNAs in complex
traits
要旨:
Genome sequence data can be handled in a similar way as image data byconsidering the four bases (ATGC) as the three primary colors (RGB). From the
combination of local features in the image (e.g., eyes, ears, etc.), we recognize anobject (e.g., dog or cat), whose mechanisms can be modeled by the deep
convolutional neural networks (deep CNN). Similarly, we may know the meaning oflong genome sequence patterns from the combination of motif sequences. In fact,
the deep CNN has made it possible to predict tissue-specific gene expression levels
from the surrounding kb-order genome sequences alone. I extended this concept
into the transcription of non-coding RNAs (ncRNAs) and developed mutation effectprediction on ncRNA transcription (MENTR), a machine learning framework reliably
connecting genetic associations with expression of ncRNAs (Koido et al., bioRxiv2020). MENTR-predicted mutation effects on ncRNA transcription were concordant
with estimates from previous genetic studies in a cell type-dependent manner.
MENTR proposed 7,775 enhancers and 3,548 long-ncRNAs as complex traitassociated ncRNAs in 348 major human primary cells and tissues, including
plausible enhancer-mediated functional alterations. In this workshop, I want todiscuss how we can use machine learning methods for interpreting the results ofstatistical genetic studies.

玉田 嘉紀（京都大学）

タイトル:
Personalized gene network analysis with large scale Bayesian network estimation
要旨:
A Bayesian network is a graphical model that can be used to estimate cause-andeffect relationships among variables by considering conditional independencies
between them. It is an explainable artificial intelligence model since therelationships among variables are represented explicitly by mathematical equations
that are learned automatically from data. We are using this model for a long timeto analyze gene-to-gene regulatory relationships with gene expression data. It
enables us to model and analyze the entire transcripts including over 20,000
thousand genes in human cells. The difficulty in Bayesian network estimation is thestructure search of the network because it is known to be an NP-hard problem, i.e.,the search space of possible structures increases exponentially as the number ofvariables increases. Also, the network estimation results in more than 100,000
edges. Thus, it is hard to extract meaningful results from such a huge network andto interpret them. In this talk, we begin with a brief history of gene network
estimation, and then introduce our history of straggling against this difficult problem.
The current focus of our research group is on the estimation of personalized
individual networks from limited samples. We present our latest progress that
overcomes this problem.

中戸 隆一郎（東京大学）

タイトル:
Large-scale analysis of multi-omics data toward the elucidation of epigenomic
diversity
要旨:
Large-scale epigenomic analysis that compares hundreds of samples is an essential
approach to reveal the high-dimensional interrelationship for regulatory elements
and also annotate novel functional genomic regions de novo. As a part of theInternational Human Epigenome Consortium (IHEC) project, we collected histone
modification and gene expression data to profile and characterize the epigenomic
landscape of human endothelial cells (ECs) and implemented comprehensive
analysis for genome-wide data. To avoid the batch effect and individual differencesamong samples, we developed a computational pipeline for epigenome analysis
combined with chromatin interaction data. We found that most of the deferentiallyexpressed genes and enhancer sites were cooperatively enriched in more than one
EC type, suggesting that the distinct combinations of multiple genes play key rolesin the diverse phenotypes across EC types. Notably, many homeobox genes weredifferentially expressed across EC types, and their expression was correlated withthe relative position of each organ in the body. Lastly, I discuss several future
prospects including the single-cell analysis.