Above are papers related to the following paper: [Paper_ID] Paper #1104191. 	[Title] Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations [Abstract] We present a new method for determining the consensus sequence in DNA fragment assemblies. The new method, Trace-Evidence, directly incorporates aligned ABI trace information into consensus calculations via our previously described representation, TraceData Classifications. The new method extracts and sums evidence indicated by the representation to determine consensus calls. Using the Trace-Evidence method results in automatically produced consensus sequences that are more accurate and less ambiguous than those produced with standard majority- voting methods. Additionally, these improvements are achieved with less coverage than required by the standard methods using Trace-Evidence and a coverage of only three, error rates are as low as those with a coverage of over ten sequences. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38771. 	[Title] Improving the Quality of Automatic DNA Sequence Assembly using Fluorescent Trace-Data Classifications [Abstract] Virtually all large-scale sequencing projects use automatic sequence-assembly programs to aid in the determination of DNA sequences. The computer-generated assemblies require substantial handediting to transform them into submissions for GenBank. As the size of sequencing projects increases, it becomes essential to improve the quality of the automated assemblies so that this time-consuming handediting may be reduced. Current ABI sequencing technology uses base calls made from fluorescently-labeled DNA fragments run on gels. We present a new representation for the fluorescent trace data associated with individual base calls. This representation can be used before, during, and after fragment assembly to improve the quality of assemblies. We demonstrate one such use end-trimming of suboptimal data that results in a significant improvement in the quality of subsequent assemblies. Please summarise all the given information.

Paper #1104191 has the title `Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations  
` and the abstract `We present a new method for determining the consensus sequence in DNA fragment assemblies. The new method, Trace-Evidence, directly incorporates aligned ABI trace information into consensus calculations via our previously described representation, TraceData Classifications. The new method extracts and sums evidence indicated by the representation to determine consensus calls. Using the Trace-Evidence method results in automatically produced consensus sequences that are more accurate and less ambiguous than those produced with standard majority- voting methods. Additionally, these improvements are achieved with less coverage than required by the standard methods using Trace-Evidence and a coverage of only three, error rates are as low as those with a coverage of over ten sequences. 
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Question: Which category should Paper #1104191 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1104191 has the title `Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations  
` and the abstract `We present a new method for determining the consensus sequence in DNA fragment assemblies. The new method, Trace-Evidence, directly incorporates aligned ABI trace information into consensus calculations via our previously described representation, TraceData Classifications. The new method extracts and sums evidence indicated by the representation to determine consensus calls. Using the Trace-Evidence method results in automatically produced consensus sequences that are more accurate and less ambiguous than those produced with standard majority- voting methods. Additionally, these improvements are achieved with less coverage than required by the standard methods using Trace-Evidence and a coverage of only three, error rates are as low as those with a coverage of over ten sequences. 
`.
Question: Which category should Paper #1104191 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks