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
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
2001.10

-

2002.03

東京電機大学理工学部情報科学科 ,教授
2002.04

-

2005.03

慶應義塾大学理工学部生命情報学科 ,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
Informatics / Life, health and medical informatics (Bioinformatics)

Research Keywords 【 Display / hide 】

cancer genome
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，13編6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 】

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 ） 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 ） 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

　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)
Quantum Algorithm for Position Weight Matrix Matching

Miyamoto K., Yamamoto N., Sakakibara Y.

IEEE Transactions on Quantum Engineering （IEEE Transactions on Quantum Engineering) 4 2023

　View Summary

In this article, we propose two quantum algorithms for a problem in bioinformatics, position weight matrix (PWM) matching, which aims to find segments (sequence motifs) in a biological sequence, such as DNA and protein that have high scores defined by the PWM and are, thus, of informational importance related to biological function. The two proposed algorithms, the naive iteration method and the Monte-Carlo-based method, output matched segments, given the oracular accesses to the entries in the biological sequence and the PWM. The former uses quantum amplitude amplification (QAA) for sequence motif search, resulting in the query complexity scaling on the sequence length n, the sequence motif length m, and the number of the PWMs K as O(m,Kn), which means speedup over existing classical algorithms with respect to n and K. The latter also uses QAA and, further, quantum Monte Carlo integration for segment score calculation, instead of iteratively operating quantum circuits for arithmetic in the naive iteration method; then, it provides the additional speedup with respect to m in some situation. As a drawback, these algorithms use quantum random access memories, and their initialization takes O(n) time. Nevertheless, our algorithms keep the advantage especially when we search matches in a sequence for many PWMs in parallel.
- Access to Document (DOI)
Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests

Hu M., Asami C., Iwakura H., Nakajima Y., Sema R., Kikuchi T., Miyata T., Sakamaki K., Kudo T., Yamada M., Akamizu T., Sakakibara Y.

Communications Medicine （Communications Medicine) 2 （ 1 ） 2022.12

　View Summary

Background: Approximately 2.4 million patients in Japan would benefit from treatment for thyroid disease, including Graves’ disease and Hashimoto’s disease. However, only 450,000 of them are receiving treatment, and many patients with thyroid dysfunction remain largely overlooked. In this retrospective study, we aimed to develop and conduct preliminary testing on a machine learning method for screening patients with hyperthyroidism and hypothyroidism who would benefit from prompt medical treatment. Methods: We collected electronic medical records and medical checkup data from four hospitals in Japan. We applied four machine learning algorithms to construct classification models to distinguish patients with hyperthyroidism and hypothyroidism from control subjects using routine laboratory tests. Performance evaluation metrics such as sensitivity, specificity, and the area under receiver operating characteristic (AUROC) were obtained. Techniques such as feature importance were further applied to understand the contribution of each feature to the machine learning output. Results: The results of cross-validation and external evaluation indicated that we achieved high classification accuracies (AUROC = 93.8% for hyperthyroidism model and AUROC = 90.9% for hypothyroidism model). Serum creatinine (S-Cr), mean corpuscular volume (MCV), and total cholesterol were the three features that were most strongly correlated with the hyperthyroidism model, and S-Cr, lactic acid dehydrogenase (LDH), and total cholesterol were correlated with the hypothyroidism model. Conclusions: We demonstrated the potential of machine learning approaches for diagnosing the presence of thyroid dysfunction from routine laboratory tests. Further validation, including prospective clinical studies, is necessary prior to application of our method in the clinic.
- Access to Document (DOI)

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

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
Mechanisms of the development of neuronal cell community in the central nervous system

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2013
Mechanisms of the development of neuronal cell community in the central nervous system

Sakakibara, Yasubumi

科学研究費補助金研究成果報告書 2010

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 ) 2019.01

ISSN 15455963
- Access to Document (DOI)

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

学術変革領域研究(A), Principal investigator
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, Grant-in-Aid for Scientific Research (A) , Principal investigator
Deep analysis of chemical communication space using artificial intelligence technology

2017.06

-

2022.03

MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research on Innovative Areas, Principal investigator
ビッグデータ駆動型創薬システム研究拠点

2014

-

2018

戦略的研究基盤形成支援事業, 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
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」

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
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
システムバイオロジー特論

Keio University

2015.04

-

2016.03
,
Autumn Semester, Lecture, Outside own faculty (within Keio), 50people
BioProgramming 2

Keio University

2015.04

-

2016.03
,
Autumn Semester, Lecture, Lecturer outside of Keio, 50people
Algorithm and Information processing

Keio University

2015.04

-

2016.03
,
Autumn Semester, Lecture, Within own ｆaculty, 50people

Social Activities 【 Display / hide 】

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
（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
2nd,3rd,4th,5th,6th International Colloquium on Gr

1994

-

2002
5th,7th,8th,9th,11th International Workshop on Alg

1994

-

2000

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
2009.12

organizing chairman, 20th International Conference on Genome Informatics(GIW2009)
2009.09

Committee Chair, 第８回新しい RNA/RNP を見つける会
1998.04

-

2002.03

幹事, 人工知能学会人工知能基礎論研究会
1998.04

-

2002.03

委員, 情報処理学会論文誌編集委員会