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數據分析
David Shiuan
Department of Life Science
Institute of Biotechnology
Interdisciplinary Program of Bioinformatics
National Dong Hwa University
Microsoft Excel
Plot with Standard Deviation
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Data input
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Select data S/average
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Insert  Function  Statistics  STDEV
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Select average plotting
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Select data/plot /2 knocksY-axis deviation
select Upper/Lower self-determination
choose standard deviation data into + / columns enter
Microsoft Excel
Plot with Regression
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Data input
Plotting
Regression - function
Hidden Markov Model
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A hidden Markov model (HMM) is a statistical model
where the system being modeled is assumed to be a
Markov process with unknown parameters, and the
challenge is to determine the hidden parameters from
the observable parameters.

The extracted model parameters can then be used to
perform further analysis, for example for pattern
recognition applications.
Hidden Markov Model
- Machine Learning Algorithm Method
Hidden Markov Model
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In a regular Markov model, the state is directly visible
to the observer, the state transition probabilities are the
only parameters.

In a hidden Markov model, the state is not directly
visible, but variables influenced by the state are visible.
Each state has a probability distribution over the
possible output tokens. Therefore the sequence of tokens
generated by an HMM gives some information about
the sequence of states.

Hidden Markov models are especially known for their
application in temporal pattern recognition such as
speech, handwriting, gesture recognition and
bioinformatics.
History of Hidden Markov Model

Hidden Markov Models were first described in a
series of statistical papers by Leonard E. Baum and
other authors in the 1960s. One of the first
applications of HMMs was speech recognition.

In the second half of the 1980s, HMMs began to be
applied to the analysis of biological sequences, in
particular DNA. Since then, they have become
ubiquitous in the field of bioinformatics.
Applications of hidden Markov models
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speech recognition , gesture and body motion
recognition , optical character recognition
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machine translation
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bioinformatics and genomics
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prediction of protein-coding regions in genome
sequences
modelling families of related DNA or protein
sequences
prediction of secondary structure elements from
protein primary sequences
Architecture of a Hidden Markov Model
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The random variable x(t) is the value of the hidden
variable at time t. The random variable y(t) is the value
of the observed variable at time t. The arrows in the
diagram denote conditional dependencies.
From the diagram, it is clear that the value of the
hidden variable x(t) (at time t) only depends on the
value of the hidden variable x(t − 1) (at time t − 1).
This is called the Markov property. Similarly, the value
of the observed variable y(t) only depends on the value
of the hidden variable x(t) (both at time t).
Probability of an observed sequence
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The probability of observing a sequence Y = y(0),
y(1),...,y(L − 1) of length L is given by:
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where the sum runs over all possible hidden node
sequences X = x(0), x(1),..., x(L − 1). A brute force
calculation of P(Y) is intractable for realistic
problems, as the number of possible hidden node
sequences typically is extremely high. The
calculation can however be sped up enormously
using an algorithm called the forward-backward
procedure.
Using Hidden Markov Models
Theree canonical正統 problems associated with HMMs:
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Given the parameters of the model, compute the
probability of a particular output sequence. This
problem is solved by the forward-backward procedure.
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Given the parameters of the model, find the most likely
sequence of hidden states that could have generated a
given output sequence. This problem is solved by the
Viterbi algorithm
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Given an output sequence or a set of such sequences,
find the most likely set of state transition and output
probabilities. In other words, train the parameters of
the HMM given a dataset of sequences. This problem is
solved by the Baum-Welch algorithm
A concrete example
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Your friend lives far away and you talk daily over the telephone
about what he did that day. He is only interested in three activities:
walking in the park, shopping, and cleaning his apartment. The
choice of what to do is determined exclusively by the weather on a
given day. You have no definite information about the weather
where your friend lives, but you know general trends. Based on
what he tells you he did each day, you try to guess what the
weather must have been like.
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You believe that the weather operates as a discrete Markov chain.
There are two states, "Rainy" and "Sunny", but you cannot
observe them directly, that is, they are hidden from you. On each
day, there is a certain chance that your friend will perform one of
the following activities, depending on the weather: "walk", "shop",
or "clean". Since your friend tells you about his activities, those
are the observations. The entire system is that of a hidden Markov
model (HMM).
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You know the general weather trends in the area, and what your
friend likes to do on average. In other words, the parameters of the
HMM are known.
You can write them down in the Python
programming language:
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states = ('Rainy', 'Sunny')
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observations = ('walk', 'shop', 'clean')
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start_probability = {'Rainy': 0.6, 'Sunny': 0.4}
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transition_probability = { 'Rainy' : {'Rainy': 0.7,
'Sunny': 0.3}, 'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6}, }
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emission_probability = { 'Rainy' : {'walk': 0.1,
'shop': 0.4, 'clean': 0.5}, 'Sunny' : {'walk': 0.6,
'shop': 0.3, 'clean': 0.1},
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In this piece of code, start_probability represents your
uncertainty about which state the HMM is in when
your friend first calls you (all you know is that it tends
to be rainy on average).
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The particular probability distribution used here is not
the equilibrium one, which is (given the transition
probabilities) actually approximately {'Rainy': 0.571,
'Sunny': 0.429}.
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The transition_probability represents the change of the
weather in the underlying Markov chain. In this
example, there is only a 30% chance that tomorrow will
be sunny if today is rainy.
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The emission_probability represents how likely your
friend is to perform a certain activity on each day. If it
is rainy, there is a 50% chance that he is cleaning his
apartment; if it is sunny, there is a 60% chance that he
is outside for a walk.
Signal P : Hidden Markov Model
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Three Regions :
1. the N-terminal part
2. the hydrophobic region
3. the region around the cleavage site
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For known signal peptides, the model can be used
to assign objective boundaries between these three
regions.
Signal P : Hidden Markov Model
Signal P : Hidden Markov Model