Papillary Thyroid Carcinoma: A thorough Bioinformatic Analysis of Gene Expression and Clinical Data.

The likelihood of being diagnosed with thyroid cancer has increased in recent years; it is the fastest-expanding cancer in the United States and it has tripled in the last three decades. In particular, Papillary Thyroid Carcinoma (PTC) is the most common type of cancer affecting the thyroid. It is a slow-growing cancer and, thus, it can usually be cured.

Hemoxygenase-1 Promotes Head and Neck Cancer Cell Viability.

Head and neck squamous cell carcinoma (HNSCC) is a remarkably heterogeneous disease with around 50% mortality, a fact that has prompted researchers to try new approaches to improve patient survival. Hemoxygenase-1 (HO-1) is the rate-limiting step for heme degradation into carbon monoxide, free iron and biliverdin. We have previously reported that HO-1 protein is upregulated in human HNSCC samples and that it is localized in the cytoplasmic and nuclear compartments; additionally, we have demonstrated that HO-1 nuclear localization is associated with malignant progression.

A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching.

The identification of nested motifs in genomic sequences is a complex computational problem. The detection of these patterns is important to allow the discovery of transposable element (TE) insertions, incomplete reverse transcripts, deletions, and/or mutations. In this study, a de novo strategy for detecting patterns that represent nested motifs was designed based on exhaustive searches for pairs of motifs and combinatorial pattern analysis.

Crosstalk pathway inference using topological information and biclustering of gene expression data.

Detection of crosstalks among pathways is a challenging task, which requires the identification of different types of interactions associated with cellular processes. A common strategy used in bioinformatics consists in extrapolating pathway associations from the pairwise analysis of some genes related to them, using gene expression data and topological information. PET, the method proposed in this paper, goes a step further by incorporating a strategy for the detection of correlation across conditions between differentially expressed genes based on biclustering analysis.

Discretization of gene expression data revised.

Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference. Selection of an appropriate discretization process has a major impact on the design and outcome of the inference algorithms, as there are a number of relevant issues that need to be considered.

Discovering time-lagged rules from microarray data using gene profile classifiers.

Background: Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes.

Results: This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets.