A neurodynamic optimization approach to supervised feature selection via fractional programming.

Feature selection is an important issue in machine learning and data mining. Most existing feature selection methods are greedy in nature thus are prone to sub-optimality. Though some global feature selection methods based on unsupervised redundancy minimization can potentiate clustering performance improvements, their efficacy for classification may be limited. In this paper, a neurodynamics-based holistic feature selection approach is proposed via feature redundancy minimization and relevance maximization. An information-theoretic similarity coefficient matrix is defined based on multi-information and entropy to measure feature redundancy with respect to class labels. Supervised feature selection is formulated as a fractional programming problem based on the similarity coefficients. A neurodynamic approach based on two one-layer recurrent neural networks is developed for solving the formulated feature selection problem. Experimental results with eight benchmark datasets are discussed to demonstrate the global convergence of the neural networks and superiority of the proposed neurodynamic approach to several existing feature selection methods in terms of classification accuracy, precision, recall, and F-measure.

Mechanisms for the reduction of caffeine consumption: What, how and why.

BACKGROUND: Anecdotal evidence suggests consumers of caffeine self-administer strategies to reduce consumption, but little is known of what these strategies are or how they are implemented. This study aimed to understand the lived experience of reducing caffeine consumption including specific techniques (what) and implementation strategies (how), harm and withdrawal symptoms (why). METHODS: We developed a classification system through an inductive and deductive approach and applied it to a large dataset derived from online sources. RESULTS: A total of 112 internet sources were identified, containing 2,682 different strategies. The classification system identified 22 categories of Behaviour Change Techniques (BCT): 10 categories were directly aligned with a BCT, one was split into two categories (substance and behavioural substitution), six represented a cluster of BCT's (e.g., withdrawal management and maintaining momentum) and four appeared to uniquely represent a consumer perspective (e.g., realisation of a problem). The most common techniques were substance substitution, seek knowledge and information, avoidance of caffeine and identify prompts for change. The most frequently perceived benefit was the stimulating effects of caffeine and a feeling of mental alertness. The most frequently cited harms were sleep problems including insomnia and concerns about dependence (or addiction) to caffeine. We found 16 categories of withdrawal symptoms. The most frequently endorsed symptom was headaches, followed by fatigue, exhaustion and low energy. CONCLUSIONS: Consumers use a wide range of techniques when attempting to reduce caffeine consumption. Treatment approaches are focused on fading, but the current study found consumers most frequently focus on substance and behavioural substitution.

A searchable map of PubChem.

The database PubChem was classified using 42 integer value descriptors of molecular structure, here called molecular quantum numbers (MQNs), which count atoms and bond types, polar groups, and topological features. Principal component analysis of the MQN data set shows that PubChem compounds occupy a partially filled elliptical cone in the (PC1,PC2,PC3) space whose axis is the first principal component PC1 (65% variability) representing molecular size, and the ellipse axes are PC2 (18% variability, representing structural flexibility) and PC3 (7% variability, representing polarity). A visual overview of PubChem is provided by color-coded representations of the (PC2,PC3) plane. The MQNs form a scalar fingerprint which can be used to measure the similarity between pairs of molecules and enable ligand-based virtual screening, as illustrated for the enrichment of bioactives from the DUD data set from PubChem. An MQN-annotated version of PubChem with an MQN-similarity search tool is available at www.gdb.unibe.ch .

Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques.

In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.