Eisa Alanazi

Prediction of novel drug indications using network driven biological data prioritization and integration.

BackgroundWith the rapid development of high-throughput genomic technologies and the accumulation of genome-wide datasets for gene expression profiling and biological networks, the impact of diseases and drugs on gene expression can be comprehensively characterized. Drug repositioning offers the possibility of reduced risks in the drug discovery process, thus it is an essential step in drug development.ResultsComputational prediction of drug-disease interactions using gene expression profiling datasets and biological networks is a new direction in drug repositioning that has gained increasing interest. We developed a computational framework to build disease-drug networks using drug- and disease-specific subnetworks. The framework incorporates protein networks to refine drug and disease associated genes and prioritize genes in disease and drug specific networks. For each drug and disease we built multiple networks using gene expression profiling and text mining. Finally a logistic regression model was used to build functional associations between drugs and diseases.ConclusionsWe found that representing drugs and diseases by genes with high centrality degree in gene networks is the most promising representation of drug or disease subnetworks.

A Preference-Aware Interactive System for Online Shopping

Preference Elicitation is very important for online shopping interactive applications. The potential buyers usually have interest in some of the attributes of the product they want to purchase. While the current online shopping systems allow the users to provide some keywords and other information in order to ?lter and get only what they need, these latter feel that what they get does not necessarily meet their satisfaction. In this paper, we propose a new shopping system that enables the customers to express their needs when buying a product online. More precisely, the users are given the ability to provide their requirements and desires in a friendly and interactive way. The system will then provide a list of suggestions meeting the users’ requirements and maximizing their desires. Requirements and desires are managed, in a unique model, respectively as a set of hard constraints and preferences where these latter can be quantitative (numerical), qualitative (ordinal) or both. These constraints and preferences represent a constraint optimization problem where optimal solutions (best outcomes) are those satisfying the hard constraints and maximizing the user’s preferences. The branch and bound method is applied in order to provide the user with a list of best outcomes.

Data Mining Techniques and Preference Learning in Recommender Systems

The importance of implementing recommender systems has significantly increased during the last decade. The majority of available recommender systems do not offer clients the ability to make selections based on their choices or desires. This has motivated the development of a web based recommender system in order to recommend products to users and customers. The new system is an extension of an online application previously developed for online shopping under constraints and preferences. In this work, the system is enhanced by introducing a learning component to learn user preferences and suggests products based on them. More precisely, the new component learns from other customers’ preferences and makes a set of recommendations using data mining techiques including classification, association rules and cluster analysis techniques. The results of experimental tests, conducted to evaluate the performance of this component when compiling a list of recommendations, are very promising.

A Three-way clustering approach for handling missing data using GTRS

Abstract Clustering is an important data analysis task. It becomes a challenge in the presence of uncertainty due to reasons such as incomplete, missing or corrupted data. A three-way approach has recently been introduced to deal with uncertainty in clustering due to missing values. The essential idea is to make a deferment decision whenever it is not clear and possible to decide whether or not to include an object in a cluster. A key issue in the three-way approach is to determine the thresholds that are used to define the three types of decisions, namely, include an object in a cluster, exclude an object from a cluster, or delay (defer) the decision of inclusion or exclusion from a cluster. The existing studies do not sufficiently address the determination of thresholds and generally use its fix values. In this paper, we explore the use of game-theoretic rough set (GTRS) model to handle this issue. In particular, a game is defined where the determination of thresholds is approached based on a tradeoff between the properties of accuracy and generality of clusters. The determined thresholds are then used to induce three-way decisions for clustering uncertain objects. Experimental results on four datasets from UCI machine learning repository suggests that the GTRS significantly improves the generality while keeping similar levels of accuracy in comparison to other three-way and similar models.

Variable ordering and constraint propagation for constrained CP-nets

A Conditional Preferences network (CP-net) is a known graphical model for representing qualitative preferences. In many real world applications we are often required to manage both constraints and preferences in an efficient way. The goal here is to select one or more scenarios that are feasible according to the constraints while maximizing a given utility function. This problem has been modelled as a CP-net where some variables share a set of constraints. This latter framework is called a Constrained CP-net. Solving the constrained CP-net has been proposed in the past using a variant of the branch and bound algorithm called Search CP. In this paper, we experimentally study the effect of variable ordering heuristics and constraint propagation when solving a constrained CP-net using a backtrack search algorithm. More precisely, we investigate several look ahead strategies as well as the most constrained heuristic for variable ordering during search. The results of the experiments conducted on random Constrained CP-net instances generated through the RB model, clearly show a significant improvement when adopting these techniques for specific graph structures as well as the case where a large number of variables are sharing constraints.

Managing qualitative preferences with constraints

Preferences and Constraints co-exist naturally in different domains. Thus, handling both of them is of great interest for many real applications. Preferences usually expressed in qualitative format where a constraint satisfaction problem (CSP) is a well known formalism to handle constraints. In this paper, we investigate the problem of managing both qualitative user preferences and system requirements. We model our preference part as an instance of Conditional Preference networks (CP-nets) and the constraints as CSP. We propose a new method to handle both aspects in an efficient manner. Our method is based on the well-known Arc Consistency (AC) propagation technique. The experiments demonstrate that the new approach can save a substantial amount of time for finding the optimal solution for given preferences and constraints.

Variable Ordering and Constraint Propagation for Constrained CP-Nets

In many real world applications we are often required to manage constraints and preferences in an efficient way. The goal here is to select one or more scenarios that are feasible according to the constraints while maximizing a given utility function. This problem can be modeled as a constrained Conditional Preference Networks Constrained CP-Nets where preferences and constraints are represented through CP-Nets and Constraint Satisfaction Problems respectively. This problem has gained a considerable attention recently and has been tackled using backtrack search. However, there has been no study about the effect of variable ordering heuristics and constraint propagation on the performance of the backtrack search solving method. We investigate several constraint propagation strategies over the CP-Net structure while adopting the most constrained heuristic for variables ordering during search. In order to assess the effect of constraint propagation and variable ordering on the time performance of the backtrack search, we conducted an experimental study on several constrained CP-Net instances randomly generated using the RB model. The results of these experiments clearly show a significant improvement when compared to the well known methods for solving constrained CP-Nets.

Representing and reasoning with constrained PCP-nets

A Probabilistic Conditional Preference network (PCP-net) provides a compact representation of preferences characterized with uncertainty. We propose to enrich the expressive power of the PCP-net by adding constraints between some of the variables. We call this new model, the Constrained PCP-net (CPCP-net). We study the key preference reasoning task with the proposed CPCP-net which consists in finding the most probable optimal outcome i.e. the most probable outcome that best represents the preferences while satisfying all the constraints. In this regard, a variant of the Branch and Bound algorithm has been proposed and experimentally evaluated on CPCP-net instances, randomly generated based on the RB-model. The results of these experiments show that the new proposed solving method is capable of returning the most probable optimal outcome in a reasonable time.

A New System for the Dynamic Shortest Route Problem

The Shortest Route Problem concerns routing one vehicle to one customer while minimizing some objective functions. The problem is essentially a shortest path problem and has been studied extensively in the literature. We report a system with the objective to address two dynamic aspects of the Shortest Route Problem. The first aspect corresponds to handing incremental changes during the routing plan. The second one is about finding the most probable shortest path i.e. the path with the highest probability of being not congested. We describe how each of these two aspects has been implemented in the system as well as the other features and components of this latter.

Configuring the Webpage Content through Conditional Constraints and Preferences

Configuring the webpage content to reflect the user desires is highly demanded in the era of personalization. The problem can be viewed as a preference-based constraint problem including a set of components forming the webpage along with the preferences. Our goal is then to locate each of these components such that the user preferences are maximized. Additionally, constraints might exist between different components of the given page. We investigate the problem of handling the web page content based on user preferences and constraints. Unlike previous attempts, we model the constraint part as an instance of the conditional CSP. This gives further expressive power to handle different relations among components. The preferences are expressed through the well-known CP-Nets graphical model.