Design of a Multi-Feature Classification Scheme for Infant Epileptic Seizures.

Neonatal epileptic seizures take place in the early childhood years, accounting for a severe condition with several deaths and neurological problems in newborn neonates. Despite the early advancements on the diagnosis and/or treatment of this condition, as a major difficulty accounts the inability of the physicians to identify and characterize a seizure, as one a small percentage gets detected in neonatal intensive care units (NICU). An important step towards any kind of seizure classification is the detection and reduction of non-cerebral activity. Towards this direction, our multi-feature approach contains spectral and statistical characteristics of EEG signals of 79 infants with suspicion of seizure and assesses the performance of two classification algorithms iteratively. The trained models (Support Vector Machine (SVM) and Random Forest classifiers) yielded high classification performance (>80% and >85% respectively). A robust neonatal seizure classification scheme is thus proposed, along with nine high scoring spectrum and statistical features. The importance of embedding an artefact reduction approach is also discussed, since the complex artifacts spread throughout the signals have great impact on the accuracy of the algorithms. The nine extracted high scoring spectral and statistical features might be used as potential biomarkers for neonatal seizure prediction in a clinical setting.

Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction.

The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional methods that produce genomic signatures from DNA Microarray data tend to extract significantly different lists under relatively small variations of the training data. That instability hinders the validity of research findings and raises skepticism about the reliability of such methods. In this study, a complete framework for the extraction of stable and reliable lists of candidate genes is presented. The proposed methodology enforces stability of results at the validation step and as a result, it is independent of the feature selection and classification methods used. Furthermore, two different statistical tests are performed in order to assess the statistical significance of the observed results. Moreover, the consistency of the signatures extracted by independent executions of the proposed method is also evaluated. The results of this study highlight the importance of stability issues in genomic signatures, beyond their prediction capabilities.

Proteome Changes during Transition from Human Embryonic to Vascular Progenitor Cells.

Human embryonic stem cells (hESCs) are promising in regenerative medicine (RM) due to their differentiation plasticity and proliferation potential. However, a major challenge in RM is the generation of a vascular system to support nutrient flow to newly synthesized tissues. Here we refined an existing method to generate tight vessels by differentiating hESCs in CD34(+) vascular progenitor cells using chemically defined media and growth conditions. We selectively purified these cells from CD34(-) outgrowth populations also formed. To analyze these differentiation processes, we compared the proteomes of the hESCs with those of the CD34(+) and CD34(-) populations using high resolution mass spectrometry, label-free quantification, and multivariate analysis. Eighteen protein markers validate the differentiated phenotypes in immunological assays; nine of these were also detected by proteomics and show statistically significant differential abundance. Another 225 proteins show differential abundance between the three cell types. Sixty-three of these have known functions in CD34(+) and CD34(-) cells. CD34(+) cells synthesize proteins implicated in endothelial cell differentiation and smooth muscle formation, which support the bipotent phenotype of these progenitor cells. CD34(-) cells are more heterogeneous synthesizing muscular/osteogenic/chondrogenic/adipogenic lineage markers. The remaining >150 differentially abundant proteins in CD34(+) or CD34(-) cells raise testable hypotheses for future studies to probe vascular morphogenesis.

Comparative proteomic analysis of hypertrophic chondrocytes in osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a multi-factorial disease leading progressively to loss of articular cartilage and subsequently to loss of joint function. While hypertrophy of chondrocytes is a physiological process implicated in the longitudinal growth of long bones, hypertrophy-like alterations in chondrocytes play a major role in OA. We performed a quantitative proteomic analysis in osteoarthritic and normal chondrocytes followed by functional analyses to investigate proteome changes and molecular pathways involved in OA pathogenesis. METHODS: Chondrocytes were isolated from articular cartilage of ten patients with primary OA undergoing knee replacement surgery and six normal donors undergoing fracture repair surgery without history of joint disease and no OA clinical manifestations. We analyzed the proteome of chondrocytes using high resolution mass spectrometry and quantified it by label-free quantification and western blot analysis. We also used WebGestalt, a web-based enrichment tool for the functional annotation and pathway analysis of the differentially synthesized proteins, using the Wikipathways database. ClueGO, a Cytoscape plug-in, is also used to compare groups of proteins and to visualize the functionally organized Gene Ontology (GO) terms and pathways in the form of dynamical network structures. RESULTS: The proteomic analysis led to the identification of a total of ~2400 proteins. 269 of them showed differential synthesis levels between the two groups. Using functional annotation, we found that proteins belonging to pathways associated with regulation of the actin cytoskeleton, EGF/EGFR, TGF-ß, MAPK signaling, integrin-mediated cell adhesion, and lipid metabolism were significantly enriched in the OA samples (p ≤10(-5)). We also observed that the proteins GSTP1, PLS3, MYOF, HSD17B12, PRDX2, APCS, PLA2G2A SERPINH1/HSP47 and MVP, show distinct synthesis levels, characteristic for OA or control chondrocytes. CONCLUSION: In this study we compared the quantitative changes in proteins synthesized in osteoarthritic compared to normal chondrocytes. We identified several pathways and proteins to be associated with OA chondrocytes. This study provides evidence for further testing on the molecular mechanism of the disease and also propose proteins as candidate markers of OA chondrocyte phenotype.

A network-based approach to enrich gene signatures for the prediction of breast cancer metastases.

Despite the multiplicity of the gene expression analysis studies for the identification of genomics based origins of cancerous diseases, the presented gene signatures have generally little overlap. The genes do not function in isolation and therefore a more holistic approach that takes into account the interactions among them is needed. In this study we present a stepwise refinement methodology where starting from some initial set of biomarkers we expand and enrich this set taking into account existing biological information. In particular, we start with a 27 gene signature previously identified as indicative of the presence of circulating tumor cells (CTCs) in peripheral blood of breast cancer patients. We use the manually curated HINT database of protein-protein interactions as the background biological network to locate the network-based similarity of the input genes and how they connect to each other. The result is an enriched connected set of genes that is subsequently expanded to form an even bigger network based on the ability of the surrounding genes to strongly correlate with the phenotypes of a training set of breast cancer patient cases. The induced network is then used as a new gene signature for the classification of breast brain metastases in an independent dataset. The results are encouraging for the validity of this method.

Designing a patient monitoring system for bipolar disorder using Semantic Web technologies.

The new movement to personalize treatment plans and improve prediction capabilities is greatly facilitated by intelligent remote patient monitoring and risk prevention. This paper focuses on patients suffering from bipolar disorder, a mental illness characterized by severe mood swings. We exploit the advantages of Semantic Web and Electronic Health Record Technologies to develop a patient monitoring platform to support clinicians. Relying on intelligently filtering of clinical evidence-based information and individual-specific knowledge, we aim to provide recommendations for treatment and monitoring at appropriate time or concluding into alerts for serious shifts in mood and patients' non response to treatment.

On the identification of circulating tumor cells in breast cancer.

Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence.

Shape-influenced clustering of dynamic patterns of gene profiles.

Statistical evaluation of temporal gene expression profiles plays an important role in particular biological processes and conditions. We introduce a clustering method for this purpose, which is based on the expression patterns but is also influenced by temporal changes. We compare the results of our platform with methods based on expression or the rank of temporal changes. The proposed platform is illustrated with a temporal gene expression dataset comprised of primary human chondrocytes and mesenchymal stem cells (MSCs). We derived three clusters in each cell type and compared the content of these classes in terms of temporal changes, which can support biological performance. For statistical evaluation we introduce a validity measure that takes under consideration these temporal changes and we also perform an enrichment analysis of three central genes in each cluster. Even though we can detect certain statistical similarities, these might be due to different biological processes. Our proposed platform contributes to both the statistical and biological validation of temporal profiles.

A disease annotation study of gene signatures in a breast cancer microarray dataset.

Breast cancer is a complex disease with heterogeneity between patients regarding prognosis and treatment response. Recent progress in advanced molecular biology techniques and the development of efficient methods for database mining lead to the discovery of promising novel biomarkers for prognosis and prediction of breast cancer. In this paper, we applied three computational algorithms (RFE-LNW, Lasso and FSMLP) to one microarray dataset for breast cancer and compared the obtained gene signatures with a recently described disease-agnostic tool, the Genotator. We identified a panel of 152 genes as a potential prognostic signature and the ERRFI1 gene as possible biomarker of breast cancer disease.