The possibility of an event or outcome occurring depending on the occurrence of a previous event or outcome is defined as conditional probability. Conditional probability is calculated by multiplying the preceding event's probability by the updated probability of the following, or conditional, event.

1. CONDITIONAL PROBABILITY
RITHIKA. R. S,
II - M. SC. BIOINFORMATICS,
ALGORITHMS IN BIOINFORMATICS.

2. PROBABILITY
 Probability refers to possibility.
 Probability is a measure of how possible any event is to happen.
 1 is the probability of every event in a sample space.
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3. CONDITIONAL PROBABILITY
 Conditional probability is defined as the
likelihood of an event or outcome occurring,
based on the occurrence of a previous event or
outcome.
 Conditional probability is calculated by
multiplying the probability of the preceding event
by the updated probability of the succeeding, or
conditional, event.
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4. CONDITIONAL PROBABILITY AND BAYES THEOREM
 Bayes Theorem is a formula that describes how to update the probabilities of
hypotheses when given evidence. It follows simply from the axioms of conditional
probability.
 Conditional probability is the probability of one thing given that another thing is true.
Also, Conditional Probability is the base concept in Bayes Theorem.
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5. DIFFERENCE BETWEEN CONDITIONAL PROBABILITY
BAYES THEOREM
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Conditional Probability Bayes Theorem
Conditional Probability is the probability of
occurrence of a certain event, say A, based on
some other event whether B is true or not.
Bayes Theorem includes two conditional
probabilities for the events, say A and B.
It is used to compute the conditional probability
and the events A and B are relatively simple.
It is used in Bayesian inference and in models
where we are interested in the distribution up to
a normalizing factor P(B)
It is used for relatively simple problems. It gives a structured formula for solving more
complex problems.

6. APPLICATIONS OF CONDITIONAL PROBABILITY
 Conditional probability is applied in hidden Markov Models, Bayesian analysis and
Baum-welch algorithm
 Used in identifying the expression level of gene A, given (Parents of gene A) the the
set of genes that have a direct regulatory influence on gene A, along with the help of
Bayesian networks.
 To reconstruct haplotypes efficiently for a large pedigree with a large number of
linked loci, two algorithms are taken for choice one such is conditional probabilities
and other is likelihood computations or the conditional enumeration method.
 The conditional probability method produces a single, approximately optimal
haplotype configuration, with computing time increasing linearly in the number of
linked loci and the pedigree size.
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7. APPLICATIONS…
 With the curated associations between genes, treatments (drugs), and diseases in
pharmGKB, a Bayesian network has been constructed based on conditional
probability tables extracted from biological entities.
 Conditional probability (cp) of a cure given particular treatments and diseases
(p(cure|treatment,disease)).
 Conditional probabilities between treatments (drugs), diseases, and genes: p(t|d,g), by
analyzing their co-occurrence in literature. Eg: p(azidothymidine | HIV, ABCC4) =
0.6.
 The conditional probabilities among these entities, eventually may help with
personalized genetic medicine: p(c|t,d,g).
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8. DEPARTMENT OF BIOINFORMATICS, SKASC 8
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