Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RKHS). Two Gini distance based dependence measures are explored: Gini distance covariance and Gini distance correlation. Unlike Pearson covariance and correlation, which do not characterize independence, the above Gini distance based measures define dependence as well as independence of random variables. The test statistics are simple to calculate and do not require probability density estimation. Uniform convergence bounds and asymptotic bounds are derived for the test statistics. Comparisons with distance covariance statistics are provided. It is shown that Gini distance statistics converge faster than distance covariance statistics in the uniform convergence bounds, hence tighter upper bounds on both Type I and Type II errors. Moreover, the probability of Gini distance covariance statistic under-performing the distance covariance statistic in Type II error decreases to 0 exponentially with the increase of the sample size. Extensive experimental results are presented to demonstrate the performance of the proposed method.

Time plays an essential role in the diffusion of information, influence and disease over networks. Usually we are only able to collect cascade data in which an infection (receiving) time of each node is recorded but without any transmission information over the network. In this paper, we infer the transmission rates among nodes by Pareto distributions. Pareto modeling has several advantages. It is naturally motivated and has a nice interpretation. The scale parameter of a Pareto distribution naturally fits into the starting time of a transition, i.e., the infection time of a parent node in the cascade data is the starting point for a transition from the parent to its receiver. The shape parameter (alpha) serves as the transition rate. The larger the alpha is, the faster the transition is and there is a higher probability for disease or information to spread in a short time period. Pareto modeling is mathematically simple and computationally easy. It has explicit solutions for the optimization problem that maximizes time-dependent pairwise transmission likelihoods between all pairs of nodes. We present three modelings with a common transmission rate, with different transmission rates and with different infection rates. Experiments on real and synthetic data show that our models accurately estimate the transmission rates and perform better than the existing method.

Molecularly targeted therapies significantly contribute to the efforts of personalized approaches for cancer diagnosis and chemotherapeutic treatment. One of a critical step to identify target molecules is to determine the most representative features for different patient's sub-groups. Breast cancer, one of the most heterogeneous cancer has five main subtypes, so accurately identify gene signatures associated with intrinsic subtypes based on currently available data resource will benefit for precision medicine. Traditional ways to identify subtype-specific targeted molecular biomarkers are based on the platforms such as microarray, or immunohistochemistry (IHC) markers. Very few studies using RNAseq data to predict cancer subtype, due to the limited data resource. Gene expression in RNAseq platform is highly correlated with the microarray. However, it shows superior benefits than microarray in many ways, such as detection of low abundance transcripts, the broader dynamic range of gene expression in both coding and non-coding genes. Therefore, RNAseq platform will become the predominant tool for transcriptome analysis in the long term. To benefit the identification of molecule targets for transcriptomic data including RNAseq and microarray expression, we developed a new feature selection method. Our results of classification accuracy for intrinsic subtypes and prognostic evaluation of selected genes outperform than the traditional PAM50 gene signature, which is derived from microarray and widely used in clinical practice.

With rapid development, wireless sensor networks (WSNs) have been focused on improving the performance consist of energy efficiency, communication effectiveness, and system throughput. Many novel mechanisms have been implemented by adapting the social behaviors of natural creatures, such as bats, birds, ants, fish and honeybees. These systems are known as nature inspired systems or swarm intelligence in in order to provide optimization strategies, handle large-scale networks and avoid resource constraints. Spider monkey optimization (SMO) is a recent addition to the family of swarm intelligence algorithms by structuring the social foraging behavior of spider monkeys. In this paper, we aim to study the mechanism of SMO in the field of WSNs, formulating the mathematical model of the behavior patterns which cluster-based Spider Monkey Optimization (SMO-C) approach is adapted. In addition, our proposed methodology based on the Spider Monkey's behavioral structure aims to improve the traditional routing protocols in term of low-energy consumption and system quality of the network.

Cancer is a disease characterized largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the state of art of microarray technologies and clinical studies, a large numbers of candidate genes are extracted. Extracting informative genes out of them is essential. In our project we aim to find the cancer driver genes using somatic mutation data and protein protein interaction data. We developed a generative mixture model coupled with Bayesian parameter estimation to estimate background mutation rates and driver probabilities of each gene as well as the proportion of drivers among all sequenced genes. We choose suitable prior distributions for modelling both driver probabilities and background mutations of each gene. We apply our method to ovarian cancer data and numerically estimated the solution. Upon convergence, we are able to discover and identify some new candidate cancer driver genes.

Cancer is a disease driven largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the widespread use of microarray experiments and clinical studies, a large numbers of candidate cancer genes are produced and extracting informative genes out of them is essential. In our project we aim to find the informative genes for cancer by using mutation data from ovarian cancers. In our model we utilized the patient gene mutation profile, gene expression data and gene gene interactions network to build a graphical representation of genes and patients and construct Markov processes for mutation and patients separately. After this process, we can prioritize cancers genes automatically by looking into their scores at their stationary distributions. Comprehensive experiments show that the utilization of heterogeneous sources of information is very helpful in finding important cancer genes.

Regression is widely utilized in a variety of biological problems involving continuous outcomes. There are a number of methods for building regression models ranging from linear models to more complex nonlinear ones. While linear regression techniques can identify linear correlations between input and output, in many practical applications, the relations are nonlinear. These relations can be modeled by nonlinear regression techniques effectively. However, many models built with nonlinear techniques have limited interpretation, which is crucial in many biological problems. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features, and hence is able to provide a simple interpretation. We tested the approach on a seacoast chemical sensors dataset, a Stockori flowering time dataset, and three datasets from the UCI repository. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of conventional random forests regression. It demonstrates high potential in terms of prediction performance and interpretation ease on studying nonlinear relationships of the subjects.

BACKGROUND: In drug discovery and development, it is crucial to determine which conformers (instances) of a given molecule are responsible for its observed biological activity and at the same time to recognize the most representative subset of features (molecular descriptors). Due to experimental difficulty in obtaining the bioactive conformers, computational approaches such as machine learning techniques are much needed. Multiple Instance Learning (MIL) is a machine learning method capable of tackling this type of problem. In the MIL framework, each instance is represented as a feature vector, which usually resides in a high-dimensional feature space. The high dimensionality may provide significant information for learning tasks, but at the same time it may also include a large number of irrelevant or redundant features that might negatively affect learning performance. Reducing the dimensionality of data will hence facilitate the classification task and improve the interpretability of the model. RESULTS: In this work we propose a novel approach, named multiple instance learning via joint instance and feature selection. The iterative joint instance and feature selection is achieved using an instance-based feature mapping and 1-norm regularized optimization. The proposed approach was tested on four biological activity datasets. CONCLUSIONS: The empirical results demonstrate that the selected instances (prototype conformers) and features (pharmacophore fingerprints) have competitive discriminative power and the convergence of the selection process is also fast.

BACKGROUND: In the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule. METHODS: We encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features. RESULTS: The predictive abilities of the proposed approach were compared with three classical predictive models without instance-based embedding. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations. To validate the ability of the proposed approach to find bioactive conformers, 12 small molecules with co-crystallized structures were seeded in one data set. 10 out of 12 co-crystallized structures were indeed identified as significant conformers using the proposed approach. CONCLUSIONS: The proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers.

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Joint Rule Extraction and Feature Selection (JRF), based on 1-norm regularized random forests. JRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied JRF to several drug activity prediction and microarray data sets. JRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

BACKGROUND: It is commonly believed that including domain knowledge in a prediction model is desirable. However, representing and incorporating domain information in the learning process is, in general, a challenging problem. In this research, we consider domain information encoded by discrete or categorical attributes. A discrete or categorical attribute provides a natural partition of the problem domain, and hence divides the original problem into several non-overlapping sub-problems. In this sense, the domain information is useful if the partition simplifies the learning task. The goal of this research is to develop an algorithm to identify discrete or categorical attributes that maximally simplify the learning task. RESULTS: We consider restructuring a supervised learning problem via a partition of the problem space using a discrete or categorical attribute. A naive approach exhaustively searches all the possible restructured problems. It is computationally prohibitive when the number of discrete or categorical attributes is large. We propose a metric to rank attributes according to their potential to reduce the uncertainty of a classification task. It is quantified as a conditional entropy achieved using a set of optimal classifiers, each of which is built for a sub-problem defined by the attribute under consideration. To avoid high computational cost, we approximate the solution by the expected minimum conditional entropy with respect to random projections. This approach is tested on three artificial data sets, three cheminformatics data sets, and two leukemia gene expression data sets. Empirical results demonstrate that our method is capable of selecting a proper discrete or categorical attribute to simplify the problem, i.e., the performance of the classifier built for the restructured problem always beats that of the original problem. CONCLUSIONS: The proposed conditional entropy based metric is effective in identifying good partitions of a classification problem, hence enhancing the prediction performance.

Explosive compounds such as TNT and RDX have various toxicological effects on the natural environment. The goal of the earthworm microarray experiment is to unearth the biomarker for toxicity evaluation. We propose a novel recursive gene selection method which can handle the multi-class setting effectively and efficiently. The selection is performed iteratively. In each iteration, a linear multi-class classifier is trained using 1-norm regularization, which leads to sparse weight vectors, i.e., many feature weights are exactly zero. Those zero-weight features are eliminated in the next iteration. The empirical results demonstrate that the selected features (genes) have very competitive discriminative power. In addition, the selection process has fast rate of convergence.

BACKGROUND: Microarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes in a single experiment. However, the large number of genes greatly increases the challenges of analyzing, comprehending and interpreting the resulting mass of data. Selecting a subset of important genes is inevitable to address the challenge. Gene selection has been investigated extensively over the last decade. Most selection procedures, however, are not sufficient for accurate inference of underlying biology, because biological significance does not necessarily have to be statistically significant. Additional biological knowledge needs to be integrated into the gene selection procedure. RESULTS: We propose a general framework for gene ranking. We construct a bipartite graph from the Gene Ontology (GO) and gene expression data. The graph describes the relationship between genes and their associated molecular functions. Under a species condition, edge weights of the graph are assigned to be gene expression level. Such a graph provides a mathematical means to represent both species-independent and species-dependent biological information. We also develop a new ranking algorithm to analyze the weighted graph via a kernelized spatial depth (KSD) approach. Consequently, the importance of gene and molecular function can be simultaneously ranked by a real-valued measure, KSD, which incorporates the global and local structure of the graph. Over-expressed and under-regulated genes also can be separately ranked. CONCLUSION: The gene-function bigraph integrates molecular function annotations into gene expression data. The relevance of genes is described in the graph (through a common function). The proposed method provides an exploratory framework for gene data analysis.

MCBIOS 2009 was held February 20–21, 2009 at the Hunter Henry Center in Starkville, Mississippi on the Mississippi State University campus. The conference was hosted by the four research universities in Mississippi comprising the Mississippi Computational Biology Consortium – Jackson State University (JSU), Mississippi State University (MSU), the University of Mississippi (UM), and the University of Southern Mississippi (USM). Dr. Dawn Wilkins of UM and Dr. Susan Bridges of MSU served as conference co-chairs. Keynote speakers were Dr. Laura Elnitski, Head of the Genomic Functional Analysis Section at the National Human Genome Research Institute (NHGRI), Howard Cash, President and CEO of Gene Codes Corporation, and Dr. Cathy Wu, Director of the Protein Information Resource (PIR).

MCBIOS 2008 was held February 23–24, 2008 in Oklahoma City, Oklahoma at the Cox Convention Center in Bricktown. It was the best attended in the series of MCBIOS conferences (140 registrants) with the most participation (68 posters submitted). Informative and engaging keynote talks were delivered by Dr. Bruce Roe and Dr. Edward Dougherty. The full agenda is online at http://www.okbios.org .

BACKGROUND: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor center representative for high dimensional data. We need a new algorithm that is robust and works well in high dimensional data sets e.g. gene expression data. RESULTS: Here we propose a new robust divisive clustering algorithm, the bisecting k-spatialMedian, based on the statistical spatial depth. A new subcluster selection rule, Relative Average Depth, is also introduced. We demonstrate that the proposed clustering algorithm outperforms the componentwise-median-based bisecting k-median algorithm for high dimension and low sample size (HDLSS) data via applications of the algorithms on two real HDLSS gene expression data sets. When further applied on noisy real data sets, the proposed algorithm compares favorably in terms of robustness with the componentwise-median-based bisecting k-median algorithm. CONCLUSION: Statistical data depths provide an alternative way to find the "center" of multivariate data sets and are useful and robust for clustering.

The Fourth Annual MidSouth Computational Biology and Bioinformatics Society (MCBIOS-IV) conference was held in New Orleans, Louisiana on February 1 st –3 rd , 2007. The conference venue included two locations: the Lindy Boggs Conference facility at the University of New Orleans (UNO); and Dinner/Speaker venues in the French Quarter – Broussard's of New Orleans and The House of Blues. The conference featured three days of technical presentations, with the third day partly devoted to a satellite Conference on Nanopore Cheminformatics with an on-site demo presentation of a nanopore detector (from the Research Institute for Children and UNO Nanopore Cheminformatics/Biophysics Labs). The theme of this year's conference was "Computational Frontiers in Biomedicine".

BACKGROUND: Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. The effectiveness of the RFE algorithm is generally considered excellent, but the primary obstacle in using it is the amount of computational power required. RESULTS: Here we introduce a variant of RFE which employs ideas from simulated annealing. The goal of the algorithm is to improve the computational performance of recursive feature elimination by eliminating chunks of features at a time with as little effect on the quality of the reduced feature set as possible. The algorithm has been tested on several large gene expression data sets. The RFE algorithm is implemented using a Support Vector Machine to assist in identifying the least useful gene(s) to eliminate. CONCLUSION: The algorithm is simple and efficient and generates a set of attributes that is very similar to the set produced by RFE.

In 1999, most computer science students participate in at least one group project in some class prior to graduation. However, assessing individual student contributions to a group project is a difficult task faced by instructors of these classes. In this paper, we have compiled a wide range of assessment instruments, and identified situations where they can be effective. This paper is a compilation of potential evaluation strategies. No comparison is made among the many strategies nor are particular techniques ranked above or below others. The goal is simply to provide a wide range of potential team evaluation techniques. Since each technique evaluates a particular characteristic and different team project courses have different goals it is up to the instructor to choose the techniques that best evaluate the individual in light of the course goals.

One of the difficult tasks in the court system is the scheduling of the entities involved at the various stages of the criminal justice system.These include judges, jurors, witnesses, defendants, attorneys and court rooms.In this paper we examine the feasibility of using machine learning techniques for the task of predicting the elapsed time between the arrest of an offender and the final disposition of his or her case.Accurate prediction of time to case disposition will aid in the resolution of conflicts that arise in the scheduling of the above entities.Using a pre-existing dataset called Offender Based Transaction Statistics (1990) and two well-known learning algorithms we show that there is scope for the use of such techniques.

We investigate the query complexity of exact learning in the membership and (proper) equivalence query model. We give a complete characterization of concept classes that are learnable with a polynomial number of polynomial sized queries in this model. We give applications of this characterization, including results on learning a natural subclass of DNF formulas, and on learning with membership queries alone. Query complexity has previously been used to prove lower bounds on the time complexity of exact learning. We show a new relationship between query complexity and time complexity in exact learning: If any “honest” class is exactly and properly learnable with polynomial query complexity, but not learnable in polynomial time, then P = NP. In particular, we show that an honest class is exactly polynomial-query learnable if and only if it is learnable using an oracle for Γ p 4 .

We investigate the query complexity of exact learning in the membership and (proper) equivalence query model.We give '

We show that the class of p-branching programs can be exactly learned in ()(rz5) time us:n$ only O(n) equivalence queries and O(n ) membership queries, but neither type of query alone is sufficient for polynomial time learning.