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It has been long thought that (As in Mendelian genetics) parental origin of gene does not affect its expression. With respect to autosomal genes, male and female contribute equal number of genes and produce similar effects. But expression of some genes is significantly affected by the parental type, i.e. whether it came from male or female. This phenomenon is called Genomic Imprinting. One of the example of genomic imprinting is Igf2 (Insulin like growth factor II). It is present in both mice and humans and in both cases genomic imprinting takes place. A child inherit one allele from male parent and other allele from female parent. The paternal copy of Igf2 is actively expressed in the fetus and placenta but maternal copy remains completely silent. So, when the paternal copy is deleted in the mice, the mice produces small placenta and low weight baby. Another example would be Prader-Willi and Angelman syndromes. A child with Prader-Willi syndrome

Hidden Markov Models Hidden Markov Models is one of the popular machines learning approach that can be used to decipher highly complex biological data. One example would be CG-Island. CG is the least frequent nucleotide in many genomes Reason: C within CG is easily methylated Resulting methylation has tendency to mutate into T However methylation is suppressed around genes in areas called CG-Island. Here CG appears relatively frequently. Fair Bet Casino: Through it CG-Islands can be modeled. Dealer flips coin and player bets on outcome: heads or tails Dealer uses either a biased coin (head with probability ¾) or fair coin head or tail equally likely Given a sequence of coin tosses, problem to find out when dealer used a biased coin or a fair coin Obviously, if you observe long line of heads it’s likely that dealer used biased coin and if you observe even distribution of heads and tails he likely used fair one.