(4):504–505.https://doi.org/10.1093/bio
informatics/bts728
- Hocking TD, Goerner-Potvin P, Morin A,
Shao X, Pastinen T, Bourque G (2017) Opti-
mizing ChIP-seq peak detectors using visual
labels and supervised machine learning. Bio-
informatics 33(4):491–499.https://doi.org/
10.1093/bioinformatics/btw672 - Hepler NL, Scheffler K, Weaver S, Murrell B,
Richman DD, Burton DR, Poignard P, Smith
DM, Kosakovsky Pond SL (2014) IDEPI:
rapid prediction of HIV-1 antibody epitopes
and other phenotypic features from sequence
data using a flexible machine learning plat-
form. PLoS Comput Biol 10(9):e1003842.
https://doi.org/10.1371/journal.pcbi.
1003842 - Somarowthu S, Ondrechen MJ (2012)
POOL server: machine learning application
for functional site prediction in proteins. Bio-
informatics 28(15):2078–2079.https://doi.
org/10.1093/bioinformatics/bts321 - Ryvkin P, Leung YY, Ungar LH, Gregory BD,
Wang LS (2014) Using machine learning and
high-throughput RNA sequencing to classify
the precursors of small non-coding RNAs.
Methods 67(1):28–35.https://doi.org/10.
1016/j.ymeth.2013.10.002 - Spinella JF, Mehanna P, Vidal R, Saillour V,
Cassart P, Richer C, Ouimet M, Healy J, Sin-
nett D (2016) SNooPer: a machine learning-
based method for somatic variant identifica-
tion from low-pass next-generation sequenc-
ing. BMC Genomics 17(1):912.https://doi.
org/10.1186/s12864-016-3281-2 - Heinonen M, Shen H, Zamboni N, Rousu J
(2012) Metabolite identification and molecu-
lar fingerprint prediction through machine
learning. Bioinformatics 28(18):2333–2341.
https://doi.org/10.1093/bioinformatics/
bts437 - Wu SG, Wang Y, Jiang W, Oyetunde T, Yao R,
Zhang X, Shimizu K, Tang YJ, Bao FS (2016)
Rapid prediction of bacterial heterotrophic
fluxomics using machine learning and con-
straint programming. PLoS Comput Biol 12
(4):e1004838. https://doi.org/10.1371/
journal.pcbi.1004838 - Yu T, Jones DP (2014) Improving peak detec-
tion in high-resolution LC/MS metabolo-
mics data using preexisting knowledge and
machine learning approach. Bioinformatics
30(20):2941–2948. https://doi.org/10.
1093/bioinformatics/btu430 - Vervier K, Mahe P, Tournoud M, Veyrieras
JB, Vert JP (2016) Large-scale machine
learning for metagenomics sequence classifi-
cation. Bioinformatics 32(7):1023–1032.
https://doi.org/10.1093/bioinformatics/
btv683
- Pasolli E, Truong DT, Malik F, Waldron L,
Segata N (2016) Machine learning meta-
analysis of large metagenomic datasets: tools
and biological insights. PLoS Comput Biol 12
(7):e1004977. https://doi.org/10.1371/
journal.pcbi.1004977 - Pybus M, Luisi P, Dall’Olio GM,
Uzkudun M, Laayouni H, Bertranpetit J,
Engelken J (2015) Hierarchical boosting: a
machine-learning framework to detect and
classify hard selective sweeps in human popu-
lations. Bioinformatics 31(24):3946–3952.
https://doi.org/10.1093/bioinformatics/
btv493 - Magnan CN, Baldi P (2014) SSpro/ACCpro
5: almost perfect prediction of protein sec-
ondary structure and relative solvent accessi-
bility using profiles, machine learning and
structural similarity. Bioinformatics 30
(18):2592–2597.https://doi.org/10.1093/
bioinformatics/btu352 - Cao R, Adhikari B, Bhattacharya D, Sun M,
Hou J, Cheng J (2017) QAcon: single model
quality assessment using protein structural
and contact information with machine
learning techniques. Bioinformatics 33
(4):586–588. https://doi.org/10.1093/bio
informatics/btw694 - Gangal R, Sharma P (2005) Human pol II
promoter prediction: time series descriptors
and machine learning. Nucleic Acids Res 33
(4):1332–1336. https://doi.org/10.1093/
nar/gki271 - Mort M, Sterne-Weiler T, Li B, Ball EV, Coo-
per DN, Radivojac P, Sanford JR, Mooney SD
(2014) MutPred Splice: machine learning-
based prediction of exonic variants that dis-
rupt splicing. Genome Biol 15(1):R19.
https://doi.org/10.1186/gb-2014-15-1-
r19 - Busser BW, Taher L, Kim Y, Tansey T, Bloom
MJ, Ovcharenko I, Michelson AM (2012) A
machine learning approach for identifying
novel cell type-specific transcriptional regula-
tors of myogenesis. PLoS Genet 8(3):
e1002531. https://doi.org/10.1371/jour
nal.pgen.1002531 - Sutphin GL, Mahoney JM, Sheppard K, Wal-
ton DO, Korstanje R (2016) WORMHOLE:
novel least diverged ortholog prediction
through machine learning. PLoS Comput
Biol 12(11):e1005182.https://doi.org/10.
1371/journal.pcbi.1005182 - Li F, Li C, Wang M, Webb GI, Zhang Y,
Whisstock JC, Song J (2015) GlycoMine: a
machine learning-based approach for
200 Xiang-tian Yu et al.