Interdisciplinary Sciences: Computational Life Sciences最新文献_第6页

Predicting Promoters in Multiple Prokaryotes with Prompt. 利用 Prompt 预测多种原核生物的启动子

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-12-01 Epub Date: 2024-08-07 DOI: 10.1007/s12539-024-00637-8

Qimeng Du, Yixue Guo, Junpeng Zhang, Fuping Lu, Chong Peng, Chichun Zhou

{"title":"Predicting Promoters in Multiple Prokaryotes with Prompt.","authors":"Qimeng Du, Yixue Guo, Junpeng Zhang, Fuping Lu, Chong Peng, Chichun Zhou","doi":"10.1007/s12539-024-00637-8","DOIUrl":"10.1007/s12539-024-00637-8","url":null,"abstract":"Promoters are important cis-regulatory elements for the regulation of gene expression, and their accurate predictions are crucial for elucidating the biological functions and potential mechanisms of genes. Many previous prokaryotic promoter prediction methods are encouraging in terms of the prediction performance, but most of them focus on the recognition of promoters in only one or a few bacterial species. Moreover, due to ignoring the promoter sequence motifs, the interpretability of predictions with existing methods is limited. In this work, we present a generalized method Prompt (Promoters in multiple prokaryotes) to predict promoters in 16 prokaryotes and improve the interpretability of prediction results. Prompt integrates three methods including RSK (Regression based on Selected k-mer), CL (Contrastive Learning) and MLP (Multilayer Perception), and employs a voting strategy to divide the datasets into high-confidence and low-confidence categories. Results on the promoter prediction tasks in 16 prokaryotes show that the accuracy (Accuracy, Matthews correlation coefficient) of Prompt is greater than 80% in highly credible datasets of 16 prokaryotes, and is greater than 90% in 12 prokaryotes, and Prompt performs the best compared with other existing methods. Moreover, by identifying promoter sequence motifs, Prompt can improve the interpretability of the predictions. Prompt is freely available at https://github.com/duqimeng/PromptPrompt , and will contribute to the research of promoters in prokaryote.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"814-828"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897299","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}

引用次数: 0

SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading. SCINet：用于动脉硬化性视网膜病变分级的分割与分类交互 CNN 方法。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-12-01 Epub Date: 2024-09-02 DOI: 10.1007/s12539-024-00650-x

Xiongwen Quan, Xingyuan Ou, Li Gao, Wenya Yin, Guangyao Hou, Han Zhang

{"title":"SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.","authors":"Xiongwen Quan, Xingyuan Ou, Li Gao, Wenya Yin, Guangyao Hou, Han Zhang","doi":"10.1007/s12539-024-00650-x","DOIUrl":"10.1007/s12539-024-00650-x","url":null,"abstract":"As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor reflecting the severity of cardiovascular and cerebrovascular diseases, is imperative to detect the arteriosclerotic retinopathy. However, the detection of arteriosclerosis retinopathy requires expensive and time-consuming manual evaluation, while end-to-end deep learning detection methods also need interpretable design to high light task-related features. Considering the importance of automatic arteriosclerotic retinopathy grading, we propose a segmentation and classification interaction network (SCINet). We propose a segmentation and classification interaction architecture for grading arteriosclerotic retinopathy. After IterNet is used to segment retinal vessel from original fundus images, the backbone feature extractor roughly extracts features from the segmented and original fundus arteriosclerosis images and further enhances them through the vessel aware module. The last classifier module generates fundus arteriosclerosis grading results. Specifically, the vessel aware module is designed to highlight the important areal vessel features segmented from original images by attention mechanism, thereby achieving information interaction. The attention mechanism selectively learns the vessel features of segmentation region information under the proposed interactive architecture, which leads to reweighting the extracted features and enhances significant feature information. Extensive experiments have confirmed the effect of our model. SCINet has the best performance on the task of arteriosclerotic retinopathy grading. Additionally, the CNN method is scalable to similar tasks by incorporating segmented images as auxiliary information.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"926-935"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142107051","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}

引用次数: 0

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data. 基于对比学习的深度神经网络，通过整合多种光学数据进行癌症亚型分析

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-12-01 Epub Date: 2024-09-04 DOI: 10.1007/s12539-024-00641-y

Hua Chai, Weizhen Deng, Junyu Wei, Ting Guan, Minfan He, Yong Liang, Le Li

{"title":"A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.","authors":"Hua Chai, Weizhen Deng, Junyu Wei, Ting Guan, Minfan He, Yong Liang, Le Li","doi":"10.1007/s12539-024-00641-y","DOIUrl":"10.1007/s12539-024-00641-y","url":null,"abstract":"Background: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into tumor molecular subtyping. However, the high dimensionality and small sample size of the data may result in ambiguous and overlapping cancer subtypes during clustering. In this study, we propose a novel contrastive-learning-based approach to address this issue. The proposed end-to-end deep learning method can extract crucial information from the multi-omics features by self-supervised learning for patient clustering.Results: By applying our method to nine public cancer datasets, we have demonstrated superior performance compared to existing methods in separating patients with different survival outcomes (p < 0.05). To further evaluate the impact of various omics data on cancer survival, we developed an XGBoost classification model and found that mRNA had the highest importance score, followed by DNA methylation and miRNA. In the presented case study, our method successfully clustered subtypes and identified 14 cancer-related genes, of which 12 (85.7%) were validated through literature review.Conclusions: Our findings demonstrate that our method is capable of identifying cancer subtypes that are both statistically and biologically significant. The code about COLCS is given at: https://github.com/Mercuriiio/COLCS .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"966-975"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142125654","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}

引用次数: 0

A Deniable Encryption Method for Modulation-Based DNA Storage. 基于调制的 DNA 存储的可抵赖加密方法。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-12-01 Epub Date: 2024-08-19 DOI: 10.1007/s12539-024-00648-5

Ling Chu, Yanqing Su, Xiangzhen Zan, Wanmin Lin, Xiangyu Yao, Peng Xu, Wenbin Liu

{"title":"A Deniable Encryption Method for Modulation-Based DNA Storage.","authors":"Ling Chu, Yanqing Su, Xiangzhen Zan, Wanmin Lin, Xiangyu Yao, Peng Xu, Wenbin Liu","doi":"10.1007/s12539-024-00648-5","DOIUrl":"10.1007/s12539-024-00648-5","url":null,"abstract":"Recent advancements in synthesis and sequencing techniques have made deoxyribonucleic acid (DNA) a promising alternative for next-generation digital storage. As it approaches practical application, ensuring the security of DNA-stored information has become a critical problem. Deniable encryption allows the decryption of different information from the same ciphertext, ensuring that the \"plausible\" fake information can be provided when users are coerced to reveal the real information. In this paper, we propose a deniable encryption method that uniquely leverages DNA noise channels. Specifically, true and fake messages are encrypted by two similar modulation carriers and subsequently obfuscated by inherent errors. Experiment results demonstrate that our method not only can conceal true information among fake ones indistinguishably, but also allow both the coercive adversary and the legitimate receiver to decrypt the intended information accurately. Further security analysis validates the resistance of our method against various typical attacks. Compared with conventional DNA cryptography methods based on complex biological operations, our method offers superior practicality and reliability, positioning it as an ideal solution for data encryption in future large-scale DNA storage applications.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"872-881"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141999872","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}

引用次数: 0

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis. 利用脑电信号的可解释神经网络揭示大脑同步动态：应用于阅读障碍诊断。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-12-01 Epub Date: 2024-07-02 DOI: 10.1007/s12539-024-00634-x

Nicolás J Gallego-Molina, Andrés Ortiz, Juan E Arco, Francisco J Martinez-Murcia, Wai Lok Woo

{"title":"Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.","authors":"Nicolás J Gallego-Molina, Andrés Ortiz, Juan E Arco, Francisco J Martinez-Murcia, Wai Lok Woo","doi":"10.1007/s12539-024-00634-x","DOIUrl":"10.1007/s12539-024-00634-x","url":null,"abstract":"The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1005-1018"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11512920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification. 集合机器学习和预测特性促进了抗菌肽的鉴定。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-12-01 Epub Date: 2024-07-07 DOI: 10.1007/s12539-024-00640-z

Guolun Zhong, Hui Liu, Lei Deng

{"title":"Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.","authors":"Guolun Zhong, Hui Liu, Lei Deng","doi":"10.1007/s12539-024-00640-z","DOIUrl":"10.1007/s12539-024-00640-z","url":null,"abstract":"The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"951-965"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141544779","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}

引用次数: 0

Luteinizing Hormone Receptor Mutation (LHR^N316S) Causes Abnormal Follicular Development Revealed by Follicle Single-Cell Analysis and CRISPR/Cas9. 卵泡单细胞分析和 CRISPR/Cas9 发现促黄体生成素受体突变（LHRN316S）导致卵泡发育异常

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-12-01 Epub Date: 2024-08-16 DOI: 10.1007/s12539-024-00646-7

Chen Zhang, Yongqiang Nie, Bufang Xu, Chunlan Mu, Geng G Tian, Xiaoyong Li, Weiwei Cheng, Aijun Zhang, Dali Li, Ji Wu

{"title":"Luteinizing Hormone Receptor Mutation (LHRN316S) Causes Abnormal Follicular Development Revealed by Follicle Single-Cell Analysis and CRISPR/Cas9.","authors":"Chen Zhang, Yongqiang Nie, Bufang Xu, Chunlan Mu, Geng G Tian, Xiaoyong Li, Weiwei Cheng, Aijun Zhang, Dali Li, Ji Wu","doi":"10.1007/s12539-024-00646-7","DOIUrl":"10.1007/s12539-024-00646-7","url":null,"abstract":"Abnormal interaction between granulosa cells and oocytes causes disordered development of ovarian follicles. However, the interactions between oocytes and cumulus granulosa cells (CGs), oocytes and mural granulosa cells (MGs), and CGs and MGs remain to be fully explored. Using single-cell RNA-sequencing (scRNA-seq), we determined the transcriptional profiles of oocytes, CGs and MGs in antral follicles. Analysis of scRNA-seq data revealed that CGs may regulate follicular development through the BMP15-KITL-KIT-PI3K-ARF6 pathway with elevated expression of luteinizing hormone receptor (LHR). Because internalization of the LHR is regulated by Arf6, we constructed LHRN316S mice by CRISPR/Cas9 to further explore mechanisms of follicular development and novel treatment strategies for female infertility. Ovaries of LHRN316S mice exhibited reduced numbers of corpora lutea and ovulation. The LHRN316S mice had a reduced rate of oocyte maturation in vitro and decreased serum progesterone levels. Mating LHRN316S female mice with ICR wild type male mice revealed that the infertility rate of LHRN316S mice was 21.4% (3/14). Litter sizes from LHRN316S mice were smaller than those from control wild type female mice. The oocytes from LHRN316S mice had an increased rate of maturation in vitro after progesterone administration in vitro. Furthermore, progesterone treated LHRN316S mice produced offspring numbers per litter equivalent to WT mice. These findings provide key insights into cellular interactions in ovarian follicles and provide important clues for infertility treatment.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"976-989"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11512921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141987894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DP-ID: Interleaving and Denoising to Improve the Quality of DNA Storage Image. DP-ID：交错和去噪提高 DNA 存储图像的质量。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-11-22 DOI: 10.1007/s12539-024-00671-6

Qi Xu, Yitong Ma, Zuhong Lu, Kun Bi

{"title":"DP-ID: Interleaving and Denoising to Improve the Quality of DNA Storage Image.","authors":"Qi Xu, Yitong Ma, Zuhong Lu, Kun Bi","doi":"10.1007/s12539-024-00671-6","DOIUrl":"https://doi.org/10.1007/s12539-024-00671-6","url":null,"abstract":"In the field of storing images into DNA, the code tables and universal error correction codes have the potential to mitigate the effect of base errors to a certain extent. However, they prove to be ineffective in dealing with indels (insertion and deletion errors), resulting in a decline in information density and the quality of reconstructed image. This paper proposes a novel encoding and decoding method named DP-ID for storing images into DNA that improves information density and the quality of reconstructed image. Firstly, the image is compressed as bitstreams by the dynamic programming algorithm. Secondly, the bitstreams obtained are mapped to DNA, which are then interleaved. The reconstructed image is obtained by applying median filtering to remove salt-and-pepper noise. Simulation results show the reconstructed image by DP-ID at 5% error rate is better than that by other methods at 1% error rate. This robustness to high errors is compatible with the unsatisfied biological constraints caused by high information density. Wet experiments show that DP-ID can reconstruct high quality image at 5X sequencing depth. The high information density and low sequencing depth significantly reduce the cost of DNA storage, facilitating the large-scale storage of images into DNA.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692977","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}

引用次数: 0

Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach. 基于深度学习方法发现靶向 TLR4 治疗肝脏炎症疾病的银翘解毒片有效成分

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-11-19 DOI: 10.1007/s12539-024-00670-7

Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu

{"title":"Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.","authors":"Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu","doi":"10.1007/s12539-024-00670-7","DOIUrl":"10.1007/s12539-024-00670-7","url":null,"abstract":"Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulating the Toll-like receptor 4 (TLR4) target. Therefore, a thorough exploration of the effective components and therapeutic mechanisms targeting TLR4 in YCHD is a promising strategy for liver diseases. In this study, the AIGO-DTI deep learning framework was proposed to predict the targeting probability of major components in YCHD for TLR4. Comparative evaluations with four machine learning models (RF, SVM, KNN, XGBoost) and two deep learning models (GCN, GAT) demonstrated that the AIGO-DTI framework exhibited the best overall performance, with Recall and AUC reaching 0.968 and 0.991, respectively.This study further utilized the AIGO-DTI model to identify the potential impact of Isoscopoletin, a major component of YCHD, on TLR4. Subsequent wet experiments revealed that Isoscopoletin could influence the maturation of Dendritic Cells (DCs) induced by Lipopolysaccharide (LPS) through TLR4, suggesting its therapeutic potential for liver diseases, especially hepatitis. Additionally, based on the AIGO-DTI framework, this study established an online platform named TLR4-Predict to facilitate domain experts in discovering more compounds related to TLR4. Overall, the proposed AIGO-DTI framework accurately predicts unique compounds in YCHD that interact with TLR4, providing new insights for identifying and screening lead compounds targeting TLR4.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667815","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}

引用次数: 0

iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming. iAmyP：基于序列最小二乘法编程的淀粉样蛋白六肽识别多视角学习。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2024-11-15 DOI: 10.1007/s12539-024-00666-3

Jinling Cai, Jianping Zhao, Yannan Bin, Junfeng Xia, Chunhou Zheng

{"title":"iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming.","authors":"Jinling Cai, Jianping Zhao, Yannan Bin, Junfeng Xia, Chunhou Zheng","doi":"10.1007/s12539-024-00666-3","DOIUrl":"https://doi.org/10.1007/s12539-024-00666-3","url":null,"abstract":"The development of peptide drug is hindered by the risk of amyloidogenic aggregation; if peptides tend to aggregate in this manner, they may be unsuitable for drug design. Computational methods aimed at predicting amyloidogenic sequences often face challenges in extracting high-quality features, and their predictive performance can be enchanced. To surmount these challenges, iAmyP was introduced as a specialized computational tool designed for predicting amyloidogenic hexapeptides. Utilizing multi-view learning, iAmyP incorporated sequence, structural, and evolutionary features, performing feature selection and feature fusion through recursive feature elimination and attention mechanisms. This amalgamation of features and subsequent feature selection and fusion lead to optimal performance facilitated by an optimization algorithm based on sequence least squares programming. Notably, iAmyP exhibited robust generalization for peptides with lengths of 7-10 amino acids. The role of hydrophobic amino acids in the aggregation process is critical, and a thorough analysis have significantly enhanced our insight into their significance in amyloidogenic hexapeptides. This tool represented an advancement in the development of peptide therapeutics by providing an understanding of amyloidogenic aggregation, establishing itself as a valuable framework for assessing amyloidogenic sequences. The data and code can be freely accessed at https://github.com/xialab-ahu/iAmyP .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638847","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}

引用次数: 0