A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-10

Umber 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-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

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data-6

Erimental counterpart. Figure 7-A: The red line represents the sorted scores calculated with the predicted intensity information. The blue line represents the corresponding scores calculated without intensity information. The two score use the same variances predicted by the Bayesian neural network model. Figure 7-B: The red line represents the sorted scores calculated with the predicted intensity information. The blue line represents the corresponding scores calculated without intensity information. Variances for intensity-free scores are set to 1.<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-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-9

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

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

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

DataSheet1_Velocity changes after the 2021 MS 6.4 Yangbi earthquake based on passive image interferometry.docx

An MS 6.4 earthquake occurred in Yangbi, Yunan Province, China, on 21 May 2021. The epicenter was on the blind branch fault in the west of the Weixi–Qiaohou–Weishan fault, but no surface rupture was obvious. In the present study, the continuous vertical component of waveforms that were recorded in six nearby permanent stations was collected and the noise cross-correlation and autocorrelation techniques were utilized to investigate velocity changes that were induced by the Yangbi Earthquake. Velocity changes based on the single-station autocorrelation method reveal mainly coseismic declines, and a maximum of .09% was recorded in the EYA station. Results from the cross-correlation technique show both positive and negative velocity changes, and these lasted for approximately 3 months. The volumetric strain that was generated by the Yangbi Earthquake at a depth of 5 km exhibits an obvious four-quadrant distribution. Station pairs in the dilatation region (e.g., EYA–HEQ) mainly display a decrease in velocity, whereas those in the contraction region (e.g., BAS–TUS, TUS–YUL, and LUS–TUS) show an increase in velocity. Based on the depth sensitivity of scattered waves, velocity changes that were obtained using the noise cross-correlation involve the highest weight coefficients near the related two stations. Regarding stations of one station pair in different stress loading regions, the static stress of the station that is nearest to the epicenter exerted a greater impact on the velocity change. The observed velocity changes are likely attributed to a combination of near-surface physical damage and static stress changes. The validation of clock errors with magnitudes of seconds that were obtained using the noise cross-correlation and effects of these errors on measured velocity changes are also discussed.</p