% 著作的單一來源。由 Zotero (Better BibTeX) 匯出,或手動維護。
%
% 兩個消費者:
%   core/publish.py   網站的著作清單 —— 只取標了 keywords = {selected} 的
%   core/assemble.py  投影片 deck 的 bibliography —— 可引用任何一筆
%
% 所以「想在投影片引用但不想放上網站」的論文,不要標 selected 就好。
% 欄位契約見 SCHEMA.md;doi 一律寫裸的 10.xxxx/…,不要帶網址前綴。
% 完整著作列表在 Google Scholar,這裡只放精選。

@article{kahn2023topological,
  author        = {Kahn, Tatyana G and Savitsky, Mikhail and Kuong, Chikuan and
                   Jacquier, Caroline and Cavalli, Giacomo and Chang, Jia-Ming and
                   Schwartz, Yuri B},
  % 學名用 markdown 斜體:網站直接吃,pandoc 的 citeproc 也認得
  title         = {Topological screen identifies hundreds of Cp190- and CTCF-dependent
                   *Drosophila* chromatin insulator elements},
  journal       = {Science Advances},
  year          = {2023},
  doi           = {10.1126/sciadv.ade0090},
  corresponding = {Jia-Ming Chang and Yuri B Schwartz},
  keywords      = {selected},
  why      = {A topological screen in *Drosophila* cells lacking CTCF and Cp190 identifies
             hundreds of insulator elements that modulate local chromatin interactions without
             driving global genome folding, suggesting Cp190 functions by promoting insulator
             protein cobinding rather than loop formation.},
}

@article{lin2019ms2cnn,
  author   = {Lin, Yang-Ming and Chen, Ching-Tai and Chang, Jia-Ming},
  title    = {MS2CNN: predicting MS/MS spectrum based on protein sequence using
              deep convolutional neural networks},
  journal  = {BMC Genomics},
  year     = {2019},
  doi      = {10.1186/s12864-019-6297-6},
  links    = {code: https://github.com/changlabtw/MS2CNN},
  keywords = {selected},
  corresponding = {Jia-Ming Chang},
  why      = {Predicted MS2 spectra improve the sensitivity and coverage of peptide
             identification. MS2CNN was among the first deep-learning approaches to this problem,
             outperforming MS2PIP, matching pDeep overall, and performing best on the hardest 3+
             charge peptides.},
}

@article{chang2021bootstrap,
  author   = {Chang, Jia-Ming and Floden, Evan W and Herrero, Javier and
              Gascuel, Olivier and Di Tommaso, Paolo and Notredame, Cedric},
  title    = {Incorporating alignment uncertainty into Felsenstein's phylogenetic
              bootstrap to improve its reliability},
  journal  = {Bioinformatics},
  % 以正式出版(print)為準。online first 是 2019-02,兩個年份都看得到,
  % 所以在這裡定調一次,不要每次引用都重新判斷。
  year     = {2021},
  volume   = {37},
  number   = {11},
  pages    = {1506--1514},
  doi      = {10.1093/bioinformatics/btz082},
  links    = {code: https://github.com/changlabtw/wpSBOOT, docs: https://changlabtw.github.io/wpSBOOT/},
  keywords = {selected},
  note     = {online first 2019-02},
  why      = {Sampling alignment columns across alternative aligners enhances the discriminative
             power of bootstrap support regarding tree topology correctness.},
}

@article{chang2014tcs,
  author   = {Chang, Jia-Ming and Di Tommaso, Paolo and Notredame, Cedric},
  title    = {TCS: A New Multiple Sequence Alignment Reliability Measure to Estimate
              Alignment Accuracy and Improve Phylogenetic Tree Reconstruction},
  journal  = {Molecular Biology and Evolution},
  year     = {2014},
  doi      = {10.1093/molbev/msu117},
  links    = {web: http://tcoffee.crg.cat/core},
  keywords = {selected},
  why      = {The reliability of a multiple sequence alignment can be robustly quantified using
             the Transitive Consistency Score, derived from the T-Coffee pairwise library.},
}

@article{chang2012psicoffee,
  author   = {Chang, Jia-Ming and Di Tommaso, Paolo and Taly, Jean-François F and
              Notredame, Cedric},
  title    = {Accurate Multiple Sequence Alignment of Transmembrane Proteins with
              PSI-Coffee},
  journal  = {BMC Bioinformatics},
  year     = {2012},
  doi      = {10.1186/1471-2105-13-s4-s1},
  links    = {web: http://tcoffee.crg.cat/apps/tcoffee/do:tmcoffee},
  keywords = {selected},
  why      = {Homology extension combined with a consistency-based approach significantly improves
             the multiple sequence alignment of alpha-helical transmembrane proteins.},
}