MOTIVATION: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. RESULTS: This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. AVAILABILITY AND IMPLEMENTATION: HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.

Job burnout is a special type of work-related stress that is prevalent in our modern society, and constant burnout is extremely harmful for people's physical health and emotional wellbeing. Traditional studies for burnout mainly rely on surveys/questionnaires, which have revealed several interesting findings but are of high cost and very time consuming. With the prevalence of social networking applications, we aim to re-investigate the burnout phenomenon in a novel perspective. In this paper, we collected a dataset consisting of 1532 burnout Weibo users with their postings. Based on the previous literature, we propose a number of hypotheses about what might be the burst signal of the burnout from the perspective of language, time and interaction. Furthermore, extensive correlation analysis is conducted to investigate if these hypotheses are supported, which leads to a number of interesting findings. Finally, we develop machine learning models to predict the burst of burnout based on extracted features and achieve a relatively high accuracy, which reveals potential implications in early-stage intervention.

This paper introduces a novel energy minimization method, namely iterated cross entropy with partition strategy (ICEPS), into the Markov random field theory. The solver, which is based on the theory of cross entropy, is general and stochastic. Unlike some popular optimization methods such as belief propagation (BP) and graph cuts (GC), ICEPS makes no assumption on the form of objective functions and thus can be applied to any type of Markov random field (MRF) models. Furthermore, compared with deterministic MRF solvers, it achieves higher performance of finding lower energies because of its stochastic property. We speed up the original cross entropy algorithm by partitioning the MRF site set and assure the effectiveness by iterating the algorithm. In the experiments, we apply ICEPS to two MRF models for medical image segmentation and show the aforementioned advantages of ICEPS over other popular solvers such as iterated conditional modes (ICM) and GC.

Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 x 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use.

To segment a whole object from an image is an essential and challenging task in image processing. In this paper, we propose a hybrid segmentation algorithm which combines prior shape information with normalized cut. With the help of shape information, we can utilize normalized cut to correctly segment the target whose boundary may be corrupted by noise or outliers. At the same time, we introduce the use of segmentation results of the normalized cut to guide the shape model, and thus avoid searching the shape space. The proposed method was demonstrated to be effective by our experiments on both synthetic and real data.

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions.

Efficient diagnosis of faults in VLSI circuits requires high quality diagnostic test sets. In this work novel techniques for diagnostic test generation are proposed that require significantly less time than previous methods. The set of fault pairs left undistinguished by a detection-oriented test set is first filtered to target only testable faults. Then diagnostic test generation is performed using a genetic algorithm (GA) combined with a diagnostic fault simulator. A new fitness metric is proposed for the GA that accurately measures the quality of candidate sequences while requiring a limited amount of CPU time. Experimental results illustrate the effectiveness of the approach for sequential circuits.

A new diagnostic fault simulator is described that diagnoses both feedback and nonfeedback bridge faults in combinational circuits while using information from fault simulation of single stuck-at faults. A realistic fault model is used which considers the existence of the Byzantine Generals problem. Sets representing nodes possibly involved in a defect are partitioned based on logic and fault simulation of failing vectors. The approach has been demonstrated for two-line bridge faults on several large combinational benchmark circuits containing Boolean primitives and has achieved over 98% accuracy for nonfeedback bridge faults and over 85% accuracy for feedback bridge faults with good diagnostic resolution.

A new fault list reduction approach is proposed for use in the first stage of a two-stage bridge fault diagnosis procedure. Modified structural analysis and layout extraction procedures are performed to obtain a reduced realistic bridge fault list that can be used in the second stage, which employs diagnostic fault simulation. The fault list reduction approach can reduce the final candidate bridge fault list by 92% to 99% compared with the diagnosis results achieved by the diagnostic fault simulator alone.