and similarity measures. BMC Genomics 12
(Suppl 5):S1. https://doi.org/10.1186/
1471-2164-12-S5-S1- Wang Z, Wen X, Lu Y, Yao Y, Zhao H (2016)
Exploiting machine learning for predicting
skeletal-related events in cancer patients with
bone metastases. Oncotarget 7
(11):12612–12622. https://doi.org/10.
18632/oncotarget.7278 - Way GP, Allaway RJ, Bouley SJ, Fadul CE,
Sanchez Y, Greene CS (2017) A machine
learning classifier trained on cancer transcrip-
tomes detects NF1 inactivation signal in glio-
blastoma. BMC Genomics 18(1):127.
https://doi.org/10.1186/s12864-017-
3519-7 - Mani S, Chen Y, Li X, Arlinghaus L, Chakra-
varthy AB, Abramson V, Bhave SR, Levy MA,
Xu H, Yankeelov TE (2013) Machine learning
for predicting the response of breast cancer to
neoadjuvant chemotherapy. J Am Med
Inform Assoc 20(4):688–695.https://doi.
org/10.1136/amiajnl-2012-001332 - De Bari B, Vallati M, Gatta R, Lestrade L,
Manfrida S, Carrie C, Valentini V (2016)
Development and validation of a machine
learning-based predictive model to improve
the prediction of inguinal status of anal cancer
patients: a preliminary report. Oncotarget.
https://doi.org/10.18632/oncotarget.
10749 - Lee BJ, Shin MS, YJ O, Oh HS, Ryu KH
(2009) Identification of protein functions
using a machine-learning approach based on
sequence-derived properties. Proteome Sci
7:27.https://doi.org/10.1186/1477-5956-
7-27 - Mordelet F, Vert JP (2011) ProDiGe: Priori-
tization Of Disease Genes with multitask
machine learning from positive and unlabeled
examples. BMC Bioinformatics 12:389.
https://doi.org/10.1186/1471-2105-12-
389 - Torii M, Wagholikar K, Liu H (2011) Using
machine learning for concept extraction on
clinical documents from multiple data
sources. J Am Med Inform Assoc 18
(5):580–587. https://doi.org/10.1136/
amiajnl-2011-000155 - Jiang M, Chen Y, Liu M, Rosenbloom ST,
Mani S, Denny JC, Xu H (2011) A study of
machine-learning-based approaches to extract
clinical entities and their assertions from dis-
charge summaries. J Am Med Inform Assoc
18(5):601–606. https://doi.org/10.1136/
amiajnl-2011-000163
78. Ware H, Mullett CJ, Jagannathan V, El-Rawas
O (2012) Machine learning-based corefer-
ence resolution of concepts in clinical docu-
ments. J Am Med Inform Assoc 19
(5):883–887. https://doi.org/10.1136/
amiajnl-2011-000774
79. Wu Y, Levy MA, Micheel CM, Yeh P, Tang B,
Cantrell MJ, Cooreman SM, Xu H (2012)
Identifying the status of genetic lesions in
cancer clinical trial documents using machine
learning. BMC Genomics 13(Suppl 8):S21.
https://doi.org/10.1186/1471-2164-13-
S8-S21
80. Bravo A, Li TS, Su AI, Good BM, Furlong LI
(2016) Combining machine learning, crowd-
sourcing and expert knowledge to detect
chemical-induced diseases in text. Database.
https://doi.org/10.1093/database/baw094
81. Yip KY, Cheng C, Gerstein M (2013)
Machine learning and genome annotation: a
match meant to be? Genome Biol 14(5):205.
https://doi.org/10.1186/gb-2013-14-5-
205
82. Xu X, Hoang S, Mayo MW, Bekiranov S
(2010) Application of machine learning
methods to histone methylation ChIP-Seq
data reveals H4R3me2 globally represses
gene expression. BMC Bioinformatics
11:396. https://doi.org/10.1186/1471-
2105-11-396
83. De Santis M, Rinaldi F, Falcone E, Lucidi S,
Piaggio G, Gurtner A, Farina L (2014) Com-
bining optimization and machine learning
techniques for genome-wide prediction of
human cell cycle-regulated genes. Bioinfor-
matics 30(2):228–233.https://doi.org/10.
1093/bioinformatics/btt671
84. Kangas LJ, Metz TO, Isaac G, Schrom BT,
Ginovska-Pangovska B, Wang L, Tan L, Lewis
RR, Miller JH (2012) In silico identification
software (ISIS): a machine learning approach
to tandem mass spectral identification of
lipids. Bioinformatics 28(13):1705–1713.
https://doi.org/10.1093/bioinformatics/
bts194
85. Acharjee A, Ament Z, West JA, Stanley E,
Griffin JL (2016) Integration of metabolo-
mics, lipidomics and clinical data using a
machine learning method. BMC Bioinfor-
matics 17(Suppl 15):440. https://doi.org/
10.1186/s12859-016-1292-2
86. Gorkin DU, Lee D, Reed X, Fletez-Brant C,
Bessling SL, Loftus SK, Beer MA, Pavan WJ,
McCallion AS (2012) Integration of ChIP-
seq and machine learning reveals enhancers
and a predictive regulatory sequence
202 Xiang-tian Yu et al.