Accurately predicting protein complexes from protein interaction networks is a significant area of study in systems biology, as they are involved in many essential cellular processes. Gaining a more complete understanding of cellular functions will require a comprehensive map of human protein complexes. Unfortunately, we still lack a comprehensive map of experimentally identified protein complexes, thereby only a partial understanding of the composition and function of human protein complexes. To close this gap, we design a computational framework to accurate predict human protein complexes by integrating different protein interaction networks. To predict protein complexes, we design a complex score to control the generation of human complexes by using the seed diffusion strategy on a weighted protein interaction network. Compared with the existing methods, our method achieves promising performance in the human protein interaction network. Further, we also predict 303 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affected human protein complexes. These results suggest that our method is not only a computing framework on which to integrate additional new data sources, but also a valuable resource for exploring biologically meaningful complexes and helping new biological findings.

In environments where intelligent video surveillance systems (IVSSs) are deployed, particularly in review room, the detection of electronic devices constitutes a crucial task. Nevertheless, this task presents significant challenges attributed to the high rates of false positives and false negatives in electronic device detection (EDD), compounded by the low resolution of objects when viewed from multiple angles.To address these challenges, we propose a deep learning-based cascaded detection framework. Specifically, we design a mixed region sampling (MRS) method to enhance the foreground perception with background information and image details. We design a nonstrided downsampling method (ASDP) to map the attention spatial features to depth and improve the detection of low-resolution objects with fine-grained features. We enhance the model’s robustness to different viewing angles by feature perturbation during training. Moreover, we use a cascaded strategy to reduce false positives. To evaluate our method, we construct a real review room dataset (EDD) with 28,000 images from multiple angles. Our method improves the multiview generalization performance by 4.48% mAP and 5.62% mAR. On the public datasets Pascal VOC-2007 and visDrone-2019, our method is also superior to other suboptimal methods. We propose a framework for review environment detection, which is accurate, fast, and generalizable to other scenarios.

Wearable technology has played a crucial role in the diagnosis and long-term monitoring of Parkinson’s disease in computer-aided diagnosis. With the development of wearable devices, mobile health detection has become a hot spot. Different from traditional computer-aided diagnosis, this paper is devoted to modeling Parkinson’s disease staging in the field of mobile health using smart watch. First, we analyze the factors that hinder the Parkinson’s disease staging model in the field of mobile health from the three aspects of environment, wearables and wearing. Second, in a free-living environment, the problem of uncertainty in activities of daily living has a fatal impact on machine learning models. On the one hand, the uncertainty of activities caused by patients’ independent completion of activity collection; on the other hand, the uncertainty of staging labels caused by the use of remote video diagnosis by clinicians. Finally, considering many of the above factors, we used smart watch to collect patients’ daily activity data, and built a machine learning model to stage the condition of Parkinson’s patients in free-living environment. Following that, experiments were conducted using our collected dataset of 70 Parkinson’s disease patients to quantify and remove uncertainty in daily activity data in free-living environment. The experimental results show that for Parkinson’s disease staging in free-living environment, the uncertainty of the data is the main factor leading to the instability and low performance of the model. Meanwhile, the anomaly detection and uncertain label detection can alleviate the impact of the uncertainty of the data on the model. In addition, it is verified that the staging framework for Parkinson’s patients in free-living environment constructed using smart watch is feasible.

Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that different modalities can provide more complementary information to help classify the samples from the public dataset Alzheimer's Disease Neuroimaging Initiative (ADNI). In addition, the combination of modalities and different tasks are external factors that affect the performance of multimodal learning. Above all, we summrise three main problems in the early diagnosis of Alzheimer's disease: (i) unimodal vs multimodal; (ii) different combinations of modalities; (iii) classification of different tasks. In this paper, to experimentally verify these three problems, a novel and reproducible multi-classification framework for Alzheimer's disease early automatic diagnosis is proposed to evaluate and verify the above issues. The multi-classification framework contains four layers, two types of feature representation methods, and two types of models to verify these three issues. At the same time, our framework is extensible, that is, it is compatible with new modalities generated by new technologies. Following that, a series of experiments based on the ADNI-1 dataset are conducted and some possible explanations for the early diagnosis of Alzheimer's disease are obtained through multimodal learning. Experimental results show that SNP has the highest accuracy rate of 57.09% in the early diagnosis of Alzheimer's disease. In the modality combination, the addition of Single Nucleotide Polymorphism modality improves the multi-modal machine learning performance by 3% to 7%. Furthermore, we analyse and discuss the most related Region of Interest and Single Nucleotide Polymorphism features of different modalities.

Most of the previous machine learning-based models for multi-modal medical diagnosis, primarily designed for unimodal images, usually do not fully leverage the potential of multimodal medical images, leading to limited classification accuracy. These conventional methods typically focus only on the intermodality common information, neglecting the intra-modality specific information and assuming that the common information is more effective in disease diagnosis. Moreover, they do not adequately address the impact of fuzzy information between different medical imaging modalities on diagnostic results. To this end, we propose a Modality Fuzzy Information Separation Network for disease classification, which extracts both common and specific information from fuzzy information to construct a comprehensive representation of multi-modal medical images. Specifically, we extract modality invariant features as common information by explicitly modeling and maximizing loss constraints on mutual information. For specific information extraction, a constraint on feature space independence between specific and common information is imposed on each modality. Above two steps, we concatenate common information and specific information to construct a comprehensive multi-modal representation for separating fuzzy information. Finally, we purposely design a decoder network to reconstruct medical images from uni-modal specific information and common information to demonstrate the effectiveness of the modality fuzzy information separation network. We conducted a validation of the proposed method's performance in classifying cardiomegaly, pneumothorax, edema, and skin disease. The experimental results substantiate the effectiveness of our proposed approach.

In this paper, our goal is to design a simple learning paradigm for long-tail visual recognition, which not only improves the robustness of the feature extractor but also alleviates the bias of the classifier towards head classes while reducing the training skills and overhead. We propose an efficient one-stage training strategy for long-tailed visual recognition called Global and Local Mixture Consistency cumulative learning (GLMC). Our core ideas are twofold: (1) a global and local mixture consistency loss improves the robustness of the feature extractor. Specifically, we generate two augmented batches by the global MixUp and local CutMix from the same batch data, respectively, and then use cosine similarity to minimize the difference. (2) A cumulative head-tail soft label reweighted loss mitigates the head class bias problem. We use empirical class frequencies to reweight the mixed label of the head-tail class for long-tailed data and then balance the conventional loss and the rebalanced loss with a coefficient accumulated by epochs. Our approach achieves state-of-the-art accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets. Additional experiments on balanced ImageNet and CIFAR demonstrate that GLMC can significantly improve the gen-eralization of backbones. Code is made publicly available at https://github.com/ynu-yangpeng/GLMC.

An effective auxiliary diagnostic model for the severity of Parkinson’s disease (PD) could help hospitals reduce their workload, particularly in nations or regions where medical resources are limited. However, a critical challenge persists that hampers the progress of such endeavors. Previous studies have employed label propagation techniques that assign uniform labels to all activity signal segments of a patient, neglecting the complex expression of PD symptoms, thereby introducing label noise. To confront this challenge, we have collected an extensive set of PD activity signals from a clinical setting and have proposed an efficient and robust framework for assessing PD severity. Specifically, we gathered wearable device data on 14 daily activities from 70 PD patients, based on the Unified Parkinson’s Disease Rating Scale Part III. Our data analysis indicates that many segments within the activities were incorrectly labeled, significantly impairing the classification performance of the model. We introduced a novel framework based on Multi-Instance Learning with a Re-weighted Discriminative Instance Mapping (RDIM) to model PD auxiliary diagnosis, aiming to eliminate the impact of label noise present in the data. The results demonstrate that our framework achieves an accuracy of 80.88% in classifying the severity of PD, effectively addressing the label noise caused by coarse-grained label propagation.

In order to control the first wave of COVID-19 pandemic in 2020, many models have shown effectiveness in predicting the spread of new coronary pneumonia and the different interventions. However, few models can collect large amounts of high-quality real-time data faster under the premise of protecting privacy, considering the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant and the mass vaccination program as a new intervention. Therefore, we developed a mobile intelligent application that can collect a large amount of real-time data while protecting privacy and conducted a feasibility study by defining a new COVID-19 mathematical model SEMCVRD. By simulating different intervention measures, the prediction model of the mobile intelligent application used in this article simulates the epidemic situation in the U.K. as an example. The findings are as below: the optimal intervention strategy is to suppress the intervention at$P=3$(intervention intensity: the average number of contacts per person per day) before the end of March 2021, then gradually release the intervention intensity at a rate of$P+2$, and finally release the intensity to$P=9$in June 2021. The COVID-19 pandemic will end at the end of June 2021, when the total number of deaths will reach 128772. This strategy will be able to balance the tradeoff between loss of life and economic loss. Compared with the official statistics released by the U.K. government on May 31, 2021, our model can accurately predict the relative error rate of the total number of cases is less than 6.9%, and the relative error rate of the total number of deaths is less than 1%. Furthermore, the model is also suitable for collecting data from countries/regions around the world.

Alzheimer's Disease (AD) is the most common reason of dementia that causes serious problems in patients' congnitive functions. Multi-task learning (MTL) has performed well in studies of longitudinal processes in Alzheimer's disease for revealing the progression of AD. Combined with prior knowl-edges in disease progression or medical science, regularization MTL framework could introduce empirical constraints more flexibly. Meanwhile, it brings higher cost during optimization. While it shown that most of formulations could not define the disease progression precisely. Existing regression methods with temporal smoothness method eliminated abnormal fluctuation of cognitive scores, and neglected the sophisticated progression in disease. In this article, we proposed an analytic method to define the progression of AD, and a flexible bandwidth method to encourage the points of disease time sequence temporal smoothness in an appropriate way. To solve three non-smooth penalties in our method, we proposed an optimization method combined accelerated gradient descent (AGD) and alternating direction method of multipliers (ADMM).

The clinical progression of Alzheimer's disease( AD ) can't be accurately evaluated by single modality data alone. Multi-modal data have a good effect on the diagnosis of AD. Clarifying the complementarity between modalities is crucial for the assessment of each stage of AD. Few studies have specifically explored the complementarity between different modalities due to the lack of completely aligned and paired multi-modal data and the limitation of sample size. However, collecting the full set of aligned and paired data is expensive or even impractical. In addition, the limited number of samples poses a great challenge to the robustness of the model. In this paper, different machine learning( ML ) methods were used to explore data complementarity between T1-weighted magnetic resonance imaging ( MRI ), cerebrospinal fluid ( CSF ), and fluorodeoxyglucose-positron emission tomography ( FDG-PET ) modalities. The different modal data of Alzheimer's Neuroimaging Initiative ( ADNI ) and the self-extracted neuroimaging data were experimentally explored. Experiments show that there is obvious complementarity between MRI and CSF. By fusing MRI and CSF data, three binary classification tasks using multi-modal fusion data have achieved varying degrees of improvement. At the same time, we also explored the important features of multi-modal fusion data through SHapley Additive exPlanations ( SHAP ), and found that most important features are supported by relevant literature.

Alzheimer’s Disease (AD) is the most common dementia characterized by loss of brain function. Multi-tasking learning methods have been widely used to predict cognitive performance and select important imaging biomarkers in AD research. The temporal smoothness assumption, prevalent for modeling AD progression, means the difference between cognitive scores at two consecutive time points is relatively small. However, it’s not appropriate due to the presence of sample disturbance and the effectiveness of drug therapy. In addition, many multi-task learning methods select discriminative feature subset from MRI features, assuming that correlations between tasks are consistent, which ignores the complex intrinsic correlation structure of tasks. In this paper, we present a multi-task learning framework which utilizes generalized fused Lasso and generalized group Lasso (GFGGL for abbreviation) to model the disease progression with the complex intrinsic nonlinear structures of disease. The proposed framework is more flexible to utilize the inherent nonlinear relation of AD than existing methods for the reason of we represent the intrinsic structure as three correlation matrices which are functions of super parameters. The framework involves (1) two nonlinear structures of disease progression and (2) one nonlinear structure among tasks. An efficient optimization method is designed for the difficult optimization problem due to the presence of three nonsmooth penalties. Extensive experimental results using dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of the proposed method.