Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization. However, a pressing issue in this field is that the invention of new ETO algorithms has far outpaced the development of fundamental theories needed to clearly understand the key factors contributing to the success of these algorithms for effective generalization. In response to this challenge, this study aims to establish theoretical foundations for analogy-based ETO, specifically to support various algorithms that frequently reference a key concept known as similarity. First, we introduce analogical reasoning and link its subprocesses to three key issues in ETO. Then, we develop theories for analogy-based knowledge transfer, rooted in the principles that underlie the subprocesses. Afterwards, we present two theorems related to the performance gain of analogy-based knowledge transfer, namely unconditionally nonnegative performance gain and conditionally positive performance gain, to theoretically demonstrate the effectiveness of various analogy-based ETO methods. Last but not least, we offer a novel insight into analogy-based ETO that interprets its conditional superiority over traditional evolutionary optimization through the lens of the no free lunch theorem for optimization.

Neuroimaging analysis aims to reveal the information-processing mechanisms of the human brain in a noninvasive manner. In the past, graph neural networks (GNNs) have shown promise in capturing the non-Euclidean structure of brain networks. However, existing neuroimaging studies focused primarily on spatial functional connectivity, despite temporal dynamics in complex brain networks. To address this gap, we propose a spatio-temporal interactive graph representation framework (STIGR) for dynamic neuroimaging analysis that encompasses different aspects from classification and regression tasks to interpretation tasks. STIGR leverages a dynamic adaptive-neighbor graph convolution network to capture the interrelationships between spatial and temporal dynamics. To address the limited global scope in graph convolutions, a self-attention module based on Transformers is introduced to extract long-term dependencies. Contrastive learning is used to adaptively contrast similarities between adjacent scanning windows, modeling cross-temporal correlations in dynamic graphs. Extensive experiments on six public neuroimaging datasets demonstrate the competitive performance of STIGR across different platforms, achieving state-of-the-art results in classification and regression tasks. The proposed framework enables the detection of remarkable temporal association patterns between regions of interest based on sequential neuroimaging signals, offering medical professionals a versatile and interpretable tool for exploring task-specific neurological patterns. Our codes and models are available at https://github.com/77YQ77/STIGR/.

To relieve the growing burden of mental disorders, deep learning techniques have emerged as a promising tool to aid clinicians by detecting abnormal patterns in neuroimaging data. However, the efficacy of such models is contingent upon access to vast pools of patient data, which is impractical for individual healthcare institutions. Moreover, the privacy-preserving policy regulations governing medical images further complicate the pooling of information necessary for training robust models. Federated Learning (FL) offers a solution to this dilemma by aggregating the local model updates without compromising patient privacy. However, current studies fail to adequately account for the need to personalize models according to the diverse structures of local data. In this work, an effective heterogeneous structured FL framework using graph convolutional aggregation dubbed GAHFL is proposed to diagnose mental disorders on functional magnetic resonance imaging data. In addition, we propose to perform the global model self-evaluation to enable the training to emphasize the samples that are difficult to classify. To solve the catastrophic forgetting problem, we build a historical logit pool to awaken the global model’s recognition ability by performing a server knowledge self-distillation. Empirical evaluations demonstrate that the proposed framework achieves averaged diagnosis AUC values of 69.01% and 69.04% with different sizes of public datasets of ABIDE-I and ADHD-200 datasets, respectively. The ablation studies and robustness validation test further demonstrate the superior performance of our framework.

Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.

The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthcare services. Particularly, multimodal representation learning gains increasing attention thanks to the high temporal and spatial resolution information extracted from neuroimaging fusion. In this work, we propose an efficient multimodality fusion framework to identify multiple mental disorders based on the combination of functional and structural magnetic resonance imaging. A multioutput conditional generative adversarial network (GAN) is developed to address the scarcity of multimodal data for augmentation. Based on the augmented training data, the multiheaded gating fusion model is proposed for classification by extracting the complementary features across different modalities. The experiments demonstrate that the proposed model can achieve robust accuracies of 75.1 ± 1.5 %, 72.9 ± 1.1 %, and 87.2 ± 1.5 % for autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and schizophrenia, respectively. In addition, the interpretability of our model is expected to enable the identification of remarkable neuropathology diagnostic biomarkers, leading to well-informed therapeutic decisions.

Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.

The high prevalence of mental disorders gradually poses a huge pressure on the public healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to relieve the tension in healthcare institutions by detecting abnormal neuroimaging-derived phenotypes. However, training deep learning models relies on sufficient annotated datasets, which can be costly and laborious. Semi-supervised learning (SSL) and transfer learning (TL) can mitigate this challenge by leveraging unlabeled data within the same institution and advantageous information from source domain, respectively. This work is the first attempt to propose an effective semi-supervised transfer learning (SSTL) framework dubbed S3TL for CAD of mental disorders on fMRI data. Within S3TL, a secure cross-domain feature alignment method is developed to generate target-related source model in SSL. Subsequently, we propose an enhanced dual-stage pseudo-labeling approach to assign pseudo-labels for unlabeled samples in target domain. Finally, an advantageous knowledge transfer method is conducted to improve the generalization capability of the target model. Comprehensive experimental results demonstrate that S3TL achieves competitive accuracies of 69.14%, 69.65%, and 72.62% on ABIDE-I, ABIDE-II, and ADHD-200 datasets, respectively. Furthermore, the simulation experiments also demonstrate the application potential of S3TL through model interpretation analysis and federated learning extension.

The high prevalence of mental disorders gradually poses a huge pressure on the public healthcare services. Recently, deep learning-based computer-aided diagnosis has been introduced to relieve the tension in healthcare institutions by automatically detecting abnormal neuroimaging-derived pheno-types in patients. However, the training of deep learning models relies on sufficiently large annotated datasets, which can be costly, time-consuming, and laborious. Semi-supervised learning (SSL) can mitigate this challenge by leveraging both labeled and unlabeled samples. In this work, an effective dual-stage pseudo-labeling based classification framework dubbed DSPL is proposed to diagnose mental disorders on functional magnetic resonance imaging data. A bicriteria-based pseudo-labels selection method is developed to filter out inferior pseudo-labeled samples. Subsequently, we further propose a self-mutual learning enhanced pseudo-labeling generation approach to mitigate the adverse effects bought by the noisy pseudo-labeled samples. On real-world datasets, the proposed method achieves diagnosis accuracies of 68.09%, 67.94%, and 68.13% on ABIDE-I, ABIDE-II, and ADHD-200, respectively. Ablation study suggests that each component in DSPL makes a great contribution to performance improvement.

MOTIVATION: The identification of drug-target interactions (DTIs) plays a vital role for in silico drug discovery, in which the drug is the chemical molecule, and the target is the protein residues in the binding pocket. Manual DTI annotation approaches remain reliable; however, it is notoriously laborious and time-consuming to test each drug-target pair exhaustively. Recently, the rapid growth of labelled DTI data has catalysed interests in high-throughput DTI prediction. Unfortunately, those methods highly rely on the manual features denoted by human, leading to errors. RESULTS: Here, we developed an end-to-end deep learning framework called CoaDTI to significantly improve the efficiency and interpretability of drug target annotation. CoaDTI incorporates the Co-attention mechanism to model the interaction information from the drug modality and protein modality. In particular, CoaDTI incorporates transformer to learn the protein representations from raw amino acid sequences, and GraphSage to extract the molecule graph features from SMILES. Furthermore, we proposed to employ the transfer learning strategy to encode protein features by pre-trained transformer to address the issue of scarce labelled data. The experimental results demonstrate that CoaDTI achieves competitive performance on three public datasets compared with state-of-the-art models. In addition, the transfer learning strategy further boosts the performance to an unprecedented level. The extended study reveals that CoaDTI can identify novel DTIs such as reactions between candidate drugs and severe acute respiratory syndrome coronavirus 2-associated proteins. The visualization of co-attention scores can illustrate the interpretability of our model for mechanistic insights. AVAILABILITY: Source code are publicly available at https://github.com/Layne-Huang/CoaDTI.

Increasing mental disorders have emerged as an urgent public health concern such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Related mental disorders may share high overlap in clinical symptoms. Therefore, their diagnosis can be challenging to merely rely on the observation of cognitive phenotypes and behavioral manifestations. Unfortunately, there is no additional support of biochemical markers, laboratory tests, or neuroimaging analysis, which can be used as a diagnostic gold standard currently. Over the past decades, resting-state functional magnetic resonance imaging (rs-fMRI) has been considered as one of the most promising modality to capture the intrinsic neural activation patterns between regions in the brain. In this work, we focus on ASD and ADHD due to their high prevalence and relevance with the aim to exploit the multi-task learning (MTL) paradigm for their diagnosis. To the best of our knowledge, this is the first time to make use of the disease-specific heterogeneities for the MTL classification of ASD and ADHD via rs-fMRI signal. We propose a novel graph-based feature selection method to filter out irrelevant functional connectivity features. Then an efficient structure of multi-gate mixture-of-experts (MMoE) is applied to the MTL classification framework. Finally, the experiment results demonstrate that the proposed model can achieve a reliable classification performance in a short term, yielding the mean accuracies of 0.687±0.005 and 0.650±0.014 in ASD and ADHD datasets, respectively. The graph-based feature selection method and MMoE model are demonstrated to make great contribution to performance improvement.