| Presentation | 2015-06-23 Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Background Feature extraction (FE) is difficult, particularly if there are more features than samples, as smallsample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicateFE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Results Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) wasextended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, weretested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FEboth performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heartdisease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouseheart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatmentand control samples, and significant, negative correlation with one another. Moreover, greater stability and biologicalfeasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drugdiscovery was performed as translational validation of the methods. Conclusions Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulateddata, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods havesuggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drugdiscovery. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | principal component analysis / feature extraction / bioinformatics / FAMS / chooseLD |
| Paper # | IBISML2015-1 |
| Date of Issue | 2015-06-16 (IBISML) |
| Conference Information | |
| Committee | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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| Conference Date | 2015/6/23(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Institute of Science and Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine Learning Approach to Biodata Mining, and General |
| Chair | Toshimichi Saito(Hosei Univ.) / Masakazu Sekijima(東工大) / Takashi Washio(Osaka Univ.) / Hayaru Shouno(電通大) |
| Vice Chair | Shigeo Sato(Tohoku Univ.) / / Kenji Fukumizu(ISM) / Masashi Sugiyama(Tokyo Inst. of Tech.) |
| Secretary | Shigeo Sato(Kyushu Inst. of Tech.) / (Kyoto Sangyo Univ.) / Kenji Fukumizu(京大) / Masashi Sugiyama(お茶の水女子大) / (OIST) |
| Assistant | Hiroyuki Kanbara(Tokyo Inst. of Tech.) / Hisanao Akima(Tohoku Univ.) / / Koji Tsuda(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Special Interest Group on Bioinformatics and Genomics / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Mathematical Modeling and Problem Solving |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease |
| Sub Title (in English) | |
| Keyword(1) | principal component analysis |
| Keyword(2) | feature extraction |
| Keyword(3) | bioinformatics |
| Keyword(4) | FAMS |
| Keyword(5) | chooseLD |
| 1st Author's Name | Y-h. Taguchi |
| 1st Author's Affiliation | Chuo University(Chuo Univ) |
| 2nd Author's Name | Mitsuo Iwadate |
| 2nd Author's Affiliation | Chuo University(Chuo Univ) |
| 3rd Author's Name | Hideaki Umeyama |
| 3rd Author's Affiliation | Chuo University(Chuo Univ) |
| Date | 2015-06-23 |
| Paper # | IBISML2015-1 |
| Volume (vol) | vol.115 |
| Number (no) | IBISML-112 |
| Page | pp.pp.1-8(IBISML), |
| #Pages | 8 |
| Date of Issue | 2015-06-16 (IBISML) |