Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud

Proteomic analysis of nipple aspirate fluid to detect biologic markers of breast cancer.

The early detection of breast cancer is the best means to minimise disease-related mortality. Current screening techniques have limited sensitivity and specificity. Breast nipple aspirate fluid can be obtained noninvasively and contains proteins secreted from ductal and lobular epithelia. Nipple aspirate fluid proteins are breast specific and generally more concentrated than corresponding blood levels. Proteomic analysis of 1 microl of diluted nipple aspirate fluid over a 5-40 kDa range from 20 subjects with breast cancer and 13 with nondiseased breasts identified five differentially expressed proteins. The most sensitive and specific proteins were 6500 and 15 940 Da, found in 75-84% of samples from women with cancer but in only 0-9% of samples from normal women. These findings suggest that (1) differential expression of nipple aspirate fluid proteins exists between women with normal and diseased breasts, and (2) analysis of these proteins may predict the presence of breast cancer

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features

Differential Response of Primary and Immortalized CD4+ T Cells to Neisseria gonorrhoeae-Induced Cytokines Determines the Effect on HIV-1 Replication

To compare the effect of gonococcal co-infection on immortalized versus primary CD4+ T cells the Jurkat cell line or freshly isolated human CD4+ T cells were infected with the HIV-1 X4 strain NL4-3. These cells were exposed to whole gonococci, supernatants from gonococcal-infected PBMCs, or N. gonorrhoeae-induced cytokines at varying levels. Supernatants from gonococcal-infected PBMCs stimulated HIV-1 replication in Jurkat cells while effectively inhibiting HIV-1 replication in primary CD4+ T cells. ELISA-based analyses revealed that the gonococcal-induced supernatants contained high levels of proinflammatory cytokines that promote HIV-1 replication, as well as the HIV-inhibitory IFNα. While all the T cells responded to the HIV-stimulatory cytokines, albeit to differing degrees, the Jurkat cells were refractory to IFNα. Combined, these results indicate that N. gonorrhoeae elicits immune-modulating cytokines that both activate and inhibit HIV-production; the outcome of co-infection depending upon the balance between these opposing signals

Proteomics as a Method for Early Detection of Cancer: A Review of Proteomics, Exhaled Breath Condensate, and Lung Cancer Screening

The study of expressed proteins in neoplasia is undergoing a revolution with the advent of proteomic analysis. Unlike genomic studies where individual changes may have no functional significance, protein expression is closely aligned with cellular activity. This perspective will review proteomics as a method of detecting markers of neoplasia with a particular emphasis on lung cancer and the potential to sample the lung by exhaled breath condensate (EBC). EBC collection is a simple, new, and noninvasive technique, which allows sampling of lower respiratory tract fluid. EBC enables the study of a wide variety of biological markers from low molecular weight mediators to macromolecules, such as proteins, in a range of pulmonary diseases. EBC may be applied to the detection of lung cancer where it could be a tool in early diagnosis. This perspective will explore the potential of applying proteomics to the EBC from lung cancer patients as an example of detecting potential biomarkers of disease and progression

DNA Aptamers as Molecular Probes for Colorectal Cancer Study

Understanding the molecular features of specific tumors can increase our knowledge about the mechanism(s) underlying disease development and progression. This is particularly significant for colorectal cancer, which is a heterogeneous complex of diseases developed in a sequential manner through a multistep carcinogenic process. As such, it is likely that tumors with similar characteristics might originate in the same manner and have a similar molecular behavior. Therefore, specific mapping of the molecular features can be potentially useful for both tumor classification and the development of appropriate therapeutic regimens. However, this can only be accomplished by developing high-affinity molecular probes with the ability to recognize specific markers associated with different tumors. Aptamers can most easily meet this challenge based on their target diversity, flexible manipulation and ease of development.Using a method known as cell-based Systematic Evolution of Ligands by Exponential enrichment (cell-SELEX) and colorectal cancer cultured cell lines DLD-1 and HCT 116, we selected a panel of target-specific aptamers. Binding studies by flow cytometry and confocal microscopy showed that these aptamers have high affinity and selectivity. Our data further show that these aptamers neither recognize normal colon cells (cultured and fresh), nor do they recognize most other cancer cell lines tested.The selected aptamers can identify specific biomarkers associated with colorectal cancers. We believe that these probes could be further developed for early disease detection, as well as prognostic markers, of colorectal cancers