rchers who enter this area. The book was especially written with

o give readers the opportunity to transfer their machine learning

o practical problems. From this, they can be motivated to develop

hodologies, new ideas and new algorithms for biological pattern

y. Therefore, the R programming environment was used

ut this book. Moreover, this book was written based on individual

of biological pattern discovery. The main subjects include gene

n data analysis for both microarray and sequencing count data,

sequencing comparison, peptide data analysis, spectra data

for molecule discovery, as well as pattern optimisation involving

tionary computation approaches. The selection of the subjects in

was based on individual machine learning approaches. Therefore,

pter of this book can be independently read and referred to. The

n start via any chapter according to his/her requirements.

uthor feels very lucky to have been a part of the current era in

ological pattern discovery has been revolutionised. The author

ke to make an acknowledgement to his research collaborators in

ogy department of the University of Exeter. Without these

t and fruitful collaborations, it is almost impossible for the author

op the ideas, algorithms and models for biological pattern

y, hence resulting in this book. The author would also like to thank

nts, especially some excellent students. Their desire to learn new

greatly motivated the author’s research.

importantly, the author would like to make a sincere

edgement to the huge support from his family, especially the

rom his wife and his daughter. The author makes his best wishes

ew-born grand-daughter Poppy: may her become a wise and

scholar in the future.

31 March 2021

Exeter

ron.zheng.rong.yang@gmail.com