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4.
Figure 3

Figure 3. State transition diagram.. From: Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks.

Each rectangle represents a state, which is characterized by an emission distribution and a duration distribution. The state transition topology of Philius exactly mimics that of Phobius.

Sheila M. Reynolds, et al. PLoS Comput Biol. 2008 November;4(11):e1000213.
5.
Figure 6

Figure 6. Full-topology prediction precision vs score for the TM proteins.. From: Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks.

The black line is the average score within the sliding window used to estimate the precision, and the gray lines indicate the average plus and minus one standard deviation.

Sheila M. Reynolds, et al. PLoS Comput Biol. 2008 November;4(11):e1000213.
6.
Figure 1

Figure 1. Hidden Markov model.. From: Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks.

(a) BN with two variables which constitutes the basic (single frame) template for an HMM, and (b) A DBN representation of an HMM obtained by concatenating a variable number of the BN frames and connecting successive state variables.

Sheila M. Reynolds, et al. PLoS Comput Biol. 2008 November;4(11):e1000213.
7.
Figure 4

Figure 4. Protein-type classification precision vs confidence score computed by sorting the proteins by score and computing the average score and precision within a sliding window.. From: Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks.

Left: precision vs average score for each of the three main protein types. Right: average (black) and average ±one standard deviation (gray) across all proteins.

Sheila M. Reynolds, et al. PLoS Comput Biol. 2008 November;4(11):e1000213.
8.
Figure 2

Figure 2. Philius training and decoding graphical models.. From: Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks.

(a) Training DBN: only the amino acid and the topoLabel are observed in each frame. The topoLabel is used to constrain the hidden state using an observed child node. The color of the edge between two nodes indicates the type of relationship: black is deterministic, and red is random. (b) First stage decoding DBN: the topoState is hidden and dependent on the state and the previous topoState, and specifies the behavior of pType, an additional hidden variable. (c) Second stage decoding DBN: the observed amino acid node and the duration modeling nodes have been removed, and Pr[topoStatei] is defined by the posterior probabilities computed in the first stage using the virtual evidence node topoVE.

Sheila M. Reynolds, et al. PLoS Comput Biol. 2008 November;4(11):e1000213.
9.
Figure 9

Figure 9. Philius topology prediction for the human presenilin protein as shown on the YRC web-page.. From: Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks.

The diagram shows the nine membrane-spanning regions as vertical cylinders, and the cytoplasmic and non-cytoplasmic segments as horizontal bars. Each segment is colored according to type and shaded according to the confidence score. The seventh membrane-helix is missed by many topology predictors and is assigned a relatively low confidence score by Philius and as such is shaded gray. Because of this one low-confidence membrane segment, the location of the C-terminus is less confidently assigned than the location of the N-terminus. On the YRC web page, this diagram is accompanied by the type confidence and topology confidence, as well as a copy of the protein sequence, color coded by segment type. Placing the mouse over any part of the topology diagram or the color-coded sequence will produce a pop-up showing the segment type, confidence, and boundary locations.

Sheila M. Reynolds, et al. PLoS Comput Biol. 2008 November;4(11):e1000213.

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