Cancer subtype identification pipeline: a classifusion approach

Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures

Aggregation operators are mathematical functions that enable the fusion of information from multiple sources. Fuzzy Integrals (FIs) are widely used aggregation operators, which combine information in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. However, FIs suffer from the potential drawback of not fusing information according to the intuitively interpretable FM, leading to non-intuitive results. The latter is particularly relevant when a FM has been defined using external information (e.g. experts). In order to address this and provide an alternative to the FI, the Recursive Average (RAV) aggregation operator was recently proposed which enables intuitive data fusion in respect to a given FM. With an alternative fusion operator in place, in this paper, we define the concept of ‘A Priori’ FMs which are generated based on external information (e.g. classification accuracy) and thus provide an alternative to the traditional approaches of learning or manually specifying FMs. We proceed to develop one specific instance of such an a priori FM to support the decision level fusion step in ensemble classification. We evaluate the resulting approach by contrasting the performance of the ensemble classifiers for different FMs, including the recently introduced Uriz and the Sugeno lambda-measure; as well as by employing both the Choquet FI and the RAV as possible fusion operators. Results are presented for 20 datasets from machine learning repositories and contextualised to the wider literature by comparing them to state-of-the-art ensemble classifiers such as Adaboost, Bagging, Random Forest and Majority Voting

Interpretability indices for hierarchical fuzzy systems

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve interpretability of fuzzy logic systems (FLSs). In recent years, a variety of indices have been proposed to measure the interpretability of FLSs such as the Nauck index and Fuzzy index. However, interpretability indices associated with HFSs have not so far been discussed. The structure of HFSs, with multiple layers, subsystems, and varied topologies, is the main challenge in constructing interpretability indices for HFSs. Thus, the comparison of interpretability between FLSs and HFSs-even at the index level-is still subject to open discussion. This paper begins to address these challenges by introducing extensions to the FLS Nauck and Fuzzy interpretability indices for HFSs. Using the proposed indices, we explore the concept of interpretability in relation to the different structures in FLSs and HFSs. Initial experiments on benchmark datasets show that based on the proposed indices, HFSs with equivalent function to FLSs produce higher indices, i.e. are more interpretable than their corresponding FLSs

Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms

The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study

Towards a framework for capturing interpretability of hierarchical fuzzy systems - a participatory design approach

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as: "How can we measure their interpretability?", "How can we make an informed assessment of how HFSs should be designed to enhance interpretability?". The challenges of measuring the interpretability of HFSs include issues such as their topological structure, the number of layers, the meaning of intermediate variables, and so on. In this paper, an initial framework to measure the interpretability of HFSs is proposed, combined with a participatory user design process to create a specific instance of the framework for an application context. This approach enables the subjective views of a range of practitioners, experts in the design and creation of FLSs, to be taken into account in shaping the design of a generic framework for measuring interpretability in HFSs. This design process and framework are demonstrated through two classification application examples, showing the ability of the resulting index to appropriately capture interpretability as perceived by system design experts

Recovery of the Historical SN1957D in X-rays with Chandra

SN1957D, located in one of the spiral arms of M83, is one of the small number of extragalactic supernovae that has remained detectable at radio and optical wavelengths during the decades after its explosion. Here we report the first detection of SN1957D in X-rays, as part of a 729 ks observation of M83 with \chandra. The X-ray luminosity (0.3 - 8 keV) is 1.7 (+2.4,-0.3) 10**37 ergs/s. The spectrum is hard and highly self-absorbed compared to most sources in M83 and to other young supernova remnants, suggesting that the system is dominated at X-ray wavelengths by an energetic pulsar and its pulsar wind nebula. The high column density may be due to absorption within the SN ejecta. HST WFC3 images resolve the supernova remnant from the surrounding emission and the local star field. Photometry of stars around SN1957D, using WFC3 images, indicates an age of less than 10**7 years and a main sequence turnoff mass more than 17 solar masses. New spectra obtained with Gemini-South show that the optical spectrum continues to be dominated by broad [O III] emission lines, the signature of fast-moving SN ejecta. The width of the broad lines has remained about 2700 km/s (FWHM). The [O III] flux dropped precipitously between 1989 and 1991, but continued monitoring shows the flux has been almost constant since. In contrast, radio observations over the period 1990-2011 show a decline rate inf the flux proportional to t**-4, far steeper than the rate observed earlier, suggesting that the primary shock has overrun the edge of a pre-SN wind.Comment: 28 pages, including 3 tables and 7 figures, accepted for publication in Ap

Decrease of breast cancer cell invasiveness by sodium phenylacetate (NaPa) is associated with an increased expression of adhesive molecules

Sodium phenylacetate (NaPa), a non-toxic phenylalanine metabolite, has been shown to induce in vivo and in vitro cytostatic and antiproliferative effects on various cell types. In this work, we analysed the effect of NaPa on the invasiveness of breast cancer cell (MDA-MB-231, MCF-7 and MCF-7 ras). Using the highly invasive breast cancer cell line MDA-MB-231, we demonstrated that an 18-hour incubation with NaPa strongly inhibits the cell invasiveness through Matrigel (86% inhibition at 20 mM of NaPa). As cell invasiveness is greatly influenced by the expression of urokinase (u-PA) and its cell surface receptor (u-PAR) as well as the secretion of matrix metalloproteinases (MMP), we tested the effect of NaPa on these parameters. An 18-hour incubation with NaPa did not modify u-PA expression, either on MDA-MB-231 or on MCF-7 and MCF-7 ras cell lines, and induced a small u-PA decrease after 3 days of treatment of MDA-MB-321 with NaPa. In contrast, an 18 h incubation of MDA-MB-231 increased the expression of u-PAR and the secretion of MMP-9. As u-PAR is a ligand for vitronectin, a composant of the extracellular matrix, these data could explain the increased adhesion of MDA-MB-231 to vitronectin, while cell adhesivity of MCF-7 and MCF-7 ras was unmodified by NaPa treatment. NaPa induced also an increased expression of both Lymphocyte Function-Associated-1 (LFA-1) and Intercellular Adhesion Molecule-1 (ICAM-1), which was obvious from 18 hour incubation with NaPa for the MDA-MB-231 cells, but was delayed (3 days) for MCF-7 and MCF-7 ras. Only neutralizing antibodies against LFA-1 reversed the decreased invasiveness of NaPa-treated cells. Therefore we can conclude that the strong inhibition of MDA-MB-231 invasiveness is not due to a decrease in proteases involved in cell migration (u-PA and MMP) but could be related both to the modification of cell structure and an increased expression of adhesion molecules such as u-PAR and LFA-1. © 2001 Cancer Research Campaign http://www.bjcancer.co

Extra-nuclear starbursts: Young luminous hinge clumps in interacting galaxies

Hinge clumps are luminous knots of star formation near the base of tidal features in some interacting galaxies. We use archival Hubble Space Telescope (HST) UV/optical/IR images and Chandra X-ray maps along with Galaxy Evolution Explorer UV, Spitzer IR, and ground-based optical/near-IR images to investigate the star forming properties in a sample of 12 hinge clumps in five interacting galaxies. The most extreme of these hinge clumps have star formation rates of 1-9 M ☉ yr–1, comparable to or larger than the "overlap" region of intense star formation between the two disks of the colliding galaxy system the Antennae. In the HST images, we have found remarkably large and luminous sources at the centers of these hinge clumps. These objects are much larger and more luminous than typical "super star clusters" in interacting galaxies, and are sometimes embedded in a linear ridge of fainter star clusters, consistent with star formation along a narrow caustic. These central sources have FWHM diameters of ~70 pc, compared to ~3 pc in "ordinary" super star clusters. Their absolute I magnitudes range from MI ~ – 12.2 to –16.5; thus, if they are individual star clusters they would lie near the top of the "super star cluster" luminosity function of star clusters. These sources may not be individual star clusters, but instead may be tightly packed groups of clusters that are blended together in the HST images.Comparison to population synthesis modeling indicates that the hinge clumps contain a range of stellar ages. This is consistent with expectations based on models of galaxy interactions, which suggest that star formation may be prolonged in these regions.In the Chandra images, we have found strong X-ray emission from several of these hinge clumps. In most cases, this emission is well-resolved with Chandra and has a thermal X-ray spectrum, thus it is likely due to hot gas associated with the star formation. The ratio of the extinction-corrected diffuse X-ray luminosity to the mechanical energy rate (the X-ray production efficiency) for the hinge clumps is similar to that in the Antennae galaxies, but higher than those for regions in the normal spiral galaxy NGC 2403. Two of the hinge clumps have point-like X-ray emission much brighter than expected for hot gas; these sources are likely "ultra-luminous X-ray sources" due to accretion disks around black holes. The most extreme of these sources, in Arp 240, has a hard X-ray spectrum and an absorbed X-ray luminosity of ~2 × 1041 erg s–1; this is above the luminosity expected by single high mass X-ray binaries (HMXBs), thus it may be either a collection of HMXBs or an intermediate mass black hole (≥80 M ☉)

Advertising Liking Recognition Technique Applied to Neuromarketing by Using Low-Cost EEG Headset

In this paper a new neuroscience technique is applied into Marketing, which is becoming commonly known as the field of Neuromarketing. The aim of this paper is to recognize how brain responds during the visualization of short advertising movies. Using low cost electroencephalography (EEG), brain regions used during the presentation have been studied. We may wonder about how useful it is to use neuroscience knowledge in marketing, what can neuroscience add to marketing, or why use this specific technique. By using discrete techniques over EEG frequency bands of a generated labeled dataset, C4.5 and ANN learning methods have been applied to obtain the score assigned to each ads by the user. This techniques allows to reach more than 82% of accuracy, which is an excellent result taking into account the kind of low-cost EEG sensors used.Ministerio de Economía y Competitividad TIN2013-46801- C4-1-rJunta de Andalucía TIC-805