Selected Publications
International Journal Papers
    2024
  1. T.-H. Yang*, and G.-L. He, “Identifying pathogenic variants in defective mismatch repair genes to assist colorectal cancer detection,” (Submitting).
  2. T.-H. Yang, X.-W. Li, Y.-H. Lee, H.-C. Lee*, and W.-S. Wu*, “miTarCLASH: a comprehensive miRNA target database based on chimera read-based experiments,” (Submitting).
  3. T.-H. Yang*, C.-C. Liao, Y.-Y. Chen, C.-L. Hsieh, W.-C. Tsai, Y.-Y. Tseng, W.-S. Wu*, “NoAC: an automatic builder for knowledge bases and query interfaces on genomes of non-model organisms,” (Submitting).
  4. T.-H. Yang*, Y.-H. Yu†, S.-H. Wu†, F.-Y. Zhang†, H.-C. Tsai†, and Y.-C. Yang†, “DMLS: an automated pipeline to extract the Drosophila modular transcription regulators and targets from massive literature articles,” (Under Review).
    †: These authors contributed equally
  5. T.-H. Yang, G.-D. Syu, G.-R. Chen, S.-E. Jhong, P.-H. Lin, P.-C. lin, Y.-C. Wang, Pramod Shah, Y.-Y. Tseng, C.-S. Chen* and W.-S. Wu*, “BAPCP: a comprehensive and user-friendly web tool for identifying biomarkers from protein microarray technologies,” (Under Review).
  6. T.-H. Yang*, “DEBFold: computational identification of RNA secondary structures for sequences across structural families using deep learning,” Journal of Chemical Information and Modeling, 2024 (Accepted) (SCI 2022 impact factor = 5.6 Ranking 18.3% (11/60) in Chemistry, Medicinal).
    DEBFold can be accessed at https://cobis.bme.ncku.edu.tw/DEBFold/.
  7. T.-H. Yang, J.-C. Chen, S.-H. Wu†, F.-Y. Chang†, Y.-C. Huang, M.-H. Lee, Y.-Y. Tseng, and W.-S. Wu*, “Identifying human miRNA target sites via learning the interaction patterns between miRNA and mRNA segments,” Journal of Chemical Information and Modeling, vol. 64 (7), pp. 2445–2453, 2024 (SCI 2022 impact factor = 5.6 Ranking 18.3% (11/60) in Chemistry, Medicinal).
    †: These authors contributed equally
    8. 2023
  8. T.-H. Yang†, Y.-H. Huang†, Y.-H. Lee, L.-S. Chang, K.-D. Chen, W.-S. Wu*, and H.-C. Kuo*, “KDpredictor: identifying Kawasaki disease based on regular blood sample features,” (Under Review).
    †: Co-first author
  9. T.-H. Yang*, Z.-Y. Liao, Y.-H. Yu, and M. Hsia, “RDDL: a systematic ensemble pipeline tool that streamlines balancing training schemes to reduce the effects of data imbalance in rare-disease-related deep-learning applications,” Computational Biology and Chemistry, vol. 106, p. 107929, 2023 (SCI 2022 impact factor = 3.1 Ranking 38.7% (43/111) in Biology).
    RDDL can be downloaded at https://github.com/cobisLab/RDDL/.
  10. T.-H. Yang*, Y.-H. Yu†, S.-H. Wu†, and F.-Y. Zhang†, “CFA: an explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes,” Computers in Biology and Medicine, vol. 152, p. 106375, 2023 (SCI 2021 impact factor = 6.698 Ranking 10.5% (6/57) in Mathematical & Computational Biology).
    †: These authors contributed equally
    CFA can be downloaded at https://github.com/cobisLab/CFA/.
    11. 2022
  11. T.-H. Yang, C.-W. Hsu, Y.-X. Wang, C.-H. Yu, Jagat Rathodd, Y.-Y. Tseng, and W.-S. Wu*, “YMLA: a comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast,” Computers in Biology and Medicine, vol. 151, p. 106314, 2022 (SCI 2021 impact factor = 6.698 Ranking 10.5% (6/57) in Mathematical & Computational Biology).
  12. T.-H. Yang*, C.-Y. Wang†, H.-C. Tsai†, Y.-C. Yang†, and C.-T. Liu, “YTLR: extracting yeast transcription factor-gene associations from the literature using automated literature readers,” Computational and Structural Biotechnology Journal, vol. 20, pp. 4636-4644, 2022 (SCI 2021 impact factor = 6.155, Ranking 23.6% (70/296) in Biochemistry & Molecular Biology).
    †: These authors contributed equally
    YTLR can be downloaded at https://github.com/cobisLab/YTLR/.
  13. T.-H. Yang*, Y.-C. Lin†, M. Hsia†, and Z.-Y. Liao†, “SSRTool: a web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability,” Computational and Structural Biotechnology Journal, vol. 20, pp. 2473-2483, 2022 (SCI 2020 impact factor = 7.271, Ranking 15% (45/295) in Biochemistry & Molecular Biology).
    †: These authors contributed equally
    SSRTool is available at https://cobis.bme.ncku.edu.tw/SSRTool/.
  14. W.-S. Wu†, T.-H. Yang†, K.-D. Chen, P.-H. Lin, G.-R. Chen, and H.-C. Kuo*, “KDmarkers: A biomarker database for investigating epigenetic methylation and gene expression levels in Kawasaki disease,” Computational and Structural Biotechnology Journal, vol. 20, pp. 1295-1305, 2022 (SCI 2020 impact factor = 7.271, Ranking 15% (45/295) in Biochemistry & Molecular Biology).
    †: co-first authors
  15. T.-H. Yang*, Y.-C. Yang† and K.-C. Tu†, “regCNN: identifying Drosophila genome-wide cis-regulatory modules via integrating the local patterns in epigenetic marks and transcription factor binding motifs,” Computational and Structural Biotechnology Journal, vol. 20, pp. 296-308, 2022 (SCI 2020 impact factor = 7.271, Ranking 15% (45/295) in Biochemistry & Molecular Biology).
    †: These authors contributed equally
    The regCNN download website is now moved to https://cobis.bme.ncku.edu.tw/regCNN/.
  16. T.-H. Yang*, “An aggregation method to identify the RNA meta-stable secondary structure and its functionally interpretable structure ensemble,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 1, pp. 75–86, 2022 (SCI 2020 impact factor = 3.710, Ranking 12% (15/125) in Statistics & Probability).
    17. 2021
  17. T.-H. Yang, S.-C. Shiue, K.-Y. Chen, Y.-Y. Tseng, and W.-S. Wu*, “Identifying piRNA targets on mRNAs in C. elegans using a deep multi-head attention network,” BMC Bioinformatics, vol. 22, no. 1, pp. 1-23, 2021 (SCI 2020 impact factor = 3.169, Ranking 27.6% (16/58) in Mathematical & Computational Biology).
  18. T.-H. Yang, Y.-H. Chiang, S.-C. Shiue, P.-H. Lin, Y.-C. Yang†, K.-C. Tu†, Y.-Y. Tseng, J.-T. Tseng*, and W.-S. Wu*, “Cancer DEIso: An integrative analysis platform for investigating differentially expressed gene-level and isoform-level human cancer markers,” Computational and Structural Biotechnology Journal, vol. 19, pp. 5149-5159, 2021 (SCI 2020 impact factor = 7.271, Ranking 15% (45/295) in Biochemistry & Molecular Biology).
    †: These authors contributed equally
  19. T.-H. Yang*, C.-Y. Wang†, H.-C. Tsai†, and C.-T. Liu†, “Human IRES Atlas: an integrative platform for studying IRES-driven translational regulation in humans,” Database, vol. 2021: article ID baab025; doi:10.1093/database/baab025, 2021 (SCI 2020 impact factor = 3.451, Ranking 24% (14/58) in Mathematical & Computational Biology).
    †: These authors contributed equally
    The website of Human IRES Atlas is now moved to https://cobis.bme.ncku.edu.tw/Human_IRES_Atlas/.
    20. 2019
  20. T.-H. Yang*, “Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast,” BMC Bioinformatics, vol. 20, no. 23, pp. 1-16, 2019. (SCI 2018 impact factor = 2.511, Ranking 15.3% (9/59) in Mathematical & Computational Biology).
    21. 2014
  21. T.-H. Yang, C.-C. Wang, P.-C. Hung, and W.-S. Wu*, “cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila,” BMC Systems Biology, vol. 8, no. Suppl 4, p. S8, 2014. (SCI 2013 impact factor = 2.853, Ranking 13% (7/52) in Mathematical & Computational Biology).
  22. T.-H. Yang, H.-T. Chang, E. S. Hsiao, J.-L. Sun, C.-C. Wang, H.-Y. Wu, P.-C. Liao*, and W.-S. Wu*, “iPhos: toolkit to streamline the alkaline phosphatase assisted comprehensive LC-MS phosphoproteome investigation,” BMC Bioinformatics, vol. 15, no. Suppl 16, p. S10, 2014. (SCI 2013 impact factor = 2.672, Ranking 15% (8/52) in Mathematical & Computational Biology).
  23. P.-C. Hung, T.-H. Yang, H.-J. Liao*, and W.-S. Wu*, “The Yeast Nucleosome Atlas (YNA) database: An integrative gene mining platform for studying chromatin structure and its regulation in yeast,” BMC Genomics, vol. 15, no. Suppl 9, p. S5, 2014. (SCI 2013 impact factor = 4.041, Ranking 18% (29/165) in Biotechnology & Applied Microbiology).
  24. T.-H. Yang, C.-C. Wang, Y.-C. Wang, and W.-S. Wu*, “YTRP: a repository for yeast transcriptional regulatory pathways,” Database, vol. 2014: article ID bau014; doi:10.1093/database/bau014, 2014. (SCI 2013 impact factor = 4.457 , Ranking 9.6% (5/52) in Mathematical & Computational Biology).
    25. 2013
  25. F.-J. Lai, C.-C. Chiu, T.-H. Yang, Y.-M. Huang, and W.-S. Wu*, “Identifying functional transcription factor binding sites in yeast by considering their positional preference in the promoters,” PLOS ONE, vol. 8, no. 12, p. e83791, 2013. (SCI 2012 impact factor = 3.73, Ranking 13% (7/56) in Multidisciplinary Sciences).
  26. T.-H. Yang and W.-S. Wu*, “Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data,” BMC Systems Biology, vol. 7, no. Suppl 6, p. S13, 2013. (SCI 2012 impact factor = 2.982, Ranking 15% (7/47) in Mathematical & Computational Biology).
    27. 2012
  27. T.-H. Yang and W.-S. Wu*, “Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data,” BMC Systems Biology, vol. 6, no. 1, p. 102, 2012. (SCI 2011 impact factor = 3.15, Ranking 9% (4/47) in Mathematical & Computational Biology).
Conference Talks and Posters
International Conference
  1. G.-L. He and T.-H. Yang*, “ Identifying colorectal cancer-related gene variants using deep learning. " In the 8th International Conference on Medical and Health Informatics (ICMHI 2024): Yokohama, Japan. (2024 May) (Poster).
  2. C.-C. Lee and T.-H. Yang*, “Developing an accurate internal entry site identification tool via a data cleaning and deep learning pipeline. " In the 12th International Conference on Bioinformatics and Computational Biology (ICBCB 2024): Tokyo, Japan. (2024 Mar.) (Oral).
  3. G.-L. He, Y.-A. Kuo, and T.-H. Yang*, “Identifying the target genes of cis-regulatory modules using deep learning. " In the 12th International Conference on Bioinformatics and Computational Biology (ICBCB 2024): Tokyo, Japan. (2024 Mar.) (Poster).
  4. Y.-A. Kuo, G.-L. He, and T.-H. Yang*, “Identifying genomic cis-regulatory module interactions in Drosophila using deep learning. " In the 32th International Conference on Genome Informatics (GIW): Singapore, Singapore. (2023 Nov.) (Poster).
  5. T.-H. Yang*, Y.-H. Yu†, S.-H. Wu†, F.-Y. Zhang†, H.-C. Tsai†, Y.-C. Yang†, Y.-Y. Tseng, and W.-S. Wu*, “DMLS: an automated pipeline to extract the Drosophila modular transcription regulators and targets from massive literature articles.” In the 21th Asia Pacific Bioinformatics Conference (APBC): Changsha, China. (2023 Apr.) (Poster).
  6. Y.-H. Yu, Y.-C. Lin, and T.-H. Yang*, “A multiple-species RNA secondary structure functional analysis platform.” In the 20th Asia Pacific Bioinformatics Conference (APBC): Virtual (2022 Apr.) (Oral-Poster).
  7. R.-Q. Hong, T. Gao, Y.-H. Yu, and T.-H. Yang*, “Annotating the genome-wide cis-regulatory modules in Drosophila via a deep channel-attention network.” In the 20th International Conference on Bioinformatics (InCoB): Virtual (2021 Nov.) (Oral-Poster).
  8. T.-H. Yang*, “An aggregation method to identify the RNA meta-stable secondary structure and its functionally interpretable structure ensemble.” In the 19th Asia Pacific Bioinformatics Conference (APBC): Virtual. (2021 Feb.) (Oral).
  9. Y.-H. Yu, J.-X. Xu, C.-F. Liao, and T.-H. Yang*, “Automatic transcriptional factor-gene interaction liter- ature evidence extraction via temporal convolutional neural networks.” In the 19th International Conference on Bioinformatics (InCoB): Virtual (2020 Nov.) (Oral-Poster).
  10. Y.-C. Lin and T.-H. Yang*, “Novel biological metrics for evaluating the functional significance of RNA secondary structure predictions.” In the 19th International Conference on Bioinformatics (InCoB): Virtual (2020 Nov.) (Oral-Poster).
  11. D.-Y. Guo and T.-H. Yang*, “Melanoma detection via deep transfer learning.” In the 19th International Conference on Bioinformatics (InCoB): Virtual (2020 Nov.) (Oral-Poster).
  12. J.-X. Xu, Z.-X. Yang, and T.-H. Yang*, “Identifying Drosophila cis-regulatory modules by a deep convolutional neural network on multiple transcriptional regulatory features.” In the 19th International Conference on Bioinformatics (InCoB): Virtual (2020 Nov.) (Oral-Poster).
  13. T.-H. Yang*, “Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast.” In the 30th International Conference on Genome Informatics (GIW): Sydney, Australia. (2019 Dec.) (Oral).
  14. T.-H. Yang, H.-T. Chang, E.SL. Hsiao, J.-L. Sun, C.-C Wang, H.-Y Wu, P.-C. Liao* and W.-S. Wu*, “iPhos: toolkit to streamline the alkaline phosphatase assisted comprehensive LC-MS phosphorproteome investigation.” In the 13th International Conference on Bioinformatics (InCoB): Sydney, Australia. (2014 July) (Oral).
  15. T.-H. Yang, C.-C Wang, P.-C Hung and W.-S. Wu*, “cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila.” In the 13th International Conference on Bioinformatics (InCoB): Sydney, Australia. (2014 July) (Oral).
  16. P.-C Hung, T.-H. Yang, H.-J Liaw* and W.-S. Wu*, “YNA: an integrative gene mining platform for studying chromatin structure and its regulation in Yeast.” In the 13th International Conference on Bioinformatics (InCoB): Sydney, Australia. (2014 July) (Oral).
  17. T.-H. Yang and W.-S. Wu*, “Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data.” In the 24rd International Conference on Genome Informatics (GIW): Biopolis, Singapore. (2013 Dec.) (Oral).
  18. T.-H. Yang and W.-S. Wu*, “YTRP: Yeast transcriptional regulatory pathway database.” In International Conference on Systems Biology (ICSB): Toronto, Canada. (2012 Aug.) (Poster).
  19. T.-H. Yang, Y.-H. Chang, R.-L Sun, P.-C Liao and W.-S. Wu*, “A new peak alignment algorithm for LC-MS/MS data.” In RECOMB Satellite Conference on Computational Proteomics: San Diego, USA. (2011 Mar.) (Poster).
Domestic Conference
  1. Y.-A. Kou and T.-H. Yang*, “Genomic identification of the cis-regulatory module interactions based on transcription factor binding sites and epigenetics.” In 2024 SMBE Regional Meeting in Taiwan: Taipei, Taiwan. (2024 Mar.) (Poster).
  2. H.-T. Hong and T.-H. Yang*, “Predicting RNA-protein interaction using deep learning models.” In 2024 SMBE Regional Meeting in Taiwan: Taipei, Taiwan. (2024 Mar.) (Poster).
  3. G.-L. He and T.-H. Yang*, “A deep learning approach to identify the target genes of cis- regulatory modules.” In 2024 SMBE Regional Meeting in Taiwan: Taipei, Taiwan. (2024 Mar.) (Poster).
  4. H.-Y. Lin and T.-H. Yang*, “An automated tool to integrate diverse RNA-seq analysis pipeline and facilitate systematic candidate gene list extraction.” In 2024 SMBE Regional Meeting in Taiwan: Taipei, Taiwan. (2024 Mar.) (Poster).
  5. C.-Y. Wang†, K.-C. Tu†, Y.-C. Yang†, H.-C. Tsai† and T.-H. Yang*, “農作蜜棗損傷原因之高效能分類。” In 2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI): Taichung, Taiwan. (2021 Nov.) (Oral).
    †: These authors contributed equally
  6. T.-H. Yang* and T.-H. Chen, “Construction of an IRES data knowledge base for understanding the regulation of translation initiation in human.” In 2019 Multiomics and Precision Medicine Joint Conference (MoPM): Tainan, Taiwan. (2019 Dec.) (Poster).
  7. T.-H. Yang* and Z.-X. Yang, “Cis-regulatory module identification by transcription factor binding site distribution and corresponding epigenetic profiles.” In 2019 Multiomics and Precision Medicine Joint Conference (MoPM): Tainan, Taiwan. (2019 Dec.) (Poster).