Details of a Researcher - Sakakibara, Yasubumi

Sakakibara, Yasubumi

写真a

Affiliation: Faculty of Science and Technology, Department of Biosciences and Informatics (Yagami)

Position: Professor

Related Websites

http://www.dna.bio.keio.ac.jp/

External Links

Career 【 Display / hide 】

1985.04

-

1996.03

（株）富士通研究所情報社会科学研究所 ,研究員
1985.04

-

1996.03

（株）富士通研究所情報社会科学研究所 ,研究員
1992.06

-

1993.06

兼米国カリフォルニア大学サンタクルーズ校 ,客員研究員
1992.06

-

1993.06

兼米国カリフォルニア大学サンタクルーズ校 ,客員研究員
1996.04

-

2001.09

東京電機大学理工学部情報科学科 ,助教授

Academic Background 【 Display / hide 】

1983.03

Tokyo Institute of Technology, Faculty of Science, 情報科学科

University, Graduated
1985.03

Tokyo Institute of Technology, Graduate School, Division of Science and Engineeri, 情報科学専攻

Graduate School, Completed, Master's course

Academic Degrees 【 Display / hide 】

理学, Tokyo Institute of Technology, 1991.10

Research Areas 【 Display / hide 】

Life Science / System genome science
Life Science / System genome science
Informatics / Life, health and medical informatics (Bioinformatics)
Informatics / Life, health and medical informatics (Bioinformatics)

Research Keywords 【 Display / hide 】

cancer genome
cancer genome
Bioinformatics
Bioinformatics

Research Themes 【 Display / hide 】

Big daya analysis,

2012.04

-

Present
Cancer genmome analysis,

2010.04

-

Present
functional RNA sequence analysis,

2002.04

-

Present

Books 【 Display / hide 】

情報数理シリーズ「計算論的学習」

榊原康文, 小林聡, 横森貴, 培風館, 2001.10

　View Summary

コンピュータに学習能力を持たせることを目的とする人工知能分野における機械学習の理論を解説する日本ではじめての教科書．
DNAコンピューティング

Paun, Rozenberg, Salomaa 著, 横森貴, 榊原康文, 小林聡訳, シュプリンガー・フェアラーク東京, 1999.12

　View Summary

DNAコンピュータに関するはじめての教科書．形式言語理論に基づいたDNAコンピュータの理論に関する研究を中心に書かれている．
新版情報処理ハンドブック

情報処理学会偏, 章の「学習, に関する節を分担執筆, オーム社, 1995.11

　View Summary

情報処理に関するすべての分野をカバーするハンドブック．

Papers 【 Display / hide 】

Deep learning of multimodal networks with topological regularization for drug repositioning

Ohnuki Y., Akiyama M., Sakakibara Y.

Journal of Cheminformatics 16 ( 1 ) 103 2024.12

ISSN 1758-2946

　View Summary

Motivation: Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies. Results: STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git.
- Access to Document (DOI)
Selection of anti-cytokine biologics by pretreatment levels of serum leucine-rich alpha-2 glycoprotein in patients with inflammatory bowel disease.

Amano T, Yoshihara T, Shinzaki S, Sakakibara Y, Yamada T, Osugi N, Hiyama S, Murayama Y, Nagaike K, Ogiyama H, Yamaguchi T, Arimoto Y, Kobayashi I, Kawai S, Egawa S, Kizu T, Komori M, Tsujii Y, Asakura A, Tashiro T, Tani M, Otake-Kasamoto Y, Uema R, Kato M, Tsujii Y, Inoue T, Yamada T, Kitamura T, Yonezawa A, Iijima H, Hayashi Y, Takehara T

Scientific reports 14 ( 1 ) 29755 2024.11
- Access to Document (DOI)
Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine

Uehara M., Inoue T., Hase S., Sasaki E., Toyoda A., Sakakibara Y.

mSystems (mSystems) 9 ( 5 ) e0140523 2024.05

　View Summary

The gut microbiome affects the health status of the host through complex interactions with the host’s intestinal wall. These host-microbiome interactions may spatially vary along the physical and chemical environment of the intestine, but these changes remain unknown. This study investigated these intricate relationships through a gene co-expression network analysis based on dual transcriptome profiling of different intestinal sites—cecum, transverse colon, and rectum—of the primate common marmoset. We proposed a gene module extraction algorithm based on the graph theory to find tightly interacting gene modules of the host and the microbiome from a vast co-expression network. The 27 gene modules identified by this method, which include both host and microbiome genes, not only produced results consistent with previous studies regarding the host-microbiome relationships, but also provided new insights into microbiome genes acting as potential mediators in host-microbiome interplays. Specifically, we discovered associations between the host gene FBP1, a cancer marker, and polysaccharide degradation-related genes (pfkA and fucI) coded by Bacteroides vulgatus, as well as relationships between host B cell-specific genes (CD19, CD22, CD79B, and PTPN6) and a tryptophan synthesis gene (trpB) coded by Parabacteroides distasonis. Furthermore, our proposed module extraction algorithm surpassed existing approaches by successfully defining more functionally related gene modules, providing insights for understanding the complex relationship between the host and the microbiome.
- Access to Document (DOI)
Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity

Ochiai T., Inukai T., Akiyama M., Furui K., Ohue M., Matsumori N., Inuki S., Uesugi M., Sunazuka T., Kikuchi K., Kakeya H., Sakakibara Y.

Communications Chemistry (Communications Chemistry) 6 ( 1 ) 249 2023.12

　View Summary

The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we developed a deep-learning method, called NP-VAE (Natural Product-oriented Variational Autoencoder), based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity of compounds. NP-VAE was successful in constructing the chemical latent space from large-sized compounds that were unable to be handled in existing methods, achieving higher reconstruction accuracy, and demonstrating stable performance as a generative model across various indices. Furthermore, by exploring the acquired latent space, we succeeded in comprehensively analyzing a compound library containing natural compounds and generating novel compound structures with optimized functions.
- Access to Document (DOI)
Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach

Kanauchi Y., Hashimoto M., Toda N., Okamoto S., Haque H., Jinzaki M., Sakakibara Y.

Healthcare (Switzerland) (Healthcare (Switzerland)) 11 ( 4 ) 2023.02

ISSN 2227-9032

　View Summary

Ultrasonography is widely used for diagnosis of diseases in internal organs because it is nonradioactive, noninvasive, real-time, and inexpensive. In ultrasonography, a set of measurement markers is placed at two points to measure organs and tumors, then the position and size of the target finding are measured on this basis. Among the measurement targets of abdominal ultrasonography, renal cysts occur in 20–50% of the population regardless of age. Therefore, the frequency of measurement of renal cysts in ultrasound images is high, and the effect of automating measurement would be high as well. The aim of this study was to develop a deep learning model that can automatically detect renal cysts in ultrasound images and predict the appropriate position of a pair of salient anatomical landmarks to measure their size. The deep learning model adopted fine-tuned YOLOv5 for detection of renal cysts and fine-tuned UNet++ for prediction of saliency maps, representing the position of salient landmarks. Ultrasound images were input to YOLOv5, and images cropped inside the bounding box and detected from the input image by YOLOv5 were input to UNet++. For comparison with human performance, three sonographers manually placed salient landmarks on 100 unseen items of the test data. These salient landmark positions annotated by a board-certified radiologist were used as the ground truth. We then evaluated and compared the accuracy of the sonographers and the deep learning model. Their performances were evaluated using precision–recall metrics and the measurement error. The evaluation results show that the precision and recall of our deep learning model for detection of renal cysts are comparable to standard radiologists; the positions of the salient landmarks were predicted with an accuracy close to that of the radiologists, and in a shorter time.
- Access to Document (DOI)

Papers, etc., Registered in KOARA 【 Display / hide 】

Development of a bioinformatics method for genome-wide discovery of non-coding RNA genes

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2022
Deep analysis of chemical communication space using artificial intelligence technology

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2021
Analysis and control of cell function based on novel glycomics using saccharide primer method

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2015
Exploitation of pathogenic gastrointestinal bacteria specific molecular targeting therapy

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2015
Comprehensive analysis of functional non-coding RNAs in cancer genome on multistage carcinogenesis

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2014

Reviews, Commentaries, etc. 【 Display / hide 】

Guest Editorial for the 16th Asia Pacific Bioinformatics Conference

Yamanishi Y, Sakakibara Y, Chen Y

IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE/ACM Transactions on Computational Biology and Bioinformatics) 16 ( 1 ) 1 - 2 2019.01

ISSN 15455963
- Access to Document (DOI)
Deep learning analysis of depression severity using voice data

YOTSUI Mizuki, KUO-CHING Liang, HIROHARA Maya, KITAZAWA Momoko, YOSHIMURA Michitaka, EGUCHI Yoko, FUJITA Takanori, KISHIMOTO Taishiro, SAKAKIBARA Yasubumi

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence) 2018 ( 0 ) 4C2OS27b02 - 4C2OS27b02 2018

　View Summary

<p>精神疾患の診断は現在，問診に基づく医師の主観的判断によって行われている．このような現在の診断方法は医師の経験に強く依存するため，正確な診断を行うための客観的な診断方法を開発する必要があると言われている．したがって我々の目標は，デバイスによって記録されたデータからうつ病患者の重症度を客観的に計算する深層学習手法を構築することである．本研究では，うつ病患者と健常者を音声データで分類する深層学習プログラムを開発する．</p>
- Access to Document (DOI)
新たなステージに入ったアサガオ研究。高精度なゲノム解読と幻の黄色いアサガオの再現

星野敦, 仁田坂英二, V. Jayakumar, 榊原康文

化学と生物 56 39 - 46 2018
Stmm1a(Skin tumour modifier of MSM/Ms)に位置するPak1はその発現低下により細胞増殖を抑制する

吉澤康博, 奥村和弘, 齋藤慈, 宗形春花, 青戸良賢, 磯貝恵理子, 榊原康文, 若林雄一

日本癌学会総会記事 (日本癌学会) 76回 P - 1009 2017.09

ISSN 0546-0476
がん細胞株におけるderived RNAのプロファイル解析

青木言太, 土谷麻里子, 小坂威雄, 長谷純崇, 佐藤健吾, 水野隆一, 大家基嗣, 榊原康文

日本RNA学会年会要旨集 19th 2017

Presentations 【 Display / hide 】

MetaVelvet : An extension of Velvet assembler to de novo metagenome assembly from short sequence reads

Namiki, T., Hachiya, T., Tanaka, H., and SAKAKIBARA YASUBUMI

ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011 (Chicago, USA) ,

2011.08
,
Oral presentation (general)
Comprehensive analysis of small non-coding RNAs in medaka transcriptome by deep RNA-seq approach

Abe, M., Hase, S., Ogawa, M., Okada, Y., Sato, K., Saito, Y., and SAKAKIBARA YASUBUMI

RNA 2011 Sixteenth Annual Meeting of the RNA Society (Kyoto, Japan) ,

2011.06
,
Oral presentation (general)
Genome-wide detections of non-coding RNAs on Ciona intestinalis genome: from in silico search of snoRNA to full-length sequencing and expression analysis

Kawarama, J., Hase, S., Hachiya, T., Hotta, K., Sakakibara, Y.

5th International Tunicate Meeting (Okinawa, Japan) ,

2009.06
,
Oral presentation (general)
Discriminative Detection of Cis-acting Regulatory Variation from Location Data

Yuji Kawada, Yasubumi Sakakibara

The 4th Asia-Pacific Bioinformatics Conference (APBC2006), Taiwan (Taiwan) ,

2006.02
,
Oral presentation (general)
Intensive in vitro experiments of implementing and executing finite automata in test tube

Junna Kuramochi and Yasubumi Sakakibara

The 11th International Meeting on DNA Based Computers (DNA11), London, Ontario (London, Ontario) ,

2005.06
,
Oral presentation (general)

Research Projects of Competitive Funds, etc. 【 Display / hide 】

Construction of chemical latent space using deep learning and design of artificial molecular structures

2023.04

-

2028.03

日本学術振興会, 科学研究費助成事業学術変革領域研究(A), 学術変革領域研究(A), Principal investigator
天然物が織り成す化合物潜在空間が拓く生物活性分子デザイン

2023.04

-

2028.03

日本学術振興会, 科学研究費助成事業, 学術変革領域研究(A), No Setting
Pathological diagnosis based on integration of morphological images and multi-layer omics data by artificial intelligence

2021.04

-

2024.03

Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), No Setting

　View Summary

腎細胞がん104症例の手術検体の病理プレパラートのうち、核異形度分類Fuhrman gradeが最も高度である領域と、面積的に最も優位なFuhrman gradeを示す領域を、種々の倍率で撮影した光学顕微鏡画像と、バーチャルスライドデータを深層学習に供した。転移学習に用いるため、肝・肺・心・胃等正常主要臓器の光学顕微鏡画像・バーチャルスライドデータを同様に深層学習に供した。自己符号化器AutoEncoderを用いた前処置により画像を圧縮し、畳み込みニューラールネットワーク (convolution neural network [CNN])に投入して、入力画像に対してCpGアイランドメチル化形質 (CIMP)陽性・陰性の2値分類を行った。CIMP陽性・陰性を判定するためのより高い曲線下面積 (area under the curve [AUC])を得るための、適切な光学顕微鏡画像の倍率ならびにデータ圧縮方法を明らかにした。モデル構築における、バーチャルスライド画像の有用性が示された。画像認識データセットImageNetを用いたパラメータ学習モデルInception version 3ではなく、我々自身が撮影した主要臓器の多数の光学顕微鏡画像から転移学習を行う方が、十分なAUCを獲得できることがわかった。Gradient-weighted class activation mapping (Grad-CAM)を用いて、CNNモデルがCIMP陽性・陰性の判別時に病理画像のどの領域に着目しているか可視化した。全症例・全分割画像における可視化結果を、病理専門医とバイオインフォマティシャンが討議しつつ確認した。また複数の病理専門医が、CNNモデルの着眼点を、クロマチンパターン等の核異型・細胞異型・組織構築・細胞間接着性・細胞極性・胞巣形状・血管密度・間質細胞組成に分類し言語化した。
Genomic-style feature extraction and de novo design and synthesis of DNA sequence using deep learning

2020.11

-

2026.03

JST, CREST, Commissioned ｒesearch, Principal investigator
非コードRNA遺伝子をゲノムワイドに発見する汎用システム

2018.04

-

2023.03

MEXT,JSPS, Grant-in-Aid for Scientific Research, 榊原康文, Grant-in-Aid for Scientific Research (A) , Principal investigator

Intellectual Property Rights, etc. 【 Display / hide 】

核酸分子を用いた新規な情報解析方法およびそれを用いた核酸の解析方法

Date applied：特願2000-382449 2000.12

Patent
核酸分子を用いた新規な情報解析方法およびそれを用いた核酸の解析方法

Date applied：特願2000-382449 2000.12

Patent
Data Sorting,Data Sorting Tree Creating,Derivative

Date issued： United State Patent,No.5787426 1998.07

Patent
Data Sorting,Data Sorting Tree Creating,Derivative

Date issued： United State Patent,No.5787426 1998.07

Patent

Awards 【 Display / hide 】

東京電機大学研究振興会　教育奨励賞

榊原康文, 1999, 東京電機大学研究振興会, 「プログラム言語のマルチメディア教育」
東京電機大学研究振興会教育奨励賞

榊原康文, 1999, 東京電機大学研究振興会, 「プログラム言語のマルチメディア教育」

Courses Taught 【 Display / hide 】

TOPICS IN SYSTEMS BIOLOGY

2024
TOPICS IN BIOSCIENCES AND INFORMATICS 1

2024
SEMINAR IN BIOSCIENCES AND INFORMATICS

2024
METHODOLOGY FOR POST-GENOME BIOSCIENCES

2024
INTRODUCTION TO BIOLOGY TODAY

2024

Courses Previously Taught 【 Display / hide 】

Bioinformatics

Keio University

2015.04

-

2016.03
,
Spring Semester, Lecture, Lecturer outside of Keio, 50people
Post-genome science

Keio University

2015.04

-

2016.03
,
Spring Semester, Lecture, Lecturer outside of Keio, 50people
BioProgramming 1

Keio University

2015.04

-

2016.03
,
Spring Semester, Lecture, Lecturer outside of Keio, 50people
Bio-science exercise A

Keio University

2015.04

-

2016.03
,
Spring Semester, Laboratory work/practical work/exercise, Outside own faculty (within Keio), 50people
基礎生命実験

Keio University

2015.04

-

2016.03
,
Autumn Semester, Laboratory work/practical work/exercise, Outside own faculty (within Keio), 50people

Social Activities 【 Display / hide 】

第８回新しい RNA/RNP を見つける会

2009.09
第８回新しい RNA/RNP を見つける会

2009.09
6th International Workshop on Algorithmic Learning

1995.10

-

Present
6th International Workshop on Algorithmic Learning

1995.10

-

Present
（財）新世代コンピュータ技術開発機構蛋白質立体構造予測タスクグループ

1994.04

-

1995.03

Memberships in Academic Societies 【 Display / hide 】

16th International Conference on DNA Computing and Molecular Programming,

2010.07
20th International Conference on Genome Informatics(GIW2009),

2009.12
人工知能学会人工知能基礎論研究会,

1998.04

-

2002.03
情報処理学会論文誌編集委員会,

1998.04

-

2002.03
第44回情報処理学会全国大会　チュートリアルセッション　機械学習入門,

1992

-

Present

Committee Experiences 【 Display / hide 】

2010.07

Program Committee Co-Chairman, 16th International Conference on DNA Computing and Molecular Programming
2010.07

Program Committee Co-Chairman, 16th International Conference on DNA Computing and Molecular Programming
2010.07

プログラム委員長, 16th International Conference on DNA Computing and Molecular Programming
2009.12

organizing chairman, 20th International Conference on Genome Informatics(GIW2009)
2009.12

organizing chairman, 20th International Conference on Genome Informatics(GIW2009)