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We present a probabilistic algorithm for error correction for high throughput DNA sequencing data. Our approach leverages our prior algorithm PREMIER where sequencer outputs are modeled as independent realizations of a Hidden Markov Model (HMM) and the problem of error correction is posed as one of maximum likelihood sequence detection over this HMM. In this work we propose an algorithm called PREMIER...
In this work we present a flexible, probabilistic and reference-free method of error correction for high throughput DNA sequencing data. The key is to exploit the high coverage of sequencing data and model short sequence outputs as independent realizations of a Hidden Markov Model (HMM). We pose the problem of error correction of reads as one of maximum likelihood sequence detection over this HMM...
As reliance on Internet connected systems expands, the threat of damage from malicious actors, especially undetected actors, rises. Masquerade attacks, where one individual or system poses as another, are among the most harmful and difficult to detect types of intrusion. Previous efforts to detect masquerade attacks have focused on host-based approaches, including command line, system call, and GUI...
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