Yihui Liu

When eigenfaces are combined with wavelets

This paper presents a novel and interesting combination of wavelet techniques and eigenfaces to extract features for face recognition. Eigenfaces reduce the dimensions of face vectors while wavelets reveal information that is unavailable in the original image. Extensive experiments have been conducted to test the new approach on the ORL face database, using a radial basis function neural network classifier. The results of the experiments are encouraging and the new approach is a step forward in face recognition.

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patients survival play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers of survival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to be significantly associated with survival time.

Dimensionality reduction and main component extraction of mass spectrometry cancer data

Mass spectrometry data have high dimensionality. Dimensionality reduction is a very important step to greatly improve the performance of distinguishing cancer tissue from normal tissue. In this study multilevel wavelet analysis is performed on high dimensional mass spectrometry data. A set of orthogonal wavelet basis of approximation coefficients is extracted to reduce dimensionality of mass spectra and represent main components of mass spectrometry data. The best level of wavelet decomposition of mass spectrometry data is selected based on energy distribution of approximation coefficients. Compared to traditional principal component analysis (PCA) method, which dependents on training samples to build feature space, our proposed method is using wavelet basis to extract main components of mass spectrometry, keeping local properties of data, and computing efficiently. Experiments are conducted on three datasets. The competitive performance is achieved compared to other methods of feature extraction and feature selection.

Detect adverse drug reactions for the drug Pravastatin

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical data, which are collected from day to day clinical practice. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Pravastatin. Major side effects for the drug are detected. The detected ADRs are based on computerized method, further investigation is needed.

Detect Adverse drug reactions for drug Aspirin

Adverse drug reaction (ADR) is widely concerned for public health issue. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Aspirin. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on the computerized method, further investigation is needed.

Detect Adverse Drug Reactions for Drug Atorvastatin

Adverse drug reaction (ADR) is widely concerned for public health issue. in this study we propose an original approach to detect the ADRs using feature matrix and feature selection. the experiments are carried out on the drug Atorvastatin. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. the detected ADRs are based on the computerized method, further investigation is needed.

Detect adverse drug reactions for drug Pioglitazone

Adverse drug reaction (ADR) is widely concerned for public health issue. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Pioglitazone. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on the computerized method, further investigation is needed.

Detect Adverse Drug Reactions for Drug Simvastatin

Adverse drug reaction (ADR) is widely concerned for public health issue. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Simvastatin. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on the computerized method, further investigation is needed.

Detect Adverse Drug Reactions for Drug Alendronate

Adverse drug reaction (ADR) is widely concerned for public health issue. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Alendronate. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on the computerized method, further investigation is needed.