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Biomedical subjects

Simon Rosenfeld

Publications and source records attributed to Simon Rosenfeld.

3 recordsLinked to original sources

Overview of commonly used bioinformatics methods and their applications.

Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These include a wide variety of clustering and classification algorithms, including self-organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and even hyphenated techniques as neuro-fuzzy networks. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities.

Computational Biology↗

New developments in cancer-related computational statistics.

A brief overview is presented of recently developed and currently emerging statistical and computational techniques that have been proved to be highly helpful in handling the avalanche of the new type of data generated by modern high-throughput technologies in experimental biology. The review, in no way comprehensive, focuses attention on Bayesian Networks, Hidden Markov Chain, and methods of chaotic dynamics for time-course genomic data; innovative methods in optimization and clustering; and multiple testing in the context of identification of differentially expressed genes.

Bayes Theorem↗

Numerical deconvolution of cDNA microarray signal: simulation study.

A computational model for simulation of the cDNA microarray experiments has been created. The simulation allows one to foresee the statistical properties of replicated experiments without actually performing them. We introduce a new concept, the so-called bio-weight, which allows for reconciliation between conflicting meanings of biological and statistical significance in microarray experiments. It is shown that, for a small sample size, the bio-weight is a more powerful criterion of the presence of a signal in microarray data as compared to the standard approach based on t test. Joint simulation of microarray and quantitative PCR data shows that the genes recovered by using the bio-weight have better chances to be confirmed by PCR than those obtained by the t test technique. We also employ extreme value considerations to derive plausible cutoff levels for hypothesis testing.

Cell Line, Tumor↗