Correction to: Realizing drug repositioning by adapting a recommendation system to handle the process.

Following publication of the original article [1], the authors reported that there was an error in the spelling of the name of one of the authors.

Realizing drug repositioning by adapting a recommendation system to handle the process.

Background: Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs.

Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).

Methods And Material: Different approaches exist to model GRNs; they are mostly simulated as mathematical models that represent relationships between genes. Actually, it has been realized that biological functions at the cellular level are controlled by genes; thus, by controlling the behavior of genes, it is possible to regulate these biological functions.

Batch Mode TD($\lambda$ ) for Controlling Partially Observable Gene Regulatory Networks.

External control of gene regulatory networks (GRNs) has received much attention in recent years. The aim is to find a series of actions to apply to a gene regulation system making it avoid its diseased states. In this work, we propose a novel method for controlling partially observable GRNs combining batch mode reinforcement learning (Batch RL) and TD() algorithms.

Effective gene expression data generation framework based on multi-model approach.

Objective: Overcome the lack of enough samples in gene expression data sets having thousands of genes but a small number of samples challenging the computational methods using them.

Methods And Material: This paper introduces a multi-model artificial gene expression data generation framework where different gene regulatory network (GRN) models contribute to the final set of samples based on the characteristics of their underlying paradigms. In the first stage, we build different GRN models, and sample data from each of them separately.

MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives.

The need for determining a path from an initial location to a target one is a crucial task in many applications, such as virtual simulations, robotics, and computer games. Almost all of the existing algorithms are designed to find optimal or suboptimal solutions considering only a single objective, namely path length. However, in many real life application path length is not the sole criteria for optimization, there are more than one criteria to be optimized that cannot be transformed to each other.

Toward Generalization of Automated Temporal Abstraction to Partially Observable Reinforcement Learning.

Temporal abstraction for reinforcement learning (RL) aims to decrease learning time by making use of repeated sub-policy patterns in the learning task. Automatic extraction of abstractions during RL process is difficult but has many challenges such as dealing with the curse of dimensionality. Various studies have explored the subject under the assumption that the problem domain is fully observable by the learning agent.

Subtree selection in kernels for graph classification.

Classification of structured data is essential for a wide range of problems in bioinformatics and cheminformatics. One such problem is in silico prediction of small molecule properties such as toxicity, mutagenicity and activity. In this paper, we propose a new feature selection method for graph kernels that uses the subtrees of graphs as their feature sets.

Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data.

The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques.

Influence of prior knowledge in constraint-based learning of gene regulatory networks.

Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks.

Scalable approach for effective control of gene regulatory networks.

Objective: Interactions between genes are realized as gene regulatory networks (GRNs). The control of such networks is essential for investigating issues like different diseases. Control is the process of studying the states and behavior of a given system under different conditions.

Automated large-scale control of gene regulatory networks.

Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models.

Positive impact of state similarity on reinforcement learning performance.

In this paper, we propose a novel approach to identify states with similar subpolicies and show how they can be integrated into the reinforcement learning framework to improve learning performance. The method utilizes a specialized tree structure to identify common action sequences of states, which are derived from possible optimal policies, and defines a similarity function between two states based on the number of such sequences. Using this similarity function, updates on the action-value function of a state are reflected onto all similar states.

A powerful approach for effective finding of significantly differentially expressed genes.

The problem of identifying significantly differentially expressed genes for replicated microarray experiments is accepted as significant and has been tackled by several researchers. Patterns from Gene Expression (PaGE) and q-values are two of the well-known approaches developed to handle this problem. This paper proposes a powerful approach to handle this problem.

Finding differentially expressed genes for pattern generation.

Motivation: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation.

Results: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding.