Richard Pelikan

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Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of(More)
As technology improves, the amount of information we collect about the world increases. Sensor networks collect traffic or weather information in real-time, documents and news articles are distributed and searched on-line, information in medical records is collected and stored in electronic form. All of this information can be mined so that the relations(More)
MOTIVATION The 'reproducibility' of mass spectrometry proteomic profiling has become an intensely controversial topic. The mere mention of concern over the 'reproducibility' of data generated from any particular platform can lead to the anxiety over the generalizability of its results and its role in the future of discovery proteomics. In this study, we(More)
The purpose of this study was to provide a univariate and multivariate analysis of genomic microbial data and salivary mass-spectrometry proteomic profiles for dental caries outcomes. In order to determine potential useful biomarkers for dental caries, a multivariate classification analysis was employed to build predictive models capable of classifying(More)
Whole-sample mass spectrometry (MS) proteomics allows for a parallel measurement of hundreds of proteins present in a variety of biospecimens. Unfortunately, the association between MS signals and these proteins is not straightforward. The need to interpret mass spectra demands the development of methods for accurate labeling of ion species in such(More)
Finding reliable, meaningful patterns in data with high numbers of attributes can be extremely difficult. Feature selection helps us to decide what attributes or combination of attributes are most important for finding these patterns. In this chapter, we study feature selection methods for building classification models from high-throughput genomic(More)
Mass spectrometry proteomic profiling has potential to be a useful clinical screening tool. One obstacle is providing a standardized method for preprocessing the noisy raw data. We have developed a system for automatically determining a set of preprocessing methods among several candidates. Our system's automated nature relieves the analyst of the need to(More)
Multiple technologies that measure expression levels of protein mixtures in the human body offer a potential for detection and understanding the disease. The recent increase of these technologies prompts researchers to evaluate the individual and combined utility of data generated by the technologies. In this work, we study two data sources to measure the(More)
High-throughput biological assays such as micro-arrays and mass spectrometry (MS) have risen as potential clinical tools for disease detection. Multiple potential biomarkers can be rapidly and cheaply evaluated for a large number of patients. Typical research and evaluation studies in these fields have focused primarily on data that were generated from(More)
Peptide identification for whole-sample mass spectrometry (MS) proteomics is in its infancy. While sophisticated tandem MS/MS instrumentation exists for accurate peptide identification after sample separation, there are few options for those who produce data from intact protein samples. We present a novel algorithm which uses available information from the(More)