Simplification of rules extracted from neural networks

Artificial neural networks (ANNs) have been proven to be successful general machine learning techniques for, amongst others, pattern recognition and classification. Realworld problems in agriculture (soybean, tea), medicine (cancer, cardiology, mammograms) and finance (credit rating, stock market) are successfully solved using ANNs. ANNs model biological neural systems. A biological neural system consists of neurons interconnected through neural synapses. These neurons serve as information processing units. Synapses carrt information to the neurons, which then processes or responds to the data by sending a signal to the next level of neurons. Information is strengthened or lessened according to the sign ..and magnitude of the weight associated with the connection. An ANN consists of cell-like entities called units (also called artificial neurons) and weighted connections between these units referred to as links. ANNs can be viewed as a directed graph with weighted connections. An unit belongs to one of three groups: input, hidden or output. Input units receive the initial training patterns, which consist of input attributes and the associated target attributes, from the environment. Hidden units do not interact with the environment whereas output units presents the results to the environment. Hidden and output units compute an output ai which is a function f of the sum of its input weights w; multiplied by the output x; of the units j in the preceding layer, together with a bias term fh that acts as a threshold for the unit. The output ai for unit i with n input units is calculated as ai = f("f:,'J= 1 x;w; - 8i ). Training of the ANN is done by adapting the weight values for each unit via a gradient search. Given a set of input-target pairs, the ANN learns the functional relationship between the input and the target. A serious drawback of the neural network approach is the difficulty to determine why a particular conclusion was reached. This is due to the inherit 'black box' nature of the neural network approach. Neural networks rely on 'raw' training data to learn the relationships between the initial inputs and target outputs. Knowledge is encoded in a set of numeric weights and biases. Although this data driven aspect of neural network allows easy adjustments when change of environment or events occur, it is difficult to interpret numeric weights, making it difficult for humans to understand. Concepts represent by symbolic learning algorithms are intuitive and therefore easily understood by humans [Wnek 1994). One approach to understanding the representations formed by neural networks is to extract such symbolic rules from networks. Over the last few years, a number of rule extraction methods have been reported (Craven 1993, Fu 1994). There are some general assumptions that these algorithms adhere to. The first assumption that most rule extraction algorithms make, is that non-input units are either maximally active (activation near 1) or inactive (activation near 0). This Boolean valued activation is approximated by using the standard logistic activation function /(z) = 1/( 1 + e-•z ) and setting s 5.0. The use of the above function parameters guarantees that non-input units always have non-negative activations in the range [0,1). The second underlying premise of rule extraction is that each hidden and output unit implements a symbolic rule. The concept associated with each unit is the consequent of the rule, and certain subsets of the input units represent the antecedent of the rule. Rule extraction algorithms search for those combinations of input values to a particular hidden or output unit that results in it having an optimal (near-one) activation. Here, rule extraction methods exploit a very basic principle of biological neural networks. That is, if the sum of its weighted inputs exceeds a certain threshold, then the biological neuron fires [Fu 1994). This condition is satisfied when the sum of the weighted inputs exceeds the bias, where (E'Jiz,=::l w; > 9i)• It has been shown that most concepts described by humans usally can be expressed as production rules in disjunctive normal form (DNF) notation. Rules expressed in this notation are therefore highly comprehensible and intuitive. In addition, the number of production rules may be reduced and the structure thereof simplified by using propositional logic. A method that extracts production rules in DNF is presented [Viktor 1995). The basic idea of the method is the use of equivalence classes. Similarly weighted links are grouped into a cluster, the assumption being that individual weights do not have unique importance. Clustering considerably reduces the combinatorics of the method as opposed to previously reported approaches. Since the rules are in a logically manipulatable form, significant simplifications in the structure thereof can be obtained, yielding a highly reduced and comprehensible set of rules. Experimental results have shown that the accuracy of the extracted rules compare favourably with the CN2 [Clark 1989] and C4.5 [Quinlan 1993] symbolic rule extraction methods. The extracted rules are highly comprehensible and similar to those extracted by traditional symfiolic methods

Brains swinging in concert: cortical phase synchronization while playing guitar

<p>Abstract</p> <p>Background</p> <p>Brains interact with the world through actions that are implemented by sensory and motor processes. A substantial part of these interactions consists in synchronized goal-directed actions involving two or more individuals. Hyperscanning techniques for assessing fMRI simultaneously from two individuals have been developed. However, EEG recordings that permit the assessment of synchronized neuronal activities at much higher levels of temporal resolution have not yet been simultaneously assessed in multiple individuals and analyzed in the time-frequency domain. In this study, we simultaneously recorded EEG from the brains of each of eight pairs of guitarists playing a short melody together to explore the extent and the functional significance of synchronized cortical activity in the course of interpersonally coordinated actions.</p> <p>Results</p> <p>By applying synchronization algorithms to intra- and interbrain analyses, we found that phase synchronization both within and between brains increased significantly during the periods of (i) preparatory metronome tempo setting and (ii) coordinated play onset. Phase alignment extracted from within-brain dynamics was related to behavioral play onset asynchrony between guitarists.</p> <p>Conclusion</p> <p>Our findings show that interpersonally coordinated actions are preceded and accompanied by between-brain oscillatory couplings. Presumably, these couplings reflect similarities in the temporal properties of the individuals' percepts and actions. Whether between-brain oscillatory couplings play a causal role in initiating and maintaining interpersonal action coordination needs to be clarified by further research.</p

AIB1 gene amplification and the instability of polyQ encoding sequence in breast cancer cell lines

BACKGROUND: The poly Q polymorphism in AIB1 (amplified in breast cancer) gene is usually assessed by fragment length analysis which does not reveal the actual sequence variation. The purpose of this study is to investigate the sequence variation of poly Q encoding region in breast cancer cell lines at single molecule level, and to determine if the sequence variation is related to AIB1 gene amplification. METHODS: The polymorphic poly Q encoding region of AIB1 gene was investigated at the single molecule level by PCR cloning/sequencing. The amplification of AIB1 gene in various breast cancer cell lines were studied by real-time quantitative PCR. RESULTS: Significant amplifications (5–23 folds) of AIB1 gene were found in 2 out of 9 (22%) ER positive cell lines (in BT-474 and MCF-7 but not in BT-20, ZR-75-1, T47D, BT483, MDA-MB-361, MDA-MB-468 and MDA-MB-330). The AIB1 gene was not amplified in any of the ER negative cell lines. Different passages of MCF-7 cell lines and their derivatives maintained the feature of AIB1 amplification. When the cells were selected for hormone independence (LCC1) and resistance to 4-hydroxy tamoxifen (4-OH TAM) (LCC2 and R27), ICI 182,780 (LCC9) or 4-OH TAM, KEO and LY 117018 (LY-2), AIB1 copy number decreased but still remained highly amplified. Sequencing analysis of poly Q encoding region of AIB1 gene did not reveal specific patterns that could be correlated with AIB1 gene amplification. However, about 72% of the breast cancer cell lines had at least one under represented (<20%) extra poly Q encoding sequence patterns that were derived from the original allele, presumably due to somatic instability. Although all MCF-7 cells and their variants had the same predominant poly Q encoding sequence pattern of (CAG)(3)CAA(CAG)(9)(CAACAG)(3)(CAACAGCAG)(2)CAA of the original cell line, a number of altered poly Q encoding sequences were found in the derivatives of MCF-7 cell lines. CONCLUSION: These data suggest that poly Q encoding region of AIB1 gene is somatic unstable in breast cancer cell lines. The instability and the sequence characteristics, however, do not appear to be associated with the level of the gene amplification

Guidelines for diagnosis and management of the cobalamin-related remethylation disorders cblC, cblD, cblE, cblF, cblG, cblJ and MTHFR deficiency

BACKGROUND: Remethylation defects are rare inherited disorders in which impaired remethylation of homocysteine to methionine leads to accumulation of homocysteine and perturbation of numerous methylation reactions. OBJECTIVE: To summarise clinical and biochemical characteristics of these severe disorders and to provide guidelines on diagnosis and management. DATA SOURCES: Review, evaluation and discussion of the medical literature (Medline, Cochrane databases) by a panel of experts on these rare diseases following the GRADE approach. KEY RECOMMENDATIONS: We strongly recommend measuring plasma total homocysteine in any patient presenting with the combination of neurological and/or visual and/or haematological symptoms, subacute spinal cord degeneration, atypical haemolytic uraemic syndrome or unexplained vascular thrombosis. We strongly recommend to initiate treatment with parenteral hydroxocobalamin without delay in any suspected remethylation disorder; it significantly improves survival and incidence of severe complications. We strongly recommend betaine treatment in individuals with MTHFR deficiency; it improves the outcome and prevents disease when given early

Monitoring quality and coverage of harm reduction services for people who use drugs: a consensus study.

BACKGROUND AND AIMS: Despite advances in our knowledge of effective services for people who use drugs over the last decades globally, coverage remains poor in most countries, while quality is often unknown. This paper aims to discuss the historical development of successful epidemiological indicators and to present a framework for extending them with additional indicators of coverage and quality of harm reduction services, for monitoring and evaluation at international, national or subnational levels. The ultimate aim is to improve these services in order to reduce health and social problems among people who use drugs, such as human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infection, crime and legal problems, overdose (death) and other morbidity and mortality. METHODS AND RESULTS: The framework was developed collaboratively using consensus methods involving nominal group meetings, review of existing quality standards, repeated email commenting rounds and qualitative analysis of opinions/experiences from a broad range of professionals/experts, including members of civil society and organisations representing people who use drugs. Twelve priority candidate indicators are proposed for opioid agonist therapy (OAT), needle and syringe programmes (NSP) and generic cross-cutting aspects of harm reduction (and potentially other drug) services. Under the specific OAT indicators, priority indicators included 'coverage', 'waiting list time', 'dosage' and 'availability in prisons'. For the specific NSP indicators, the priority indicators included 'coverage', 'number of needles/syringes distributed/collected', 'provision of other drug use paraphernalia' and 'availability in prisons'. Among the generic or cross-cutting indicators the priority indicators were 'infectious diseases counselling and care', 'take away naloxone', 'information on safe use/sex' and 'condoms'. We discuss conditions for the successful development of the suggested indicators and constraints (e.g. funding, ideology). We propose conducting a pilot study to test the feasibility and applicability of the proposed indicators before their scaling up and routine implementation, to evaluate their effectiveness in comparing service coverage and quality across countries. CONCLUSIONS: The establishment of an improved set of validated and internationally agreed upon best practice indicators for monitoring harm reduction service will provide a structural basis for public health and epidemiological studies and support evidence and human rights-based health policies, services and interventions

Extending local recovery techniques for distributed databases

The most frequently used local database system failure recovery techniques are logging, shadowing and differential files. In a distributed database these, local system failure recovery techniques may be utilized for recovery from a single site failure. However, these techniques need to be extended to facilitate continued distributed executions. Various extended local system failure recovery techniques are presented. The results of a comparison of these techniques are shown. It is concluded that the deferred data item logging technique proves to be the best for the system under consideration

A framework for executing multiple computational intelligent programs

Computational intelligent programs are capable of discovering interesting relationships contained in "raw" data. These programs, including artificial neural networks, set covering algorithms and decision trees, have been successfully used to address a number of real-world problems in, among others, the retail, medical, financial and educational fields. A computationally intelligent program can be very effective and useful, given that the learning problems are sufficiently narrowly defined and the data set contains a distribution of attributes favoured by the program. Many complex real-world problems, however, pose learning problems which cannot effectively be solved by a single program. These problems may be successfully addressed by using a combination of computational intelligent programs. A framework, which combines computational intelligent programs into a computational network, is presented. Employing more than one program potentially leads to more powerful and versatile results