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

Fault Coverage Requirement in Production Testing of LSI Circuits

A technique is described for evaluating the effectiveness of production tests for large scale integrated (LSI) circuit chips. It is based on a model for the distribution of faults on a chip. The model requires two parameters, the average number (n0) of faults on a faulty chip and the yield (y) of good chips. It is assumed that the yield either is known or can be calculated from the available formulas. The other parameter, n0, is determined from an experimental procedure. Once the model is fully characterized, it allows calculation of the field reject rate as a function of the fault coverage. The technique implicitly takes into account such variables as fault simulator characteristics, the feature size, and the manufacturing environment. An actual LSI circuit is used as an example

Forecasting Reject Rate of Tested LSI Chips

The reject rate of LSI chips due to incomplete fault coverage of the tests is the fraction of faulty chips, among the chips that pass the tests. This reject rate, which is a measure of the tested chip quality, contributes to the field returns. It is, however, difficult to determine the tested chip quality from the field return data which may also include rejects due to handling damages, infant mortality, etc. Also, a large number of chips must be in use in the field before an adequate amount of field return data can be obtained. This paper gives a method of forecasting the reject rate from the test data alone before any field trials are made

High frequency occurrence of single cotyledonary embryo morphotype and repetitive somatic embryogenesis in ‘Thompson Seedless’ crossed with seven grapevine male parents

Direct somatic embryogenesis was observed in zygotic embryos rescued from intra- and inter-specific crosses between ‘Thompson Seedless’ and seven male parents of grapevine when the embryos were cultured on Woody Plant Medium (WPM) supplemented with benzyladenine (1 μM). Repetitive somatic embryogenesis occurred on the same medium, which also supported a high percentage of embryo maturation, germination and plantlet development. The cultures retained embryogenic potential for more than two years. We observed a high frequency occurrence of monocotyledonous embryo morphotype and other morphological variations in somatic embryos of all the crosses. The percentage of embryos having mono-, di-, tri-, multiple and abnormal cotyledons varied among the crosses. The overall percentage of monocot embryos was 35.50 %, as against 38.64 % of dicot embryos, while the germination rates for mono- and dicot- embryos were 24.44 % and 24.15 %, respectively. Shoot development was poor in tri- and multiple-cotyledonary embryos, while there was no shoot formation in abnormal embryos. We assume that the relatively high occurrence of single cotyledonary morphotype may be due to the repetitive exposure of embryogenic tissues to a medium containing benzyladenine

The Rape that Woke Up India: Hindu Imagination and the Rape of Jyoti Singh Pandey

This essay was inspired by the gang-rape of 23-year-old Jyoti Singh Pandey in Delhi, India, on December 16, 2012. Thirteen days later she died in a Singapore hospital from injuries caused by insertion of an iron rod by her six attackers. The authors first analyze the remarks of politicians and religious leaders that invoked religious- nationalist ideals to diminish the responsibility of the attackers, to exonerate traditional Hindu ways of life, and to blame the victim. The essay next examines cultural and religious contexts of gang-rape, in particular, the positive and negative images of women in traditional Hindu mythologies and scriptures. Theories about why some men rape and why some rapists mutilate the genitalia of their victims are considered. The essay includes results of interviews and surveys of Indians in India carried out during the summer of 2013. Questions focused on religious issues such as the extent to which the mentality that women transgressing traditional limits are responsible for what happens to them fosters a rape-tolerant atmosphere. The authors conclude that parts of the sacred tradition can be useful for enhancing the status and safety of women in India today, while other, clearly misogynistic parts must be recognized, critiqued, and rejected

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

Forecasting Reject Rate of Tested LSI Chips

The reject rate of LSI chips due to incomplete fault coverage of the tests is the fraction of faulty chips, among the chips that pass the tests. This reject rate, which is a measure of the tested chip quality, contributes to the field returns. It is, however, difficult to determine the tested chip quality from the field return data which may also include rejects due to handling damages, infant mortality, etc. Also, a large number of chips must be in use in the field before an adequate amount of field return data can be obtained. This paper gives a method of forecasting the reject rate from the test data alone before any field trials are made

Grapevine DNA polymorphisms revealed by microsatellite-derived markers from soybean and rice

We report detection of DNA polymorphisms in grapevine by the use of microsatellite-flanking primer pairs from soybean and rice. These “cross species” microsatellite-derived markers were checked for their inheritance patterns in controlled grapevine crosses. They produced multiple bands that segregated and can be scored as individual genetic markers of dominant type. Employed in genetic mapping studies they offer advantages such as improved reproducibility in comparison to commonly used multi-locus marker systems like RAPDs and AFLPs.

Accurate Computation of Field Reject Ratio Based on Fault Latency

The field reject ratio, the fraction of defective devices that pass the acceptance test, is a measure of the quality of the tested product. Although the assessment of quality is important, an accurate measurement of the field reject ratio of tested VLSI chips is often not feasible. We show that the known methods of field reject ratio prediction are not accurate since they fail to realistically model the process of testing. We model the detection of a fault by an input test vector as a random event. However, we recognize that the detection of a fault may be delayed for various reasons: the fault may be detectable only by application of a sequence of vectors or it may not have been targeted until later. In our statistical model, a fault is characterized by two parameters: a per-vector detection probability and an integer-valued latency. Irrespective of the detection probability, the fault cannot be detected by a vector sequence shorter than its latency. The circuit is characterized by the joint distribution of latency and detection probability over all faults. This distribution, obtained by applying the Bayes’ rule to the actual test data, enables us to compute the field reject ratio. The sensitivity of this approach to variations in the measured parameters is also investigated