PMC:3475482 / 5920-9410 JSONTXT

{"project":"BLAH6-GNI-CORPUS","denotations":[{"id":"T144","span":{"begin":1190,"end":1193},"obj":"protein"},{"id":"T145","span":{"begin":933,"end":934},"obj":"protein"},{"id":"T146","span":{"begin":915,"end":916},"obj":"protein"},{"id":"T147","span":{"begin":869,"end":871},"obj":"protein"},{"id":"T148","span":{"begin":859,"end":861},"obj":"protein"},{"id":"T149","span":{"begin":779,"end":801},"obj":"protein"},{"id":"T150","span":{"begin":708,"end":709},"obj":"protein"},{"id":"T151","span":{"begin":690,"end":692},"obj":"protein"},{"id":"T152","span":{"begin":612,"end":614},"obj":"protein"},{"id":"T153","span":{"begin":607,"end":610},"obj":"protein"},{"id":"T154","span":{"begin":462,"end":496},"obj":"protein"},{"id":"T155","span":{"begin":312,"end":322},"obj":"DNA"},{"id":"T156","span":{"begin":230,"end":239},"obj":"DNA"},{"id":"T157","span":{"begin":205,"end":207},"obj":"protein"},{"id":"T158","span":{"begin":200,"end":203},"obj":"protein"},{"id":"T159","span":{"begin":3415,"end":3417},"obj":"protein"},{"id":"T160","span":{"begin":3181,"end":3188},"obj":"protein"},{"id":"T161","span":{"begin":3062,"end":3064},"obj":"protein"},{"id":"T162","span":{"begin":2984,"end":2996},"obj":"DNA"},{"id":"T163","span":{"begin":2975,"end":2977},"obj":"DNA"},{"id":"T164","span":{"begin":2966,"end":2968},"obj":"DNA"},{"id":"T165","span":{"begin":2900,"end":2907},"obj":"protein"},{"id":"T166","span":{"begin":2746,"end":2748},"obj":"DNA"},{"id":"T167","span":{"begin":2731,"end":2733},"obj":"DNA"},{"id":"T168","span":{"begin":2723,"end":2725},"obj":"DNA"},{"id":"T169","span":{"begin":2624,"end":2636},"obj":"DNA"},{"id":"T170","span":{"begin":2615,"end":2617},"obj":"DNA"},{"id":"T171","span":{"begin":2606,"end":2608},"obj":"DNA"},{"id":"T172","span":{"begin":2405,"end":2407},"obj":"protein"},{"id":"T173","span":{"begin":2334,"end":2336},"obj":"protein"},{"id":"T193","span":{"begin":1381,"end":1385},"obj":"protein"},{"id":"T194","span":{"begin":1338,"end":1350},"obj":"DNA"},{"id":"T195","span":{"begin":1329,"end":1331},"obj":"DNA"},{"id":"T196","span":{"begin":1320,"end":1322},"obj":"DNA"},{"id":"T174","span":{"begin":2323,"end":2330},"obj":"protein"},{"id":"T175","span":{"begin":2222,"end":2224},"obj":"protein"},{"id":"T176","span":{"begin":2172,"end":2175},"obj":"protein"},{"id":"T177","span":{"begin":1932,"end":1934},"obj":"protein"},{"id":"T178","span":{"begin":1922,"end":1924},"obj":"protein"},{"id":"T179","span":{"begin":1912,"end":1914},"obj":"protein"},{"id":"T180","span":{"begin":1902,"end":1904},"obj":"protein"},{"id":"T181","span":{"begin":1889,"end":1891},"obj":"protein"},{"id":"T182","span":{"begin":1803,"end":1818},"obj":"protein"},{"id":"T183","span":{"begin":1768,"end":1770},"obj":"protein"},{"id":"T184","span":{"begin":1687,"end":1699},"obj":"DNA"},{"id":"T185","span":{"begin":1655,"end":1657},"obj":"DNA"},{"id":"T186","span":{"begin":1646,"end":1648},"obj":"DNA"},{"id":"T187","span":{"begin":1452,"end":1454},"obj":"DNA"},{"id":"T188","span":{"begin":1447,"end":1451},"obj":"protein"},{"id":"T189","span":{"begin":1440,"end":1444},"obj":"protein"},{"id":"T190","span":{"begin":1430,"end":1432},"obj":"DNA"},{"id":"T191","span":{"begin":1424,"end":1428},"obj":"protein"},{"id":"T192","span":{"begin":1402,"end":1404},"obj":"DNA"}],"text":"Concepts and definitions\nLet Σ = {A, C, G, T} be a set of DNA alphabets, where A, C, G, and T are called DNA characters or bases. A DNA sequence S is an ordered list of DNA alphabets. S is denoted by 〈c1, c2, ..., cl〉, where ci ∈ Σ and |S| denotes the length of sequence S. A sequence with length n is called an n-sequence. A sequence database D is a set of tuples 〈sid, S〉, where sid is a sequence ID. The sum of the lengths of all sequences in D is denoted as |D|=|S1|+|S2|...|Sn|\n\nDefinition 1 (Patter)\nA pattern is a contiguous sub-sequence of DNA sequence S drawn from Σ = {A, C, G, T}. A sequence α = 〈a1, a2, ..., an〉 is called a contiguous sub-sequence of another sequence β = 〈b1, b2, ..., bm〉, and β is a contiguous super-sequence of α, denoted as α⊆β, if there exists integers 1 ≤ j1 ≤ j2 ≤ ... ≤ jn ≤ m and ji+1 = ji + 1 for 1 ≤ i ≤ n-1 such that a1 = bj1, a2 = bj2, ..., an = bjn. We can also say that α is contained by β. In our paper, we use the term \"(sub)-sequence\" to describe \"contiguous (sub)-sequence\" in brief.\n\nDefinition 2 (Support)\nGiven a pattern P and a sequence S, the number of occurrences of P in S is called the support of pattern P in sequence S, denoted as Sup(P, Si). For DNA sequence database D, the support of P in D is defined as .\n\nDefinition 3 (Confidence)\nGiven a pattern P = P1, P2, ..., Pq and a DNA sequence database D, the confidence of P1P2 with respect to P1 is defined as Conf(P1P2, P1) = Sup(P1P2,D)/Sup(P1,D).\nFor example, the character \"A\" occurs 10 times and \"AT\" occurs 7 times, and in database D in Table 1, Conf(AT,A) = 7/10 = 0.7.\n\nDefinition 4 (Pattern probability)\nGiven a pattern P = P1, P2, ..., Pq (Pi is a DNA alphabet) and a DNA sequence database D, the pattern probability of P in D is defined as , where Pr(Pi, D) = # of occurrences of an alphabet Pi/|D|.\nFor example, the pattern probability of pattern \"ATCG\" in Table 1 is Pr(ATCG,D) = Pr(A,D) × Pr(T,D) × Pr(C,D) × Pr(G,D) = (10/55) × (18/55) × (12/55) × (15/55) = 0.182 × 0.372 × 0.218 × 0.273 = 0.00403.\n\nDefinition 5 (Information)\nThe information carried by a DNA character or base in DNA sequence database D is defined as I(c) = -log|C|Pr(c,D), where |C| is the number of distinct characters in D and Pr(c) is the probability of c occurs in D.\nFor example, the occurrence probability of character A in Table 1 is Pr(A,D) = # of occurence(A)/|D|. So, the probability of character A is Pr(A,D) = 10/55 = 0.182 in our example database. Then, the information of character A in D is, I(A) = -log|C|Pr(A,D) = -log4(0.182) = 1.228.\n\nDefinition 6 (Pattern information)\nGiven a pattern P = P1, P2, ..., Pq and a DNA sequence database D, the pattern information of P in D is defined as I(P) = -log|C|Pr(P,D) = I(P1) + I(P2) +......+ I(Pq).\nFor example, the pattern information of pattern \"ATCG\" is I(ATCG,D) = I(A,D) + I(T,D) + I(C,D) + I(G,D) = 1.228 + 0.713 + 1.098 + 0.9365 = 3.9755 in Table 1.\n\nDefinition 7 (Information gain)\nGiven a pattern P = P1, P2, ..., Pq and a DNA sequence database D, the pattern information gain of P in D is defined as IG(P) = I(P) × Support(P).\nFor example, the information gain of pattern \"ATCG\" is IG(ATCG,D) = 3.9755 * 5 = 19.8775 in Table 1.\n\nDefinition 8 (Finding interesting patterns)\nGiven a sequence database D and user-specified min_conf and min_in_gain, the problem of finding interesting patterns is to find the complete set of interesting patterns, such that IG(P) and Conf(P) are greater than min_in_gain and min_conf , respectively."}

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and definitions\nLet Σ = {A, C, G, T} be a set of DNA alphabets, where A, C, G, and T are called DNA characters or bases. A DNA sequence S is an ordered list of DNA alphabets. S is denoted by 〈c1, c2, ..., cl〉, where ci ∈ Σ and |S| denotes the length of sequence S. A sequence with length n is called an n-sequence. A sequence database D is a set of tuples 〈sid, S〉, where sid is a sequence ID. The sum of the lengths of all sequences in D is denoted as |D|=|S1|+|S2|...|Sn|\n\nDefinition 1 (Patter)\nA pattern is a contiguous sub-sequence of DNA sequence S drawn from Σ = {A, C, G, T}. A sequence α = 〈a1, a2, ..., an〉 is called a contiguous sub-sequence of another sequence β = 〈b1, b2, ..., bm〉, and β is a contiguous super-sequence of α, denoted as α⊆β, if there exists integers 1 ≤ j1 ≤ j2 ≤ ... ≤ jn ≤ m and ji+1 = ji + 1 for 1 ≤ i ≤ n-1 such that a1 = bj1, a2 = bj2, ..., an = bjn. We can also say that α is contained by β. In our paper, we use the term \"(sub)-sequence\" to describe \"contiguous (sub)-sequence\" in brief.\n\nDefinition 2 (Support)\nGiven a pattern P and a sequence S, the number of occurrences of P in S is called the support of pattern P in sequence S, denoted as Sup(P, Si). For DNA sequence database D, the support of P in D is defined as .\n\nDefinition 3 (Confidence)\nGiven a pattern P = P1, P2, ..., Pq and a DNA sequence database D, the confidence of P1P2 with respect to P1 is defined as Conf(P1P2, P1) = Sup(P1P2,D)/Sup(P1,D).\nFor example, the character \"A\" occurs 10 times and \"AT\" occurs 7 times, and in database D in Table 1, Conf(AT,A) = 7/10 = 0.7.\n\nDefinition 4 (Pattern probability)\nGiven a pattern P = P1, P2, ..., Pq (Pi is a DNA alphabet) and a DNA sequence database D, the pattern probability of P in D is defined as , where Pr(Pi, D) = # of occurrences of an alphabet Pi/|D|.\nFor example, the pattern probability of pattern \"ATCG\" in Table 1 is Pr(ATCG,D) = Pr(A,D) × Pr(T,D) × Pr(C,D) × Pr(G,D) = (10/55) × (18/55) × (12/55) × (15/55) = 0.182 × 0.372 × 0.218 × 0.273 = 0.00403.\n\nDefinition 5 (Information)\nThe information carried by a DNA character or base in DNA sequence database D is defined as I(c) = -log|C|Pr(c,D), where |C| is the number of distinct characters in D and Pr(c) is the probability of c occurs in D.\nFor example, the occurrence probability of character A in Table 1 is Pr(A,D) = # of occurence(A)/|D|. So, the probability of character A is Pr(A,D) = 10/55 = 0.182 in our example database. Then, the information of character A in D is, I(A) = -log|C|Pr(A,D) = -log4(0.182) = 1.228.\n\nDefinition 6 (Pattern information)\nGiven a pattern P = P1, P2, ..., Pq and a DNA sequence database D, the pattern information of P in D is defined as I(P) = -log|C|Pr(P,D) = I(P1) + I(P2) +......+ I(Pq).\nFor example, the pattern information of pattern \"ATCG\" is I(ATCG,D) = I(A,D) + I(T,D) + I(C,D) + I(G,D) = 1.228 + 0.713 + 1.098 + 0.9365 = 3.9755 in Table 1.\n\nDefinition 7 (Information gain)\nGiven a pattern P = P1, P2, ..., Pq and a DNA sequence database D, the pattern information gain of P in D is defined as IG(P) = I(P) × Support(P).\nFor example, the information gain of pattern \"ATCG\" is IG(ATCG,D) = 3.9755 * 5 = 19.8775 in Table 1.\n\nDefinition 8 (Finding interesting patterns)\nGiven a sequence database D and user-specified min_conf and min_in_gain, the problem of finding interesting patterns is to find the complete set of interesting patterns, such that IG(P) and Conf(P) are greater than min_in_gain and min_conf , respectively."}