The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.

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The functional connectome derived from BOLD resting-state functional magnetic resonance imaging data represents meaningful functional organizations and a shift between distinct cognitive states. However, the body of knowledge on how the long-term career experience affects the brain’s functional plasticity is still very limited. In this study, we used a dynamic functional connectome characterization (DBFCC) model with the automatic target generation process K-Means clustering to explore the functional reorganization property of resting brain states, driven by long-term career experience. Taking sailors as an example, DBFCC generated seventeen reproducibly common atomic connectome patterns (ACP) and one reproducibly distinct ACP, i.e., ACP14. The common ACPs indicating the same functional topology of the resting brain state transitions were shared by two control groups, while the distinct ACP, which mainly represented functional plasticity and only existed in the sailors, showed close relationships with the long-term career experience of sailors. More specifically, the distinct ACP14 of the sailors was made up of four specific sub-networks, such as the auditory network, visual network, executive control network, and vestibular function-related network, which were most likely linked to sailing experience, i.e., continuously suffering auditory noise, maintaining balance, locating one’s position in three-dimensional space at sea, obeying orders, etc. Our results demonstrated DBFCC’s effectiveness in revealing the specifically functional alterations modulated by sailing experience and particularly provided the evidence that functional plasticity was beneficial in reorganizing brain’s functional topology, which could be driven by career experience.

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Compared with independent component analysis (ICA), constrained ICA (CICA) has unique advantages in functional magnetic resonance image (fMRI) data analysis by introducing some priori information into the estimation process. However, there are still some controversies in the current CICA methods, such as how to choose the threshold parameter to restrain the similarity, and how to reduce the accuracy requirements for a priori information. In this paper, we propose a new constrained spatiotemporal ICA (CSTICA) method based on the framework of multi-objective optimization, where the inequality constraint of the traditional CICA method is transformed into the objective optimization function of the CSTICA, and both temporal and spatial a priori information are included simultaneously. The simulated, hybrid, and real fMRI data experiments are designed to evaluate the performance of the proposed CSTICA method in comparison with the classical ICA and CICA methods. Compared with the traditional CICA methods, the CSTICA has circumvented the problem of threshold parameter selection. Furthermore, the experimental results demonstrate that the source recovery ability of the CSTICA has been improved to a certain extent especially in the cases of a priori information with low accuracies. Meanwhile, the results also indicate that the CSTICA reduces dependency on the accuracy of a priori information.

Extracting massive features from images to quantify tumors provides a new insight to solve the problem that tumor heterogeneity is difficult to assess quantitatively. However, quantification of tumors by single-mode methods often has defects such as difficulty in features extraction and high computational complexity. The multimodal approach has shown effective application prospects in solving these problems. In this paper, we propose a feature fusion method based on positron emission tomography (PET) images and clinical information, which is used to obtain features for lung metastasis prediction of soft tissue sarcomas (STSs). Random forest method was adopted to select effective features by eliminating irrelevant or redundant features, and then they were used for the prediction of the lung metastasis combined with back propagation (BP) neural network. The results show that the prediction ability of the proposed model using fusion features is better than that of the model using an image or clinical feature alone. Furthermore, a good performance can be obtained using 3 standard uptake value (SUV) features of PET image and 7 clinical features, and its average accuracy, sensitivity, and specificity on all the sets can reach 92%, 91%, and 92%, respectively. Therefore, the fusing features have the potential to predict lung metastasis for STSs.