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MicroRNAs are small non-coding RNAs of 21-25 nucleotides that might impact regulatory mechanisms in cancer. Due to their influence on cell physiology, alteration of miRNA regulation can be implicated in carcinogenesis and disease progression. In general, one miRNA is predicted to regulate several hundred genes, and as a result, miRNA profiling could serve as a better classifier than gene expression profiling.More than 50% of brain metastasis (brain mets) are associated with non-small cell lung cancer (NSCLC). As miRNAs can regulate certain genes, the presence or absence of certain miRNA could lead to oncogene potential for brain mets. In this study, we combine validated miRNA expression values with imaging features to separate NSCLC brain mets from non-brain mets and identify biomarkers that may indicate possibility of brain mets. This research involves comprehensive miRNA expression profiling, validation of miRNA with qRT-PCR, correlation of miRNA with imaging features such as PET/CT and CT Scan. Eleven statistically significant miRNA were identified and matched with imaging features to yield a class separation of brain mets and non-brain mets.

MicroRNAs (miRNAs) are 20–22 nucleotide, single-stranded, non-coding RNA molecules capable of regulating gene expression at both the transcriptional and translational level. miRNAs have shown involvement in carcinogenesis pathways by suppressing oncogenes. Conventional miRNA expression profiling was performed using tumor tissue samples, which quite recently has shifted to serum, opening a new of field of research for non-invasive tumor detection. We conducted a study utilizing serum specimens collected by the Pancreatic Cancer Research Team (PCRT) under an IRB approved protocol. Using an in-silico conditioning of biological samples, we confirmed that from a small amount of serum; comprehensive miRNA expression profiling is feasible and can identify biomarkers. Furthermore, there appear to be coherently expressing serum miRNAs between clinically matched pancreatic cancer patients (early-stage and locally-advanced, unresectable) compared with at-risk healthy individuals.

Extracting microorganisms from their natural environment has become a popular technique. These metagenomic fragments lack enough information that can mark them into taxonomic groups. In this paper, we implement a fuzzy k-means classifier to separate fragments into taxonomic groups present in a metagenomic data set. The fuzzy classifier is used to group shotgun sequence fragments as small as 500 base pairs according to their DNA signatures, namely GC content and oligonucleotide frequencies. A comparison of using different signatures is done and we analyze results and compare them. The classifier is also tested to classify acid mine drainage metagenome into classes to represent the major Archea and Bacteria groups. The classification achieved an accuracy of 99% for acid mine drainage a published environmental genome sample.

Nucleotide sequencing of genomic data is an important step towards building understanding of gene expression. Current limitations in sequencing limit the number of base pairs that can be processed to only several hundred at a time. Consequently, these sequenced substrings need to be assembled into the overall genome. However, the existence of insertions, deletions and substitutions can complicate the assembly of subsequences and confuse existing methods. What has been needed is an approach that deals with ambiguity in trying to match and assemble a genome from its sequenced subsequences. This research develops fuzzy similarity measures between subsequences that are then incorporated into an assembler based on fuzzy logic and fuzzy similarity measures. The research addresses the problem of extensive computation required by clustering data into meaningful groups. Preliminary evaluation of this approach in conjunction with K-Means clustering suggests that this approach is at least as good as standard approaches and in some cases better.

DNA matching is a crucial step in sequence alignment. Since sequence alignment is an approximate matching process there is a need for good approximate algorithms. The process of matching in sequence alignment is generally finding longest common subsequences. However, finding a longest common subsequence may not be the best solution for either a database match or an assembly. An optimal alignment of subsequences is based on several factors, such as quality of bases, length of overlap, etc. Factors such as quality indicate if the data is an actual read or an experimental error. Fuzzy logic allows tolerance of inexactness or errors in sub sequence matching. We propose fuzzy logic for approximate matching of subsequences. Fuzzy characteristic functions are derived for parameters that influence a match. We develop a prototype for a fuzzy assembler. The assembler is designed to work with low quality data which is generally rejected by most of the existing techniques. We test the assembler on sequences from two genome projects namely, Drosophila melanogaster and Arabidopsis thaliana. The results are compared with other assemblers. The fuzzy assembler successfully assembled sequences and performed similar and in some cases better than existing techniques

K-means is a popular clustering algorithm that requires a huge initial set to start the clustering. K-means is an unsupervised clustering method which does not guarantee convergence. Numerous improvements to K-means have been done to make its performance better. Expectation Maximization is a statistical technique for maximum likelihood estimation using mixture models. It searches for a local maxima and generally converges very well. The proposed algorithm combines these two algorithms to generate optimum clusters which do not require a huge value of K and each cluster attains a more natural shape and guarantee convergence. The paper compares the new method with Fuzzy K-means on benchmark iris data.

Several methods for doing intrusion detection have been developed over the years. However, most of these methods are based on crisp statistical techniques that measure deviation from a norm. Due to the wide range of attacks on computers, statistical methods are not always effective because they aggregate many system variables into a single mathematical measure. Instead, taxonomies of attack features based on the concepts of fuzzy logic can be utilized to classify attacks and build simple response rules based on local system variables. Taxonomies however require correct hierarchial construction from subtaxonomies of attack classifiers. An architecture that defines self organizing taxonomies based on fuzzy logic is therefore developed for future investigation.