Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many sequence data mining applications, the goal is to find frequent substrings. Some of these applications like extracting motifs in protein and DNA sequences are looking for frequently occurring approximate contiguous substrings called simple motifs. By approximate  mean that some mismatches are allod during similarity test beten substrings, and it helps to discover unknown patterns. Structured motifs in DNA sequences are frequent structured contiguous substrings which contains two or more simple motifs. There are some works that have been done to find simple motifs but these works have problems such as low scalability, high execution time, no guarantee to find all patterns, and low flexibility in adaptation to other application. The Flame is the only algorithm that can find all unknown structured patterns in a dataset and has solved most of these problems but its scalability for very large sequences is still ak. In this research a new approach named Next-Symbol-Array based Motif Discovery (NSAMD) is represented to improve scalability in extracting all unknown simple and structured patterns. To reach this goal a new data structure has been presented called Next-Symbol-Array. This data structure makes change in how to find patterns by NSAMD in comparison with Flame and helps to find structured motif faster. Proposed algorithm is as accurate as Flame and extracts all existing patterns in dataset. Performance comparisons show that NSAMD outperforms Flame in extracting structured motifs in both execution time (51% faster) and memory usage (more than 99%). Proposed algorithm is slor in extracting simple motifs but considerable improvement in memory usage (more than 99%) makes NSAMD more scalable than Flame. This advantage of NSAMD is very important in biological applications in which very large sequences are applied.
['Abdolvahed Pari', 'Ahmad Baraani', 'Saeed Parseh']
I am writing a research paper on a idea NSAMD: A new approach to discover structured contiguous substrings in sequence datasets using Next-Symbol-Array.
[{"from": "human", "value": "I am writing a research paper on a idea NSAMD: A new approach to discover structured contiguous substrings in sequence datasets using Next-Symbol-Array."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many sequence data mining applications, the goal is to find frequent substrings. Some of these applications like extracting motifs in protein and DNA sequences are looking for frequently occurring approximate contiguous substrings called simple motifs. By approximate  mean that some mismatches are allod during similarity test beten substrings, and it helps to discover unknown patterns. Structured motifs in DNA sequences are frequent structured contiguous substrings which contains two or more simple motifs. There are some works that have been done to find simple motifs but these works have problems such as low scalability, high execution time, no guarantee to find all patterns, and low flexibility in adaptation to other application. The Flame is the only algorithm that can find all unknown structured patterns in a dataset and has solved most of these problems but its scalability for very large sequences is still ak. In this research a new approach named Next-Symbol-Array based Motif Discovery (NSAMD) is represented to improve scalability in extracting all unknown simple and structured patterns. To reach this goal a new data structure has been presented called Next-Symbol-Array. This data structure makes change in how to find patterns by NSAMD in comparison with Flame and helps to find structured motif faster. Proposed algorithm is as accurate as Flame and extracts all existing patterns in dataset. Performance comparisons show that NSAMD outperforms Flame in extracting structured motifs in both execution time (51% faster) and memory usage (more than 99%). Proposed algorithm is slor in extracting simple motifs but considerable improvement in memory usage (more than 99%) makes NSAMD more scalable than Flame. This advantage of NSAMD is very important in biological applications in which very large sequences are applied."}]