Biological age is a more direct reflection of physiological status than chronological age, serving as a vital measure to evaluate health risks and aging interventions. While steroid metabolomics offers rich information for exploring aging mechanisms, the complex and nonlinear interactions within metabolic networks remain challenging in modeling. Here, we propose and describe METRON as a deep learning framework to predict biological ages from steroid metabolomics. Specifically, a Metabolite Interaction Perception Module (MIPM) is proposed to capture the interactions. Subsequently, a Group-Rational Kolmogorov-Arnold Network is also integrated to capture intricate dependencies and enhance the representation capability. We demonstrate that METRON achieves promising performance as compared to other machine learning and deep learning methods. Beyond performance, METRON offers interpretability by recovering the established markers such as Dehydroepiandrosterone (DHEA) and identifying 17-hydroxyprogesterone (17-OH-P4) as the key signature linked to hypothalamic-pituitary-adrenal axis dynamics. These results support the capacity of METRON not only to estimate biological age but also to uncover underappreciated metabolic drivers behind aging.

Cellular responses to genetic perturbations are prevalent in wide contexts from the fundamental understandings on pathology to the development of clinical therapies and the discovery of novel drug targets. Nonetheless, the substantial amount of possible perturbation combinations renders wet-lab experiments prohibitively expensive and time-consuming. To address it, the BRNET model is proposed for predicting non-linear transcriptional outcomes where multiple perturbations exist. BRNET integrates prior knowledge with advanced embeddings into a non-stacked neural structure to predict transcriptional responses to both individual and multiple genetic perturbations. For unseen scenarios, BRNET also generalizes well under the corresponding perturbations. Experimental results highlight the capabilities of BRNET, demonstrating promising performance as compared to established deep learning models.

Multimodal change detection (MCD) aims to detect changed areas between the bi-temporal multimodal images such as the RGB, panchromatic (PAN), multispectral (MS), and synthetic aperture radar (SAR) images, which has attracted attention in recent years. However, existing deep learning-based methods for MCD tasks still face several heterogeneity factors, the first one is the spatial resolution differences in multimodal data, which leads to the semantic gap between multimodal features. To solve this problem, we propose the heterogeneous collaborative fusion (HCF) module to integrate the multimodal features with spatial gaps. The other one is the consistency and dissimilarity between multimodal data, which lead to unequal detection contributions. To address this dilemma, we propose the heterogeneous adaptive fusion (HAF) module to fuse multimodal decision-making jointly. In this study, we proposed a heterogeneous fusion network for MCD (HF-MCD) with the HCF and the HAF module. We validate the proposed method on four public available MCD datasets. Extensive experimental results have demonstrated the superior performance of HF-MCD over the state-of-the-art methods.

Multimodal images can provide richer information for a wide range of applications. However, the physical heterogeneity resulted by the difference of spatial resolution pose great challenges for multimodal change detection. To this end, we propose a change detection method called primary modality guided deep neural network (PMGN), integrating multi-resolution and multimodal data. First, we propose the principal modality and rely more on its information. Second, PMGN compensates for the limitations of low spatial resolution modalities through the primary modality guided feature exchange module. Finally, the adaptive decision fusion module enables the multimodal decision-level features to fuse efficiently. Experiments demonstrate the effectiveness and advantages of the proposed approach.

Hyperspectral image (HSI) change detection can detect subtle land surface change information, which is of great significance for promoting the sustainable development of human beings. Different from traditional methods, deep learningbased methods can effectively extract more discriminative features, but the problem of mixed pixels is still a challenge due to the low spatial resolution HSI. In this study, an Adaptive Endmembers Learning (AEL)-based deep unmixing network has been proposed for the change detection task, which can perform an unsupervised unmixing through adaptive endmembers learning and then obtain both the binary and multi-class change detection results. Experiments on the China dataset and the USA dataset have shown that AEL performs better than current state-of-the-art methods.

Broad learning system (BLS), an efficient neural network with a flat structure, has received a lot of attention due to its advantages in training speed and network extensibility. However, the conventional BLS adopts the least square loss, which treats each sample equally and thus is sensitivity to noise and outliers. To address this concern, in this article we propose a self-paced BLS (SPBLS) model by incorporating the novel self-paced learning (SPL) strategy into the network for noisy data regression. With the assistance of the SPL criterion, the model output is used as feedback to learn appropriate priority weight to readjust the importance of each sample. Such a reweighting strategy can help SPBLS to distinguish samples from "easy" to "difficult" in model training, equipping the model robust to noise and outliers while maintaining the characteristics of the original system. Moreover, two incremental learning algorithms associated to SPBLS have also been developed, with which the system can be updated quickly and flexibly without retraining the entire model when new training samples are added or the network needs to be expanded. Experiments conducted on various datasets demonstrate that the proposed SPBLS can achieve satisfying performance for noisy data regression.