{"title":"NeuroPpred-MSN: A Neuropeptide Prediction Model Based on Multi-feature Fusion and Siamese Networks.","authors":"Jian Wen, Minyu Chen, Yongqi Shen, Honghong Wang, Zhuoyu Wei, Lichuan Gu, Xiaolei Zhu","doi":"10.1007/s12539-025-00730-6","DOIUrl":"https://doi.org/10.1007/s12539-025-00730-6","url":null,"abstract":"<p><p>The discovery of neuropeptides offers numerous opportunities for identifying novel drugs and targets to treat a variety of diseases. While various computational methods have been proposed, there remains potential for further performance improvement. In this work, we introduce NeuroPpred-MSN, an innovative and efficient neuropeptide prediction model that leverages multi-feature fusion and Siamese networks. To comprehensively represent the information of neuropeptides, the peptide sequences are encoded by four encoding schemes (token embedding, word2vec embedding, protein language embedding, and handcrafted features). Then, the token embedding and word2vector embedding are fed to a Siamese network channel. In the other channel of the model, peptide sequences and their secondary structure sequences are fed into ProtT5-XL-UniRef50 model to generate the embedding features, while handcrafted encoding techniques are used to extract the physicochemical information. Then the two kinds of features are fused and fed into a bidirectional gated recurrent unit (Bi-GRU) network for further processing. Ultimately, the outputs of the two channels are integrated into a fully connected layer, thereby facilitating the generation of the final prediction. The results on the independent test set indicate that NeuroPpred-MSN exhibits superior predictive performance, with an area under the receiver operating characteristic curve (AUROC) of 98.3%, exceeding the performance of other state-of-the-art predictors. Specifically, compared to other optimal results, this model exhibits improvements of 1.52% in accuracy (ACC), 1.52% in F1 score (F1), 3.2% in Matthews correlation coefficient (MCC), and 1.55% in AUROC. The model was further evaluated on imbalanced datasets, where it achieved the highest values in AUROC, ACC, MCC, sensitivity (SN), and F1, further demonstrating its robustness and generalization. The model can be accessed at the following GitHub repository: https://github.com/wenjean/NeuroPpred-MSN .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hoai-Nhan Tran, Nguyen-Phuc-Xuan Quynh, Haochen Zhao, Jianxin Wang
{"title":"Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.","authors":"Hoai-Nhan Tran, Nguyen-Phuc-Xuan Quynh, Haochen Zhao, Jianxin Wang","doi":"10.1007/s12539-025-00723-5","DOIUrl":"https://doi.org/10.1007/s12539-025-00723-5","url":null,"abstract":"<p><p>In recent years, computational methods such as machine learning and deep learning have been increasingly used to solve various bioinformatics problems related to protein sequence data, such as predicting protein interaction, protein function, subcellular location, and so on. The first crucial step in applying these methods is how to represent a protein sequence as an input feature vector, as the feature vector quality significantly impacts the performance of those methods. A range of protein sequence descriptors has been proposed to enhance the quality of protein sequence representation. Existing descriptors extract information that can be obtained from sequences, such as composition, distribution, spatial correlation between amino acids, and so on. However, improvements can still be made in spatial correlation to capture better sequence similarity, which is valuable for Protein-Protein Interaction (PPI) prediction tasks. In this study, our aim is to develop new descriptors based on six well-known sequence descriptors to improve the ability to represent protein sequences. We evaluate the performance of the new descriptors on various PPI datasets. The results demonstrate that the proposed descriptors outperform their original versions in terms of PPI prediction performance. This work also introduces ProtSeqDesc (protein sequence descriptors), a flexible Python package that includes 51 types of feature vectors, covering all proposed descriptors. The software package is aimed at meeting the demand for the application of computational methods in bioinformatics.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SPCF-YOLO: An Efficient Feature Optimization Model for Real-Time Lung Nodule Detection.","authors":"Yawen Ren, Chenyang Shi, Donglin Zhu, Changjun Zhou","doi":"10.1007/s12539-025-00720-8","DOIUrl":"https://doi.org/10.1007/s12539-025-00720-8","url":null,"abstract":"<p><p>Accurate pulmonary nodule detection in CT imaging remains challenging due to fragmented feature integration in conventional deep learning models. This paper proposes SPCF-YOLO, a real-time detection framework that synergizes hierarchical feature fusion with anatomical context modeling. First, the space-to-depth convolution (SPDConv) module preserves fine-grained features in low-resolution images through spatial dimension reorganization. Second, the shared feature pyramid convolution (SFPConv) module is designed to dynamically extract multi-scale contextual information using multi-dilation-rate convolutional layers. Incorporating a small object detection layer aims to improve sensitivity to small nodules. This is achieved in combination with the improved pyramid squeeze attention (PSA) module and the improved contextual transformer (CoTB) module, which enhance global channel dependencies and reduce feature loss. The model achieves 82.8% mean average precision (mAP) and 82.9% F1 score on LUNA16 at 151 frames per second (representing improvements of 17.5% and 82.9% over YOLOv8 respectively), demonstrating real-time clinical viability. Cross-modality validation on SIIM-COVID-19 shows 1.5% improvement, confirming robust generalization.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"AMFCL: Predicting miRNA-Disease Associations Through Adaptive Multi-source Modality Fusion and Contrastive Learning.","authors":"Yanfang Yang, Shuang Wang, Wenyue Kang, Cuina Jiao, Yinglian Gao, Jinxing Liu","doi":"10.1007/s12539-025-00724-4","DOIUrl":"https://doi.org/10.1007/s12539-025-00724-4","url":null,"abstract":"<p><p>Dysregulation of microRNAs (miRNAs) is a cause of progression in numerous diseases. Uncovering miRNA-disease associations (MDAs) is essential for discovering new biomarkers. Nonetheless, in contrast to conventional biological approaches, advanced computational approaches are typically more rapid and cost-effective. However, most computational methods still face several challenges: (i) integrating multi-source information (MSI); (ii) optimizing feature fusion; (iii) mitigating over-smoothing in graph-based models. This paper introduces a novel model, AMFCL. To encapsulate the miRNA-disease relationships, three types of networks are first constructed. After that, the node representations are learned via multi-layer graph sample and aggregate (GraphSAGE). An adaptive fusion mechanism (AFM) dynamically assigns weights to feature representations to optimize the fusion process. Additionally, a residual connection is used to combat the over-smoothing effect that occurs in graph-based models. The robustness of miRNA and disease embeddings is improved by contrastive learning (CL). Lastly, a multi-layer perceptron (MLP) has all feature embeddings fed into it for the computation of MDA scores. The corresponding experimental results show remarkable improvements in AMFCL compared to advanced models. Moreover, relevant case studies systematically validate the approach's effectiveness in identifying unknown MDAs.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Multi-modal Drug Target Affinity Prediction Based on Graph Features and Pre-trained Sequence Embeddings.","authors":"Xin Tang, Xiujuan Lei, Lian Liu","doi":"10.1007/s12539-025-00713-7","DOIUrl":"https://doi.org/10.1007/s12539-025-00713-7","url":null,"abstract":"<p><p>With the advantages of reducing biochemical experiments and enabling the rapid screening of potential druggable compounds, accurate computational methods are essential for predicting Drug-Target affinity (DTA). Current deep learning-based DTA prediction methods predominantly concentrate on single-modal information from drugs or targets. In this article, we propose a new multi-modal DTA prediction method, MGSDTA, to integrate graph features and sequence features of drug molecules and target proteins. We extract features from the drug molecular graphs and target protein graphs, meanwhile, we extract sequence features using continuous embeddings generated by advanced self-supervised pre-trained models, Mol2vec and ProtVec, for drug substructures and target subsequences respectively. Finally, they are integrated with a weighted fusion module for DTA prediction. Experiments on benchmark datasets indicate that the performance of MGSDTA exceeds single-modal methods based solely on sequences or graphs.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hajra Qayyum, Muhammad Faheem Raziq, Haseeb Manzoor, Syed Shujaat Ali Zaidi, Amjad Ali, Masood Ur Rehman Kayani
{"title":"Efficient De Novo Assembly and Recovery of Microbial Genomes from Complex Metagenomes Using a Reduced Set of k-mers.","authors":"Hajra Qayyum, Muhammad Faheem Raziq, Haseeb Manzoor, Syed Shujaat Ali Zaidi, Amjad Ali, Masood Ur Rehman Kayani","doi":"10.1007/s12539-025-00722-6","DOIUrl":"https://doi.org/10.1007/s12539-025-00722-6","url":null,"abstract":"<p><p>De novo assembly and genome binning are fundamental steps for genome-resolved metagenomics analyses. However, the availability of limited computational resources and extensive processing time limit the broader application of these analyses. To address these challenges, the optimization of the parameters employed in these processes can improve the effective utilization of available metagenomics tools. Therefore, this study tested three sets of k-mers (default, reduced, and extended) for their efficiency in metagenome assembly and suitability in recovering metagenome-assembled genomes. The results demonstrate that the reduced set of k-mers outperforms the other two sets in computational efficiency and the quality of results. The assemblies from the default set are comparable with those from the reduced set; however, less complete and highly contaminated metagenome-assembled genomes are obtained at the expense of higher processing time. The extended set of k-mers yields less contiguous but computationally expensive assemblies. This set takes approximately 3-times more processing time than the reduced k-mers and recovers the lowest proportions of high and medium-quality metagenome-assembled genomes. Contrarily, the reduced set produces better assemblies, substantially improving the number and quality of the recovered metagenome-assembled genomes in significantly reduced processing time. Validation of the reduced k-mer set on previously published metagenome datasets further demonstrates its effectiveness not only for human metagenomes but also for the metagenomes of environmental origin. These findings underscore that the reduced k-mer set is optimal for efficient metagenome analyses of varying complexities and origins. This optimization of the k-mer set used in metagenome assemblers significantly reduces computational time while improving the quality of the assemblies and recovered metagenome-assembled genomes. This efficient solution will facilitate the widespread application of genome-resolved analyses, even in resource-limited settings, and help the recovery of better-quality metagenome-assembled genomes for downstream analyses.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"UltraNet: Unleashing the Power of Simplicity for Accurate Medical Image Segmentation.","authors":"Ziyi Han, Yuanyuan Zhang, Lin Liu, Yulin Zhang","doi":"10.1007/s12539-024-00682-3","DOIUrl":"10.1007/s12539-024-00682-3","url":null,"abstract":"<p><p>The imperative development of point-of-care diagnosis for accurate and rapid medical image segmentation, has become increasingly urgent in recent years. Although some pioneering work has applied complex modules to improve segmentation performance, resulting models are often heavy, which is not practical for the modern clinical setting of point-of-care diagnosis. To address these challenges, we propose UltraNet, a state-of-the-art lightweight model that achieves competitive performance in segmenting multiple parts of medical images with the lowest parameters and computational complexity. To extract a sufficient amount of feature information and replace cumbersome modules, the Shallow Focus Float Block (ShalFoFo) and the Dual-stream Synergy Feature Extraction (DuSem) are respectively proposed at both shallow and deep levels. ShalFoFo is designed to capture finer-grained features containing more pixels, while DuSem is capable of extracting distinct deep semantic features from two different perspectives. By jointly utilizing them, the accuracy and stability of UltraNet segmentation results are enhanced. To evaluate performance, UltraNet's generalization ability was assessed on five datasets with different tasks. Compared to UNet, UltraNet reduces the parameters and computational complexity by 46 times and 26 times, respectively. Experimental results demonstrate that UltraNet achieves a state-of-the-art balance among parameters, computational complexity, and segmentation performance. Codes are available at https://github.com/Ziii1/UltraNet .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"375-389"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.","authors":"Linqian Zhao, Junliang Shang, Xianghan Meng, Xin He, Yuanyuan Zhang, Jin-Xing Liu","doi":"10.1007/s12539-024-00684-1","DOIUrl":"10.1007/s12539-024-00684-1","url":null,"abstract":"<p><p> Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drugs, has emerged as a mainstream approach for treating complex diseases and alleviating symptoms. However, drug-drug interactions (DDIs) can sometimes lead to adverse reactions, potentially endangering lives. Therefore, developing efficient and accurate DDI prediction methods is crucial for elucidating drug mechanisms and preventing side effects. Current prediction methods often focus solely on the presence of interactions between drugs when constructing DDI graphs, neglecting the specific types of DDIs. This oversight can result in a decline in predictive performance. To address this issue, we propose an Adaptive Multi-Kernel Graph Neural Network (AMKGNN) for DDI prediction. AMKGNN differentiates DDIs into increase-type and decrease-type interactions, constructing separate increased DDI and decreased DDI graphs as convolutional kernels. AMKGNN employs a graph kernel learning mechanism that adaptively determines the optimal threshold between high-frequency and low-frequency signals in the network to capture node embeddings. Initially, AMKGNN learns drug embedding representations based on these two graph convolutional kernels and various drug features. These representations are then concatenated and input into a deep neural network to predict potential DDIs. The results show that our model achieved AUC and AUPR values above 90% across three sub-tasks on two datasets, significantly outperforming the other five comparison models. Furthermore, ablation experiments and case studies validate the superiority of AMKGNN.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"409-423"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predicting Synergistic Drug Combinations Based on Fusion of Cell and Drug Molecular Structures.","authors":"Shiyu Yan, Gang Yu, Jiaoxing Yang, Lingna Chen","doi":"10.1007/s12539-025-00695-6","DOIUrl":"10.1007/s12539-025-00695-6","url":null,"abstract":"<p><p>Drug combination therapy has shown improved efficacy and decreased adverse effects, making it a practical approach for conditions like cancer. However, discovering all potential synergistic drug combinations requires extensive experimentation, which can be challenging. Recent research utilizing deep learning techniques has shown promise in reducing the number of experiments and overall workload by predicting synergistic drug combinations. Therefore, developing reliable and effective computational methods for predicting these combinations is essential. This paper proposed a novel method called Drug-molecule Connect Cell (DconnC) for predicting synergistic drug combinations. DconnC leverages cellular features as nodes to establish connections between drug molecular structures, allowing the extraction of pertinent features. These features are then optimized through self-augmented contrastive learning using bidirectional recurrent neural networks (Bi-RNN) and long short-term memory (LSTM) models, ultimately predicting the drug synergy. By integrating information about the molecular structure of drugs for the extraction of cell features, DconnC uncovers the inherent connection between drug molecular structures and cellular characteristics, thus improving the accuracy of predictions. The performance of our method is evaluated using a five-fold cross validation approach, demonstrating a 35 <math><mo>%</mo></math> reduction in the mean square error (MSE) compared to the next-best method. Moreover, our method significantly outperformed alternative approaches in various evaluation criteria, particularly in predicting different cell lines and Loewe synergy score intervals.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"321-331"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.","authors":"Jinmiao Song, Mingjie Wei, Shuang Zhao, Hui Zhai, Qiguo Dai, Xiaodong Duan","doi":"10.1007/s12539-024-00668-1","DOIUrl":"10.1007/s12539-024-00668-1","url":null,"abstract":"<p><p>Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based on multi-stage multi-modal drug representations (ModDRDSP) to reflect the properties of drugs more comprehensively, and to better model the complex interactions between cells and drugs. Specifically, we adopt the SMILES representation learning method based on the deep hierarchical bi-directional GRU network (DSBiGRU) and the molecular graph representation learning method based on the deep message-crossing network (DMCN) for the multi-modal information of drugs. Additionally, we integrate the multi-omics information of cell lines based on a convolutional neural network (CNN). Finally, we use an ensemble deep forest algorithm for the prediction of drug sensitivity. After validation, the ModDRDSP shows impressive performance which outperforms the four current industry-leading models. More importantly, ablation experiments demonstrate the validity of each module of the proposed model, and case studies show the good results of ModDRDSP for predicting drug sensitivity, further establishing the superiority of ModDRDSP in terms of performance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"231-243"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}