Biomedical subjects

Jin-Yuan Mo

Publications and source records attributed to Jin-Yuan Mo.

3 recordsLinked to original sources

PubMed2006

Using pseudo-amino acid composition and support vector machine to predict protein structural class.

As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.

Amino Acid Sequence↗

PubMed2005

[A new de-noising technique for spectra based on Mexican hat wavelet].

Signals in spectral analysis often have random noise, which has negative influence on the accuracy and detection limit of analysis. A new chemometrics method named Mexican Hat Wavelet De-noising Arithmetic (MWDA) is presented, which can be used to remove noise in analytical chemical signals. In this method, Mexican Hat wavelet is chosen to construct de-noising function because of its excellent properties, then the de-noising function is used to extract useful information from noisy signals. MWDA is effective for signals with either wide peaks or very sharp peaks. Many processing results of simulated and experimental signals indicate that MWDA is a simple and powerful de-noising method, even when the signal has very high noise (whose signal to noise ratio is 1). After processed, the relative errors of peak position, peak height and peak area are less than 0.2%, 3.2% and 1.1% respectively. When it is applied to experimental spectra, the results are also satisfactory. This new method can increase the accuracy of spectral analysis, and the result is credible and satisfying.

Algorithms↗

PubMed2002

Use of capillary electrophoresis with conductivity detection to anions.

Capillary electrophoresis with conductivity detection was used to separate and detect chloride (Cl-), nitrate (NO3-), bicarbonate (HCO3-) and dihydric phosphate (H2PO4-) ions. The proposed method was carried out by using citric acid and sodium citrate as buffer solution, under the condition of a-10 kV high voltage power supply. The effects of various buffers, concentrations, pH values and running voltages on separation were investigated. Under the chosen conditions the linear ranges of Cl-, NO3-, HCO3-, and H2PO4- were 5.0 x 10(-5) mol/L -2.5 x 10(-3) mol/L, 6.0 x 10(-5) mol/L-2.0 x 10(-3) mol/L, 5.0 x 10(-6) mol/L-2.0 x 10(-4) mol/L and 6.0 x 10(-5) mol/L-1.0 x 10(-3) mol/L respectively. The detection limits were 1.5 x 10(-5) mol/L, 3.0 x 10(-5) mol/L, 1.0 x 10(-6) mol/L, 2.0 x 10(-5) mol/L and the relative standard deviations (RSD) of migration time were 3.1%, 3.3%, 2.6% and 2.9% respectively. Tap water was analyzed under the same conditions.

Anions↗