Sensitive and specific peak detection for SELDI-TOF mass spectrometry using a wavelet/neural-network based approach

Proteins are potent biomarkers to detect colon cancer progression

Colon cancer is the most common type of cancer and major cause of death worldwide. The detection of colon cancer is difficult in early stages. However, the secretory proteins have been used as ideal biomarker for the detection of colon cancer progress in cancer patients. Serum/tissue protein expression could help general practitioners to identify colon cancer at earlier stages. By this way, we use the biomarkers to evaluate the anticancer drugs and their response to therapy in cancer models. Recently, the biomarker discovery is important in cancer biology and disease management. Also, many measurable specific molecular components have been studied in colon cancer therapeutics. The biomolecules are mainly DNA, RNA, metabolites, enzymes, mRNA, aptamers and proteins. Thus, in this review we demonstrate the important protein biomarker in colon cancer development and molecular identification of protein biomarker discovery.

Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection

Magnetic anomaly detection (MAD) is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise) with a power spectral density of 1/fa (0<a<2), which is non-stationary, self-similarity and long-range correlation. Meanwhile the orthonormal wavelet decomposition can play the role of a Karhunen-Loève-type expansion to the 1/f-type signal by its decorrelation abilities, an effective energy detection method based on undecimated discrete wavelet transform (UDWT) is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI) magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT), the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method.