The type of primary tumor responsible for brain metastases is crucial for clinical treatment. However, current approaches predominantly focus on issues such as lesion segmentation in brain MRI imaging. While some studies have attempted to predict primary tumor types using statistical methods, none have established a direct predictive relationship between brain imaging and primary tumor types. With the rapid advancement of large models, adapting pre-trained models for various tasks has become feasible. In this paper, we propose a primary tumor type prediction method called clinicViT, based on a pre-trained large model. This method integrates MRI imaging features with clinical information features. By fine-tuning a pretrained encoder model with the addition of a fusion module and classifier, the clinicViT model directly predicts primary tumor types. Furthermore, utilizing the imaging and clinical features extracted by clinicViT, survival analysis for different primary tumor types is achieved. Extensive experimental results demonstrate that clinicViT outperforms state-of-the-art methods.

Protein metabolite interactions (PMIs) play a crucial role in cellular homeostasis, supporting drug development and revealing biological processes. Traditional detection methods are constrained by resource limitations, making large-scale PMI identification challenging. Existing machine learning approaches for PMI prediction often fail to capture correlation features between proteins and metabolites and struggle with crossspecies and batch effect problems. We propose GCN Embedding-Multihead Focusing Attention (GE-MFAT), a novel method that combines heterogeneous graph neural networks with a focusing attention mechanism to extract complex PMI relationships. Our model employs transfer learning to address cross-species and batch effect challenges. Experimental results across three datasets demonstrate that GE-MFAT significantly outperforms state-of-the-art methods across all evaluation metrics.

Function annotation of proteins is fundamental for revealing the nature of life phenomena and understanding the mechanisms of disease. Several computational methods have been developed to predict protein functions. However, existing methods ignore the prior knowledge among Gene ontology (GO) terms and simply regard the prediction problem as an independent multilabel classification task. To mitigate these issues, we propose a novel predictor based on graph representation learning and iterative optimization, named GDTGO, to explore the potential protein function on the GO. GDTGO employs a graph convolutional network to learn semantically rich and topologically aware knowledge from GO terms, which serves as a strong prior to guide prediction. Furthermore, we model the function prediction task as an iterative optimization problem for set prediction. A DETR-based decoder dynamically refines predictions through a series of layers, where each layer provides corrective feedback to progressively enhance the final output. Experimental results show that GDTGO achieves state-of-the-art performance on the PDB dataset.

Educational and instructional research has provided contrasting results regarding the best representation of numerical information, with the two most common being tabular and graphical representations. This motivated us to examine the issue using a novel approach. We employed electroencephalography (EEG) with event-related synchronisation and desynchronisation (ERS/ERD) to model cognitive load, and alpha and beta band powers to model engagement. We conducted an experiment to measure the cognitive load and engagement of 48 Oregon State University students and compared their performances with respect to these two representations. Structural equation models (SEMs) were constructed to investigate the potential mediation of learning performance by engagement and cognitive load. The results indicate that learning performance was fully mediated by cognitive load and engagement. Relative to graphs, tables produced a higher cognitive load and engagement, and subsequently, greater overall learning performance. Current results showed that different representations could yield significant differences in learning performance, and that an understanding of representation-elicited affective behaviours has considerable potential for future online learning instructional design.

Non-coding RNAs (ncRNAs) play crucial roles in drug resistance and sensitivity, making them important biomarkers and therapeutic targets. However, predicting ncRNA-drug associations is challenging due to issues such as dataset imbalance and sparsity, limiting the identification of robust biomarkers. Existing models often fall short in capturing local and global sequence information, limiting the reliability of predictions. This study introduces DMGAT (diffusion map and heterogeneous graph attention network), a novel deep learning model designed to predict ncRNA-drug associations. DMGAT integrates diffusion maps for sequence embedding, graph convolutional networks for feature extraction, and GAT for heterogeneous information fusion. To address dataset imbalance, the model incorporates sensitivity associations and employs a random forest classifier to select reliable negative samples. DMGAT embeds ncRNA sequences and drug SMILES using the word2vec technique, capturing local and global sequence information. The model constructs a heterogeneous network by combining sequence similarity and Gaussian Interaction Profile kernel similarity, providing a comprehensive representation of ncRNA-drug interactions. Evaluated through five-fold cross-validation on a curated dataset from NoncoRNA and ncDR, DMGAT outperforms seven state-of-the-art methods, achieving the highest area under the receiver operating characteristic curve (0.8964), area under the precision-recall curve (0.8984), recall (0.9576), and F1-score (0.8285). The raw data are released to Zenodo with identifier 13929676. The source code of DMGAT is available at https://github.com/liutingyu0616/DMGAT/tree/main.

Signaling can be used to guide learners’ attention to relevant details of instructional materials, and consequently can foster multimedia learning. We investigate how signaling, and highlighting specifically, are predictive of individual learning performance in short-term knowledge acquisition, mediated by cognitive load and visual attention employing biometrically using electroencephalog- raphy (EEG) and eye-tracking measures. We perform a two-level experiment on short-term knowledge acquisition within the context of an online course, with content-highlighting employed as signaling cues. We use a structural equation model (SEM) to examine the potential mediation that cognitive load and visual attention may have on knowledge acquisition. The results suggest that signaling can direct attention to the crucial areas and potentially reduce the extraneous cognitive load, thereby promoting short-term knowledge acquisition, as a key stage of learning.

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

Online learning has become increasingly commonplace, including the replacement and augmentation of traditional in-residence education, as well as ad-hoc training systems and just-in-time knowledge dissemination. However, design for instructional media to facilitate performance has relied on an inadequate understanding of the behavioral affect elicited from these designs. To investigate the degree to which excitement and engagement are predictive of individual learning outcomes in an online learning setting, we evaluate an experiment with two instructional representations (static and dynamic) using learning materials for a semaphore signaling system. We examine the excitement and engagement levels of the participants using electroencephalography (EEG) as potential mediators. Learning outcomes are measured by the evaluation scores of knowledge retention. We consider several structural equation models (SEMs) to see the underlying relationship between instructional representation, excitement, engagement, and learning outcomes. Notably, the models indicate the full mediation of behavioral affect on learning outcomes, in which both the range of excitement and the maximum level of engagement are mediating effects. This article illustrates the potential for biometrically measured affect aid in the modeling and understanding of how instructional design features ultimately impact performance.

Most of the classification methods of urban functional areas nowadays are only based on single source data analysis and modeling, which can not make full use of the multi-scale and multi-source data that is easy to obtain. Therefore, this paper proposed a classification model of urban functional areas based on multi-modal machine learning, by analyzing regional remote sensing images and behavior data of visitors in the area, using the combination of supervised methods extracted the deep-seated features and relationships of kinds of data, filtered and merged the overall and local features of the data. The model used dual branch neural network combining SE-ResNeXt and Dual Path Network (DPN) to automatically mined and fused the overall characteristics of multi-source data, and used the designed feature engineering to deeply mine the behavior data of users to obtain more association information, then combined the algorithm based on Gradient Boosting Decision Tree to learn the characteristics of different levels and obtained the classification probability for different levels of features. Finally, we continued to use the algorithm based on the Gradient Boosting Decision Tree to learn the probability distribution of different levels of features to obtain the final prediction results of urban functional area classification. Through the analysis and experimental verification of real data sets, the results showed that MM-UrbanFAC model can effectively integrate the features of multi-modal data. Compared with a single classifier, the integration framework based on gradient lifting tree improved the prediction performance, this method can effectively integrate the results of multiple models and accurately classify urban functional areas, and the model can provide reference for tourism recommendation, urban land planning and urban construction.