8,102 research outputs found

    Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.

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    The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers

    Comparing the Performances of Neural Network and Rough Set Theory to Reflect the Improvement of Prognostic in Medical Data

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    In this research, I investigate and compared two of Artificial Intelligence (AI)techniques which are; Neural network and Rough set will be the best technique to be use in analyzing data. Recently, AI is one of the techniques which still in development process that produced few of intelligent systems that helped human to support their daily life such as decision making. In Malaysia, it is newly introduced by a group of researchers from University Science Malaysia. They agreed with others world-wide researchers that AI is very helpful to replaced human intelligence and do many works that can be done by human especially in medical area.In this research, I have chosen three sets of medical data; Wisoncin Prognostic Breast cancer, Parkinson’s diseases and Hepatitis Prognostic. The reason why the medical data is selected for this research because of the popularity among the researchers that done their research in AI by using medical data and the prediction or target attributes is clearly understandable. The results and findings also discussed in this paper. How the experiment has been done; the steps involved also discussed in this paper. I also conclude this paper with conclusion and future work

    A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

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    Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

    Recent advances in the theory and practice of logical analysis of data

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    Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning methods by the fact that it analyzes a significant subset of combinations of variables to describe the positive or negative nature of an observation and uses combinatorial techniques to extract models defined in terms of patterns. In recent years, the methodology has tremendously advanced through numerous theoretical developments and practical applications. In the present paper, we review the methodology and its recent advances, describe novel applications in engineering, finance, health care, and algorithmic techniques for some stochastic optimization problems, and provide a comparative description of LAD with well-known classification methods

    Clinical expression of facioscapulohumeral muscular dystrophy in carriers of 1-3 D4Z4 reduced alleles: Experience of the FSHD Italian National Registry

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    OBJECTIVES: Facioscapulohumeral muscular dystrophy type 1 (FSHD1) has been genetically linked to reduced numbers ( 64 8) of D4Z4 repeats at 4q35. Particularly severe FSHD cases, characterised by an infantile onset and presence of additional extra-muscular features, have been associated with the shortest D4Z4 reduced alleles with 1-3 repeats (1-3 DRA). We searched for signs of perinatal onset and evaluated disease outcome through the systematic collection of clinical and anamnestic records of de novo and familial index cases and their relatives, carrying 1-3 DRA. SETTING: Italy. PARTICIPANTS: 66 index cases and 33 relatives carrying 1-3 DRA. OUTCOMES: The clinical examination was performed using the standardised FSHD evaluation form with validated inter-rater reliability. To investigate the earliest signs of disease, we designed the Infantile Anamnestic Questionnaire (IAQ). Comparison of age at onset was performed using the non-parametric Wilcoxon rank-sum or Kruskal-Wallis test. Comparison of the FSHD score was performed using a general linear model and Wald test. Kaplan-Meier survival analysis was used to estimate the age-specific cumulative motor impairment risk. RESULTS: No patients had perinatal onset. Among index cases, 36 (54.5%) showed the first signs by 10 years of age. The large majority of patients with early disease onset (26 out of 36, 72.2%) were de novo; whereas the majority of patients with disease onset after 10 years of age were familial (16, 53.3%). Comparison of the disease severity outcome between index cases with age at onset before and over 10 years of age, failed to detect statistical significance (Wald test p value=0.064). Of 61 index cases, only 17 (27.9%) presented extra-muscular conditions. Relatives carrying 1-3 DRA showed a large clinical variability ranging from healthy subjects, to patients with severe motor impairment. CONCLUSIONS: The size of the D4Z4 allele is not always predictive of severe clinical outcome. The high degree of clinical variability suggests that additional factors contribute to the phenotype complexity
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