Autism spectrum disorder (ASD) is a neuro dysfunction which causes the repetitive behavior and social instability of patients. Diagnosing ASD has been of great interest. However, due to the lack of discriminate differences between neuroimages of healthy persons and ASD patients, there has been no powerful diagnosis approach. In this study, we have designed brain network-based features for the diagnosis of ASD. Specifically, we have used the 264 regions based parcellation scheme to construct a brain network from a brain functional magnetic resonance imaging (fMRI). Then we have defined 264 raw brain features by the 264 eigenvalues of the Laplacian matrix of the brain network and another three features by network centralities. By applying a feature selection algorithm, we have obtained 64 discriminate features. Furthermore, we have trained several machine learning models for diagnosing ASD with our obtained features on ABIDE (Autism Brain Imaging Data Exchange) dataset. With our derived features, the linear discriminant analysis has achieved the classification accuracy of 77.7%, which is better than the state-ofthe-art results.

Complex networks are ubiquitous in nature. In biological systems, biomolecules interact with each other to form so-called biomolecular networks, which determine the cellular behaviors of living organisms. Controlling the cellular behaviors by regulating certain biomolecules in the network is one of the most concerned problems in systems biology. Recently, the connections between biological networks and structural control theory have been explored, uncovering some interesting biological phenomena. Some researchers have paid attentions to the structural controllability of networks in notion of the minimum steering sets (MSSs). However, because the MSSs for complex networks are not unique and the importance of different MSSs is diverse in real applications, MSSs with certain meanings should be studied. In this paper, we investigated the MSSs of biomolecular networks by considering the drug binding information. The biomolecules in the MSSs with binding preference are enriched with known drug targets and are likely to have more chemical-binding opportunities with existing drugs compared with randomly chosen MSSs, suggesting novel applications for drug target identification and drug repositioning.

BackgroundAdvanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.MethodsIn this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.ResultsThe computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.ConclusionIt is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

The brain has the most complex structures and functions in living organisms, and brain networks can provide us an effective way for brain function analysis and brain disease detection. In brain networks, there exist some important neural unit modules, which contain many meaningful biological insights. It is appealing to find the neural unit modules and obtain their affiliations. In this study, we present a novel method by integrating the uniform design into the particle swarm optimization to find community modules of brain networks, abbreviated as UPSO. The difference between UPSO and the existing ones lies in that UPSO is presented first for detecting community modules. Several brain networks generated from functional MRI for studying autism are used to verify the proposed algorithm. Experimental results obtained on these brain networks demonstrate that UPSO can find community modules efficiently and outperforms the other competing methods in terms of modularity and conductance. Additionally, the comparison of UPSO and PSO also shows that the uniform design plays an important role in improving the performance of UPSO.

By the means of the density functional theory and time-dependent density functional theory, the radiative and non-radiative decay processes of a series of iridium(III) complexes are investigated to explore the...