The automated annotation pipeline at the J. Craig Venter Institute (JCVI) was employed to identify genome features in the raw DNA sequence, gather evidence for function of the features, and assign functional annotation.
JCVI employs an automated annotation pipeline that identifies genome features in the raw DNA sequence, gathers evidence for function of the features, and assigns functional annotation based on the weight of the evidence.
To illustrate Richardson's approach, consider his account in Chapter 2 of reverse engineering, which uses the design features of an organism to extrapolate backwards to the presumed function of the features and to provide evidence for their origin in some functional achievement.
Here, we examine soil-borne microbial biogeography as a function of the features that define an American Viticultural Area (AVA), a geographically delimited American wine grape-growing region, defined for its distinguishing features of climate, geology, soils, physical features (topography and water), and elevation.
For each model, we have performed three analysis in function of the features used: HOS in the time domain (H O S t ), the SK, and the mixed (time and frequency domains) analysis (H O S t +S K).
In this respect, they are intuitively similar to the agent-relative/agent-neutral distinction, in that there is a recognizable sense in which agent-relative reasons are private their status as reasons for an agent is irreducibly a function of the features of that agent as such.
Transmission loss and polarization effects in small contact holes are characterized as a function of the feature size.
Figure 12 Probability of correct classification as a function of the feature vector dimensionality.
The function of the feature extractor is to extract the desired features or data from video bistreams encapsulated in each network packet.
If we suppose that GFS is a function of the feature vector X i, the Naïve Bayes classifier comes into existence based on the fundamental conditions that the features must be mutually independent and that the training and prediction sets must have the same GFS function.
In the learning phase, predefined training data is used for computing various time-domain and frequency-domain features, for speech and music signals separately, and estimating the optimal speech/music thresholds, based on the probability density functions of the features.
