Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.

Objective: Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to accurately classify Healthy Controls, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment, and Alzheimer's Disease individuals.

Methods: A novel mapping function based on the B distribution has been developed to map correlation matrices to robust functional connectivity.

Mutual information model for link prediction in heterogeneous complex networks.

Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path.

SFM: A novel sequence-based fusion method for disease genes identification and prioritization.

The identification of disease genes from human genome is of great importance to improve diagnosis and treatment of disease. Several machine learning methods have been introduced to identify disease genes. However, these methods mostly differ in the prior knowledge used to construct the feature vector for each instance (gene), the ways of selecting negative data (non-disease genes) where there is no investigational approach to find them and the classification methods used to make the final decision.

A novel method based on physicochemical properties of amino acids and one class classification algorithm for disease gene identification.

Identifying the genes that cause disease is one of the most challenging issues to establish the diagnosis and treatment quickly. Several interesting methods have been introduced for disease gene identification for a decade. In general, the main differences between these methods are the type of data used as a prior-knowledge, as well as machine learning (ML) methods used for identification.

A two-layer classification framework for protein fold recognition.

Protein fold recognition is one of the interesting studies in bioinformatic to predicting the tertiary structure of proteins. In this paper, an individual method and a fusion method are proposed for protein fold recognition. A Two Layer Classification Framework (TLCF) is proposed as individual method.

RNA-RNA interaction prediction using genetic algorithm.

Background: RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them.

A novel method based on new adaptive LVQ neural network for predicting protein-protein interactions from protein sequences.

Protein-Protein interaction (PPI) is one of the most important data in understanding the cellular processes. Many interesting methods have been proposed in order to predict PPIs. However, the methods which are based on the sequence of proteins as a prior knowledge are more universal.

A heuristic approach to RNA-RNA interaction prediction.

RNA-RNA interaction is used in many biological processes such as gene expression regulation. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In this regard, some algorithms have been formed to predict the structure of the interaction between two RNA molecules.