THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

A DNA test to sex most birds.

生活習慣病とgenome-wide association study

Improved binary PSO for feature selection using gene expression data

The drug discovery for chemoprevention and chemotherapy remains a challenge. Natural products-derived extracts and compounds are frequently reported to discover therapeutic agents for disease and cancer.

The overall scenario of this special issue of The Scientific World Journal presents the recent advances in biological function of selected natural products for cancer and disease therapy in terms of crude extracts and components. Some studies describe the bioinformatics tool to help to investigate the field of natural products.

The papers by S. Guleria et al. and C.-C. Lee et al. provide the essential oil and/or extracts of herb Zanthoxylum alatum and Zingiber officinale for its antioxidant and antimicrobial properties, respectively. O. O. Igbinosa et al. and C.-C. Lee et al. provide the animal experiments using extractsfrom Jatropha curcas (Linn) leaf and from supercritical carbon dioxide extracted ginger, respectively. Three studies (F. M. Al-Jasass and M. S. Al-Jasser, X.-W. Chen et al., and C.-Y. Lo et al.) focus on biological functions of the compounds from Saudi Arabia herbs, Chinese herb Huang Lian (Rhizoma coptidis), and from Alpinia galangal, respectively. Further, C.-Y. Yen et al. provide the toxicological study for cardiotoxin III in growth inhibition of oral cancer. C.-Y. Lin et al. provide a review article for the chemoprevention of cytochrome P450 in oral potentially malignant disorders (OPMDs) patients in terms of betel quid metabolism. S.-S. Liang et al. introduce the novel technique for online monitoring oxidative products and metabolites of nicotine based on tandem mass spectrometry.

Some papers introduce the bioinformatics methods or resources to study or review the natural products-related studies. L. Wang et al. introduce the gene ontology (GO) network for the systems-theoretical analysis of human hepatocellular carcinoma. Y.-C. Lin et al. provide the database (TIPdb) for anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan. Three papers (W.-H. Huang et al. and J.-Y. Tang et al.) provide the drug discovery for cancer and disease therapy in terms of RNA editing, alternative splicing, and long noncoding RNAs as well as the summary for their bioinformatics resources.


Hsueh-Wei Chang

Li-Yeh Chuang

Sanjay Guleria

Sammia Yasmin

/pdf/natural-products-bioactivity-biochemistry-and-biological-3j96omq0oy.pdf

Natural Products: Bioactivity, Biochemistry, and Biological Effects in Cancer and Disease Therapy

The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space (''catfish particles''), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to 10 classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.

Improved binary particle swarm optimization using catfish effect for feature selection

Feature selection is a useful pre-processing technique for solving classification problems. The challenge of solving the feature selection problem lies in applying evolutionary algorithms capable of handling the huge number of features typically involved. Generally, given classification data may contain useless, redundant or misleading features. To increase classification accuracy, the primary objective is to remove irrelevant features in the feature space and to correctly identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully to solving feature selection problems. In this paper, two kinds of chaotic maps-so-called logistic maps and tent maps-are embedded in BPSO. The purpose of chaotic maps is to determine the inertia weight of the BPSO. We propose chaotic binary particle swarm optimization (CBPSO) to implement the feature selection, in which the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier for evaluating classification accuracies. The proposed feature selection method shows promising results with respect to the number of feature subsets. The classification accuracy is superior to other methods from the literature.

Chaotic maps based on binary particle swarm optimization for feature selection

Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data. An improved technique which combines chaotic map particle swarm optimization with an acceleration strategy is proposed, since results of one of the most used clustering algorithm, K-means can be jeopardized by improper choices made in the initializing stage. Accelerated chaotic particle swarm optimization (ACPSO) searches through arbitrary data sets for appropriate cluster centers and can effectively and efficiently find better solutions. Comparisons of the clustering performance are obtained from tests conducted on six experimental data sets; the algorithms compared with ACPSO includes PSO, CPSO, K-PSO, NM-PSO, K-NM-PSO and K-means clustering. Results of the robust performance from ACPSO indicate that this method an ideal alternative for solving data clustering problem.

Li-Yeh Chuang

Papers

Improved binary PSO for feature selection using gene expression data

Natural Products: Bioactivity, Biochemistry, and Biological Effects in Cancer and Disease Therapy

Improved binary particle swarm optimization using catfish effect for feature selection

Chaotic maps based on binary particle swarm optimization for feature selection

Chaotic particle swarm optimization for data clustering