A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-3

Number of features being reduced and the Y-axis represents the average training error in percentage over 100 training times counted in percentage. The training error increases significantly when 23 less relevant features are removed, as indicated by the red arrow. It is then suggested that at most 22 features could be eliminated.<p><b>Copyright information:</b></p><p>Taken from "A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/325</p><p>BMC Bioinformatics 2008;9():325-325.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529326.</p><p></p

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-4

Y losing HO, NH, etc. Figure 5-B: The comparison of the experimental spectrum (red) versus the spectrum predicted by the network model (blue). The experimental spectrum is the y-ions extracted from the raw data (Figure 5-A) with intensities log-transformed. Figure 5-C: The effect of using probability theory. Blue dots indicate the interval [mean intensity - SD, mean intensity + SD] within which intensities of the ions are supposed to lie.<p><b>Copyright information:</b></p><p>Taken from "A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/325</p><p>BMC Bioinformatics 2008;9():325-325.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529326.</p><p></p

Asymmetry in the frequency domain interactions.

<p>A. Mean and maximum ratio using all the three sheep before and after learning. B. Upper panel: Mean and maximum ratio of sheep B (see Experiment subsection in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000570#s2" target="_blank">Methods</a> section) before learning, after learning and one month after learning (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000570#pcbi-1000570-g004" target="_blank">Fig. 4</a>). Bottom panel: Mean and maximum ratio of sheep C before learning (the first bar), immediately after learning (the second bar), one week after learning and one month after learning (the third and the fourth bar). C. Summaries of results in B.</p

Results on Toy Model 1.

<p>A. Traces of the time series. B. The causal relationships considered in Toy Model 1 between the three state variables. C. The estimated parameters , , and for the simulated data in Toy Model 1. The initial values of the three parameters are all set to 0. The covariance matrix is first set to decay slowly to achieve faster convergence and then set to decay faster after two hundred time points to ensure a better accuracy. D. Frequency decomposition of all kinds of relationships between the state variables. Significant causal influences are marked by red.</p

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-1

N: Red circles: normalized irrelevance scores of the features under non-mobile status. Blue squares: normalized irrelevance scores of the features under partial-mobile status. Green triangles: normalized irrelevance scores of the features under mobile status. The higher an irrelevance score is, the less important the corresponding feature is. The threshold of each mobility status is shown in dashed line and the features proven to be influential on peptides' fragmentation (below threshold) are highlighted with filled circles/squares/triangles.<p><b>Copyright information:</b></p><p>Taken from "A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/325</p><p>BMC Bioinformatics 2008;9():325-325.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529326.</p><p></p

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-0

E input layer representing 35 features. 40 nodes in binary are used to represent the presence of 20 different residues at N and C terminus to the target peptide bond. Every node in the input layer has an independent coefficient to reveal its "relevance" to the network output. The hidden layer has 40 nodes and the activation function of the hidden layer is sigmoidal.<p><b>Copyright information:</b></p><p>Taken from "A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/325</p><p>BMC Bioinformatics 2008;9():325-325.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529326.</p><p></p

Asymmetry between left and right hemisphere in the time domain.

<p>A. A summary of the results in B, but locations in inferotemporal cortex are not precise, only for illustrative purposes. B. The mean connections from left hemisphere to right hemisphere, right hemisphere to left hemisphere and within both regions with the three bars corresponding to the results before learning (blue bar), after learning (green bar), and one month after learning (purple bar) in Sheep B. Significant changes after t-test are marked by arrows (right to left, all pairs are not significant, as indicated by “none”; within the right hemisphere, all pairs are significant, marked by “all”) . For Sheep C, an additional bar (one week after learning) is added (the third bar). Only significant changes from left to right and within the right hemisphere are indicated by arrows. C. Statistic summaries of results in B.</p

An example of the application of EGCM.

<p>The network detected by EGCM (top-panel) and the corresponding frequency decomposition (bottom-panel) for six randomly selected electrodes. In the frequency decomposition, significant causal influences are marked by red.</p

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-5

by losing HO, NH, etc. Figure 6-B: The comparison of the experimental spectrum (red) versus the spectrum predicted by the network model (blue). The experimental spectrum is the y-ions extracted from the raw data (Figure 6-A) with intensities log-transformed. Figure 6-C: The effect of using probability theory. Blue dots indicate the interval [mean intensity - SD, mean intensity + SD] within which intensities of the ions are supposed to lie.<p><b>Copyright information:</b></p><p>Taken from "A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/325</p><p>BMC Bioinformatics 2008;9():325-325.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529326.</p><p></p

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-2

Luence on cleavage at its C-terminus is illustrated in the right panel (red dots). The most influential residues are marked with arrows. Down arrows indicate inhibition whereas up arrows indicate enhancement. Figure 3-A: Mobile status. Figure 3-B: Partial-mobile status. Figure 3-C: Non-mobile status.<p><b>Copyright information:</b></p><p>Taken from "A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/325</p><p>BMC Bioinformatics 2008;9():325-325.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529326.</p><p></p