y

y p

g

possible to extrapolate beyond the interval of the available data

a model has been constructed. The reason that a linear model has

bility is that a linear model can discover the trend relationship

variables.

ver, linear models have a common limitation that they may fail

a complicated data set. This therefore may result in some

ng interpretations if the nonlinearity in data has been missed

modelling process. The nonlinear modelling algorithms such as

etwork learning and then later deep learning algorithms have

eat attention for modelling biological/medical data for knowledge

y and pattern analysis since 1980s. The greatest contribution is the

nt improvement of the accuracy of such a model. Deep learning

promising machine learning strategy has drawn even greater

for biological/medical data analysis and pattern discovery. The

hat deep learning has become popular in biological/medical

is its unique property. When a machine learning model is

ed based on some non-numerical variables, such as the variables

age data or spectra data, a conversion process from non-numerical

to numerical variables has to be followed, which is called the

xtraction. A feature extraction process can generate hundreds or

s of feature variables based on an image data. Some of them may

gh discrimination power, but some may not. Some of them may

y correlated to each other, making some feature variables

t, but some may not. Therefore, after feature variables have been

, a process has to be followed to filter out non-contributing

ariables. This is called a feature selection process. Although there

n many machine learning algorithms developed for both feature

n and feature selection, these two processes are still non-trivial.

the most representative feature variables is also another tedious

n addition to feature extraction and is prone to error. Importantly,

no unique method for this process. When a research objective

or even when there are some changes in the data, a new process

rt again. A deep learning model can incorporate a feature