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ORCIDHiroshi Mamitsuka
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- affiliation: Kyoto University, Bioinformatics Center, Japan
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2020 – today
- 2024
[j88]Duc Anh Nguyen
, Canh Hao Nguyen, Hiroshi Mamitsuka:
Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions. IEEE Trans. Neural Networks Learn. Syst. 35(8): 11620-11625 (2024)- 2023
- [j87]Zhirui Liao, Lei Xie, Hiroshi Mamitsuka, Shanfeng Zhu:
Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer. Bioinform. 39(1) (2023) - [j86]Wei Qu, Ronghui You, Hiroshi Mamitsuka, Shanfeng Zhu:
DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction. Bioinform. 39(9) (2023) - [c52]Xinyi Wang, Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka:
Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning. ECAI 2023: 2560-2567 - [i8]Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka:
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference. CoRR abs/2310.16705 (2023) - 2022
- [j85]Lizhi Liu, Hiroshi Mamitsuka, Shanfeng Zhu:
HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations. Bioinform. 38(3): 799-808 (2022) - [j84]Betül Güvenç Paltun, Samuel Kaski, Hiroshi Mamitsuka:
DIVERSE: Bayesian Data IntegratiVE Learning for Precise Drug ResponSE Prediction. IEEE ACM Trans. Comput. Biol. Bioinform. 19(4): 2197-2207 (2022) - 2021
- [j83]Duc Anh Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
A survey on adverse drug reaction studies: data, tasks and machine learning methods. Briefings Bioinform. 22(1): 164-177 (2021) - [j82]Betül Güvenç Paltun, Hiroshi Mamitsuka, Samuel Kaski:
Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Briefings Bioinform. 22(1): 346-359 (2021) - [j81]Menglan Cai, Canh Hao Nguyen, Hiroshi Mamitsuka, Limin Li:
XGSEA: CROSS-species gene set enrichment analysis via domain adaptation. Briefings Bioinform. 22(5) (2021) - [j80]Betül Güvenç Paltun, Samuel Kaski, Hiroshi Mamitsuka:
Machine learning approaches for drug combination therapies. Briefings Bioinform. 22(6) (2021) - [j79]Ronghui You, Yuxuan Liu, Hiroshi Mamitsuka, Shanfeng Zhu:
BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text. Bioinform. 37(5): 684-692 (2021) - [j78]Lizhi Liu, Hiroshi Mamitsuka, Shanfeng Zhu:
HPOFiller: identifying missing protein-phenotype associations by graph convolutional network. Bioinform. 37(19): 3328-3336 (2021) - [j77]Ronghui You, Shuwei Yao, Hiroshi Mamitsuka, Shanfeng Zhu:
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction. Bioinform. 37(Supplement): 262-271 (2021) - [j76]Kishan Wimalawarne, Hiroshi Mamitsuka:
Reshaped tensor nuclear norms for higher order tensor completion. Mach. Learn. 110(3): 507-531 (2021) - [j75]Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels. Mach. Learn. 110(7): 1585-1607 (2021) - [j74]Canh Hao Nguyen, Hiroshi Mamitsuka:
Learning on Hypergraphs With Sparsity. IEEE Trans. Pattern Anal. Mach. Intell. 43(8): 2710-2722 (2021) - [c51]Zhirui Liao, Xiaodi Huang, Hiroshi Mamitsuka, Shanfeng Zhu:
Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules. BIBM 2021: 340-347 - [i7]Betül Güvenç Paltun, Samuel Kaski, Hiroshi Mamitsuka:
DIVERSE: bayesian Data IntegratiVE learning for precise drug ResponSE prediction. CoRR abs/2104.00520 (2021) - [i6]Canh Hao Nguyen, Hiroshi Mamitsuka:
On Convex Clustering Solutions. CoRR abs/2105.08348 (2021) - [i5]Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels. CoRR abs/2106.04739 (2021) - [i4]Duc Anh Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
CentSmoothie: Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions. CoRR abs/2112.07837 (2021) - 2020
- [j73]Suyang Dai, Ronghui You, Zhiyong Lu, Xiaodi Huang, Hiroshi Mamitsuka, Shanfeng Zhu:
FullMeSH: improving large-scale MeSH indexing with full text. Bioinform. 36(5): 1533-1541 (2020) - [j72]Lizhi Liu, Xiaodi Huang, Hiroshi Mamitsuka, Shanfeng Zhu:
HPOLabeler: improving prediction of human protein-phenotype associations by learning to rank. Bioinform. 36(14): 4180-4188 (2020) - [j71]Kishan Wimalawarne, Makoto Yamada, Hiroshi Mamitsuka:
Scaled Coupled Norms and Coupled Higher-Order Tensor Completion. Neural Comput. 32(2): 447-484 (2020) - [c50]Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Mamitsuka:
Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String. AAAI 2020: 5240-5247 - [c49]Jonathan Strahl, Jaakko Peltonen, Hiroshi Mamitsuka, Samuel Kaski:
Scalable Probabilistic Matrix Factorization with Graph-Based Priors. AAAI 2020: 5851-5858
2010 – 2019
- 2019
- [j70]Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches. Briefings Bioinform. 20(6): 2028-2043 (2019) - [j69]Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra. Bioinform. 35(14): i164-i172 (2019) - [j68]Jussi Gillberg, Pekka Marttinen, Hiroshi Mamitsuka, Samuel Kaski:
Modelling G×E with historical weather information improves genomic prediction in new environments. Bioinform. 35(20): 4045-4052 (2019) - [j67]Ronghui You, Shuwei Yao, Yi Xiong, Xiaodi Huang, Fengzhu Sun, Hiroshi Mamitsuka, Shanfeng Zhu:
NetGO: improving large-scale protein function prediction with massive network information. Nucleic Acids Res. 47(Webserver-Issue): W379-W387 (2019) - [j66]Shuigeng Zhou, Yi-Ping Phoebe Chen, Hiroshi Mamitsuka:
Editorial. IEEE ACM Trans. Comput. Biol. Bioinform. 16(2): 350-351 (2019) - [c48]Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka:
Fast and Robust Multi-View Multi-Task Learning via Group Sparsity. IJCAI 2019: 3499-3505 - [c47]Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka:
Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning. IJCAI 2019: 3506-3512 - [c46]Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu:
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. NeurIPS 2019: 5812-5822 - [i3]Jonathan Strahl, Jaakko Peltonen, Hiroshi Mamitsuka, Samuel Kaski:
Scalable Probabilistic Matrix Factorization with Graph-Based Priors. CoRR abs/1908.09393 (2019) - 2018
- [j65]Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra. Bioinform. 34(13): i323-i332 (2018) - [j64]Ronghui You, Zihan Zhang, Yi Xiong, Fengzhu Sun, Hiroshi Mamitsuka, Shanfeng Zhu:
GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank. Bioinform. 34(14): 2465-2473 (2018) - [j63]Kishan Wimalawarne, Makoto Yamada, Hiroshi Mamitsuka:
Convex Coupled Matrix and Tensor Completion. Neural Comput. 30(11) (2018) - [j62]Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez, Ermin Hodzic, Raunak Shrestha, Avishek Saha, Hua Ouyang, Dawei Yin, Hiroshi Mamitsuka, Süleyman Cenk Sahinalp, Predrag Radivojac, Filippo Menczer, Yi Chang:
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data. IEEE Trans. Knowl. Data Eng. 30(7): 1352-1365 (2018) - [c45]Masayuki Karasuyama, Hiroshi Mamitsuka:
Factor Analysis on a Graph. AISTATS 2018: 1117-1126 - [c44]Junning Gao, Shuwei Yao, Hiroshi Mamitsuka, Shanfeng Zhu:
AiProAnnotator: Low-rank Approximation with network side information for high-performance, large-scale human Protein abnormality Annotator. BIBM 2018: 13-20 - [c43]Kishan Wimalawarne, Hiroshi Mamitsuka:
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms. NeurIPS 2018: 6902-6910 - [i2]Canh Hao Nguyen, Hiroshi Mamitsuka:
Learning on Hypergraphs with Sparsity. CoRR abs/1804.00836 (2018) - [i1]Ronghui You, Suyang Dai, Zihan Zhang, Hiroshi Mamitsuka, Shanfeng Zhu:
AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks. CoRR abs/1811.01727 (2018) - 2017
- [j61]Sohiya Yotsukura, David duVerle, Timothy Hancock, Yayoi Natsume-Kitatani, Hiroshi Mamitsuka:
Computational recognition for long non-coding RNA (lncRNA): Software and databases. Briefings Bioinform. 18(1): 9-27 (2017) - [j60]Sohiya Yotsukura, Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka:
Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code. Briefings Bioinform. 18(4): 619-633 (2017) - [j59]Masayuki Karasuyama, Hiroshi Mamitsuka:
Adaptive edge weighting for graph-based learning algorithms. Mach. Learn. 106(2): 307-335 (2017) - [j58]Ichigaku Takigawa, Hiroshi Mamitsuka:
Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set. IEEE Trans. Pattern Anal. Mach. Intell. 39(3): 617-624 (2017) - [c42]Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, Yi Chang:
Convex Factorization Machine for Toxicogenomics Prediction. KDD 2017: 1215-1224 - 2016
- [j57]Ahmed Mohamed, Canh Hao Nguyen, Hiroshi Mamitsuka:
Current status and prospects of computational resources for natural product dereplication: a review. Briefings Bioinform. 17(2): 309-321 (2016) - [j56]Qingjun Yuan, Junning Gao, Dongliang Wu, Shihua Zhang, Hiroshi Mamitsuka, Shanfeng Zhu:
DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinform. 32(12): 18-27 (2016) - [j55]Shengwen Peng, Ronghui You, Hongning Wang, Chengxiang Zhai, Hiroshi Mamitsuka, Shanfeng Zhu:
DeepMeSH: deep semantic representation for improving large-scale MeSH indexing. Bioinform. 32(12): 70-79 (2016) - [j54]Ahmed Mohamed, Canh Hao Nguyen, Hiroshi Mamitsuka:
NMRPro: an integrated web component for interactive processing and visualization of NMR spectra. Bioinform. 32(13): 2067-2068 (2016) - [j53]Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, Hiroshi Mamitsuka:
Mining approximate patterns with frequent locally optimal occurrences. Discret. Appl. Math. 200: 123-152 (2016) - [j52]Shuigeng Zhou, Yi-Ping Phoebe Chen, Hiroshi Mamitsuka:
Introduction to the special issue on GIW 2016. J. Bioinform. Comput. Biol. 14(5): 1602004:1-1602004:3 (2016) - [c41]Canh Hao Nguyen, Hiroshi Mamitsuka:
New Resistance Distances with Global Information on Large Graphs. AISTATS 2016: 639-647 - [c40]Junning Gao, Makoto Yamada, Samuel Kaski, Hiroshi Mamitsuka, Shanfeng Zhu:
A Robust Convex Formulation for Ensemble Clustering. IJCAI 2016: 1476-1482 - 2015
- [j51]Ke Liu, Shengwen Peng, Junqiu Wu, ChengXiang Zhai, Hiroshi Mamitsuka, Shanfeng Zhu:
MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence. Bioinform. 31(12): 339-347 (2015) - [j50]Jing Zhou, Yuxuan Shui, Shengwen Peng, Xuhui Li, Hiroshi Mamitsuka, Shanfeng Zhu:
MeSHSim: An R/Bioconductor package for measuring semantic similarity over MeSH headings and MEDLINE documents. J. Bioinform. Comput. Biol. 13(6): 1542002:1-1542002:17 (2015) - [j49]Beichen Wang, Xiaodong Chen, Hiroshi Mamitsuka, Shanfeng Zhu:
BMExpert: Mining MEDLINE for Finding Experts in Biomedical Domains Based on Language Model. IEEE ACM Trans. Comput. Biol. Bioinform. 12(6): 1286-1294 (2015) - [j48]Motoki Shiga, Hiroshi Mamitsuka:
Non-Negative Matrix Factorization with Auxiliary Information on Overlapping Groups. IEEE Trans. Knowl. Data Eng. 27(6): 1615-1628 (2015) - [c39]Xiaodong Zheng, Shanfeng Zhu, Junning Gao, Hiroshi Mamitsuka:
Instance-Wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters. IJCAI 2015: 4091-4097 - 2014
- [j47]Hao Ding, Ichigaku Takigawa, Hiroshi Mamitsuka, Shanfeng Zhu:
Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Briefings Bioinform. 15(5): 734-747 (2014) - [j46]Ahmed Mohamed, Timothy Hancock, Canh Hao Nguyen, Hiroshi Mamitsuka:
NetPathMiner: R/Bioconductor package for network path mining through gene expression. Bioinform. 30(21): 3139-3141 (2014) - [j45]Mitsunori Kayano, Motoki Shiga, Hiroshi Mamitsuka:
Detecting Differentially Coexpressed Genesfrom Labeled Expression Data: A Brief Review. IEEE ACM Trans. Comput. Biol. Bioinform. 11(1): 154-167 (2014) - [j44]Canh Hao Nguyen, Nicolas Wicker, Hiroshi Mamitsuka:
Selecting Graph Cut Solutions via Global Graph Similarity. IEEE Trans. Neural Networks Learn. Syst. 25(7): 1407-1412 (2014) - 2013
- [j43]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Mineichi Kudo, Hiroshi Mamitsuka:
Fast algorithms for finding a minimum repetition representation of strings and trees. Discret. Appl. Math. 161(10-11): 1556-1575 (2013) - [j42]Jun Gu, Wei Feng, Jia Zeng, Hiroshi Mamitsuka, Shanfeng Zhu:
Efficient Semisupervised MEDLINE Document Clustering With MeSH-Semantic and Global-Content Constraints. IEEE Trans. Cybern. 43(4): 1265-1276 (2013) - [j41]Masayuki Karasuyama, Hiroshi Mamitsuka:
Multiple Graph Label Propagation by Sparse Integration. IEEE Trans. Neural Networks Learn. Syst. 24(12): 1999-2012 (2013) - [c38]Xiaodong Zheng, Hao Ding, Hiroshi Mamitsuka, Shanfeng Zhu:
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. KDD 2013: 1025-1033 - [c37]Motoki Shiga, Hiroshi Mamitsuka:
Variational Bayes co-clustering with auxiliary information. MultiClust@KDD 2013: 5 - [c36]Masayuki Karasuyama, Hiroshi Mamitsuka:
Manifold-based Similarity Adaptation for Label Propagation. NIPS 2013: 1547-1555 - 2012
- [j40]David duVerle, Hiroshi Mamitsuka:
A review of statistical methods for prediction of proteolytic cleavage. Briefings Bioinform. 13(3): 337-349 (2012) - [j39]Lianming Zhang, Keiko Udaka, Hiroshi Mamitsuka, Shanfeng Zhu:
Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools. Briefings Bioinform. 13(3): 350-364 (2012) - [j38]Motoki Shiga, Hiroshi Mamitsuka:
Efficient semi-supervised learning on locally informative multiple graphs. Pattern Recognit. 45(3): 1035-1049 (2012) - [j37]Motoki Shiga, Hiroshi Mamitsuka:
A Variational Bayesian Framework for Clustering with Multiple Graphs. IEEE Trans. Knowl. Data Eng. 24(4): 577-590 (2012) - [j36]Timothy Hancock, Hiroshi Mamitsuka:
Boosted Network Classifiers for Local Feature Selection. IEEE Trans. Neural Networks Learn. Syst. 23(11): 1767-1778 (2012) - [j35]Canh Hao Nguyen, Hiroshi Mamitsuka:
Latent Feature Kernels for Link Prediction on Sparse Graphs. IEEE Trans. Neural Networks Learn. Syst. 23(11): 1793-1804 (2012) - [j34]Hiroshi Mamitsuka:
Mining from protein-protein interactions. WIREs Data Mining Knowl. Discov. 2(5): 400-410 (2012) - 2011
- [j33]Ichigaku Takigawa, Hiroshi Mamitsuka:
Efficiently mining δ-tolerance closed frequent subgraphs. Mach. Learn. 82(2): 95-121 (2011) - [j32]Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka:
A spectral approach to clustering numerical vectors as nodes in a network. Pattern Recognit. 44(2): 236-251 (2011) - [j31]Canh Hao Nguyen, Hiroshi Mamitsuka:
Discriminative Graph Embedding for Label Propagation. IEEE Trans. Neural Networks 22(9): 1395-1405 (2011) - [j30]Motoki Shiga, Hiroshi Mamitsuka:
Clustering genes with expression and beyond. WIREs Data Mining Knowl. Discov. 1(6): 496-511 (2011) - [c35]Canh Hao Nguyen, Hiroshi Mamitsuka:
Kernels for Link Prediction with Latent Feature Models. ECML/PKDD (2) 2011: 517-532 - 2010
- [j29]Timothy Hancock, Hiroshi Mamitsuka:
A markov classification model for metabolic pathways. Algorithms Mol. Biol. 5: 10 (2010) - [j28]Timothy Hancock, Ichigaku Takigawa, Hiroshi Mamitsuka:
Mining metabolic pathways through gene expression. Bioinform. 26(17): 2128-2135 (2010) - [j27]Limin Li, Wai-Ki Ching, Yatming Chan, Hiroshi Mamitsuka:
On network-based kernel methods for protein-protein interactions with applications in protein functions prediction. J. Syst. Sci. Complex. 23(5): 917-930 (2010) - [j26]Xihao Hu, Wenjian Zhou, Keiko Udaka, Hiroshi Mamitsuka, Shanfeng Zhu:
MetaMHC: a meta approach to predict peptides binding to MHC molecules. Nucleic Acids Res. 38(Web-Server-Issue): 474-479 (2010) - [c34]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo:
Algorithms for Finding a Minimum Repetition Representation of a String. SPIRE 2010: 185-190 - [c33]Timothy Hancock, Hiroshi Mamitsuka:
Boosted Optimization for Network Classification. AISTATS 2010: 305-312
2000 – 2009
- 2009
- [j25]Shanfeng Zhu, Jia Zeng, Hiroshi Mamitsuka:
Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity. Bioinform. 25(15): 1944-1951 (2009) - [j24]Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka:
Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. Bioinform. 25(21): 2735-2743 (2009) - [j23]Shanfeng Zhu, Ichigaku Takigawa, Jia Zeng, Hiroshi Mamitsuka:
Field independent probabilistic model for clustering multi-field documents. Inf. Process. Manag. 45(5): 555-570 (2009) - [j22]Raymond Wan, Larisa Kiseleva, Hajime Harada, Hiroshi Mamitsuka, Paul Horton:
HAMSTER: visualizing microarray experiments as a set of minimum spanning trees. Source Code Biol. Medicine 4: 8 (2009) - [c32]Raymond Wan, Vo Ngoc Anh, Hiroshi Mamitsuka:
Efficient Probabilistic Latent Semantic Analysis through Parallelization. AIRS 2009: 432-443 - [c31]Timothy Hancock, Hiroshi Mamitsuka:
A Markov Classification Model for Metabolic Pathways. WABI 2009: 121-132 - 2008
- [j21]Ichigaku Takigawa, Hiroshi Mamitsuka:
Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. Bioinform. 24(2): 250-257 (2008) - [j20]Kosuke Hashimoto, Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Minoru Kanehisa, Hiroshi Mamitsuka:
A new efficient probabilistic model for mining labeled ordered trees applied to glycobiology. ACM Trans. Knowl. Discov. Data 2(1): 6:1-6:30 (2008) - [c30]Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka:
Mining significant tree patterns in carbohydrate sugar chains. ECCB 2008: 167-173 - 2007
- [j19]Takashi Yoneya, Hiroshi Mamitsuka:
A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors. Bioinform. 23(7): 842-849 (2007) - [j18]Shanfeng Zhu, Yasushi Okuno, Gozoh Tsujimoto, Hiroshi Mamitsuka:
Predicting implicit associated cancer genes from OMIM and MEDLINE by a new probabilistic model. BMC Syst. Biol. 1(S-1): P16 (2007) - [j17]Hiroshi Mamitsuka, Naoki Abe:
Active ensemble learning: Application to data mining and bioinformatics. Syst. Comput. Jpn. 38(11): 100-108 (2007) - [c29]Shanfeng Zhu, Ichigaku Takigawa, Shuqin Zhang, Hiroshi Mamitsuka:
A Probabilistic Model for Clustering Text Documents with Multiple Fields. ECIR 2007: 331-342 - [c28]Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka:
Annotating gene function by combining expression data with a modular gene network. ISMB/ECCB (Supplement of Bioinformatics) 2007: 468-478 - [c27]Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka:
A spectral clustering approach to optimally combining numericalvectors with a modular network. KDD 2007: 647-656 - [c26]Raymond Wan, Vo Ngoc Anh, Hiroshi Mamitsuka:
Passage Retrieval with Vector Space and Query-Level Aspect Models. TREC 2007 - 2006
- [j16]Shanfeng Zhu, Keiko Udaka, John Sidney, Alessandro Sette, Kiyoko F. Aoki-Kinoshita, Hiroshi Mamitsuka:
Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules. Bioinform. 22(13): 1648-1655 (2006) - [j15]Hiroshi Mamitsuka:
Query-learning-based iterative feature-subset selection for learning from high-dimensional data sets. Knowl. Inf. Syst. 9(1): 91-108 (2006) - [j14]Hiroshi Mamitsuka:
Selecting features in microarray classification using ROC curves. Pattern Recognit. 39(12): 2393-2404 (2006) - [c25]Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Hiroshi Mamitsuka, Minoru Kanehisa:
ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains. ISMB (Supplement of Bioinformatics) 2006: 25-34 - [c24]Kosuke Hashimoto, Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Minoru Kanehisa, Hiroshi Mamitsuka:
A new efficient probabilistic model for mining labeled ordered trees. KDD 2006: 177-186 - [c23]Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka, Vo Ngoc Anh:
Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval. TREC 2006 - [c22]Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka:
Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. VDMB 2006: 40-49 - 2005
- [j13]Krzysztof J. Cios, Hiroshi Mamitsuka