Journal of Integrative Bioinformatics最新文献

Colon cancer survival prediction from gland shapes within histology slides using deep learning. 利用深度学习从组织学切片中的腺体形状预测结肠癌存活。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-07-14 DOI: 10.1515/jib-2024-0052

Rawan Gedeon, Atulya Nagar

引用次数: 0

Editorial - 20 years Journal of Integrative Bioinformatics. 编辑- 20 年整合生物信息学杂志。

IF 1.8

Journal of Integrative Bioinformatics Pub Date : 2025-07-09 eCollection Date: 2025-03-01 DOI: 10.1515/jib-2025-0034

Ralf Hofestädt

引用次数: 0

Sustainable software development in science - insights from 20 years of Vanted. 科学中的可持续软件开发-来自20 年Vanted的见解。

IF 1.8

Journal of Integrative Bioinformatics Pub Date : 2025-07-01 eCollection Date: 2025-03-01 DOI: 10.1515/jib-2025-0007

Falk Schreiber, Tobias Czauderna, Dimitar Garkov, Niklas Gröne, Karsten Klein, Matthias Lange, Uwe Scholz, Björn Sommer

引用次数: 0

Metagenome and metabolome study on inhaled corticosteroids in asthma patients with side effects. 哮喘患者吸入皮质类固醇副作用的宏基因组和代谢组研究。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-06-24 DOI: 10.1515/jib-2024-0062

Igor Goryanin, Anatoly Sorokin, Meder Seitov, Berik Emilov, Muktarbek Iskakov, Irina Goryanin, Batyr Osmonov

引用次数: 0

Leveraging transformers for semi-supervised pathogenicity prediction with soft labels. 利用变压器进行软标签的半监督致病性预测。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-06-23 DOI: 10.1515/jib-2024-0047

Pablo Enrique Guillem, Marco Zurdo-Tabernero, Noelia Egido Iglesias, Ángel Canal-Alonso, Liliana Durón Figueroa, Guillermo Hernández, Angélica González-Arrieta, Fernando de la Prieta

{"title":"Leveraging transformers for semi-supervised pathogenicity prediction with soft labels.","authors":"Pablo Enrique Guillem, Marco Zurdo-Tabernero, Noelia Egido Iglesias, Ángel Canal-Alonso, Liliana Durón Figueroa, Guillermo Hernández, Angélica González-Arrieta, Fernando de la Prieta","doi":"10.1515/jib-2024-0047","DOIUrl":"10.1515/jib-2024-0047","url":null,"abstract":"The rapid advancement of Next-Generation Sequencing (NGS) technologies has revolutionized the field of genomics, producing large volumes of data that necessitate sophisticated analytical techniques. This paper introduces a Deep Learning model designed to predict the pathogenicity of genetic variants, a vital component in advancing personalized medicine. The model is trained on a dataset derived from the analysis of NGS outputs, containing a combination of well-defined and ambiguous genetic variants. By employing a semi-supervised learning approach, the model efficiently utilizes both confidently labeled and less certain data. At the core of the methodology is the Feature Tokenizer Transformer architecture, which processes both numerical and categorical genomic information. The preprocessing pipeline includes key steps such as data imputation, scaling, and encoding to ensure high data quality. The results highlight the model's impressive accuracy, particularly in detecting confidently labeled variants, while also addressing the impact of its predictions on less certain (soft-labeled) data.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Petri net modeling and simulation of post-transcriptional regulatory networks of human embryonic stem cell (hESC) differentiation to cardiomyocytes. 人胚胎干细胞（hESC）向心肌细胞分化的转录后调控网络的Petri网建模和模拟。

IF 1.8

Journal of Integrative Bioinformatics Pub Date : 2025-06-23 eCollection Date: 2025-03-01 DOI: 10.1515/jib-2024-0037

Aruana F F Hansel-Fröse, Christoph Brinkrolf, Marcel Friedrichs, Bruno Dallagiovanna, Lucia Spangenberg

{"title":"Petri net modeling and simulation of post-transcriptional regulatory networks of human embryonic stem cell (hESC) differentiation to cardiomyocytes.","authors":"Aruana F F Hansel-Fröse, Christoph Brinkrolf, Marcel Friedrichs, Bruno Dallagiovanna, Lucia Spangenberg","doi":"10.1515/jib-2024-0037","DOIUrl":"10.1515/jib-2024-0037","url":null,"abstract":"Stem cells are capable of self-renewal and differentiation into various cell types, showing significant potential for cellular therapies and regenerative medicine, particularly in cardiovascular diseases. The differentiation to cardiomyocytes replicates the embryonic heart development, potentially supporting cardiac regeneration. Cardiomyogenesis is controlled by complex post-transcriptional regulation that affects the construction of gene regulatory networks (GRNs), such as: alternative polyadenylation (APA), length changes in untranslated regulatory regions (3'UTRs), and microRNA (miRNA) regulation. To deepen our understanding of the cardiomyogenesis process, we have modeled a GRN for each day of cardiomyocyte differentiation. Then, each GRN was automatically transformed by four transformation rules to a Petri net and simulated using the software VANESA. The Petri nets highlighted the relationship between genes and alternative isoforms, emphasizing the inhibition of miRNA on APA isoforms with varying 3'UTR lengths. Moreover, in silico simulation of miRNA knockout enabled the visualization of the consequential effects on isoform expression. Our Petri net models provide a resourceful tool and holistic perspective to investigate the functional orchestra of transcript regulation that differentiate hESCs to cardiomyocytes. Additionally, the models can be adapted to investigate post-transcriptional GRN in other biological contexts.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12327202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating AI and genomics: predictive CNN models for schizophrenia phenotypes. 整合人工智能和基因组学：预测精神分裂症表型的CNN模型。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-06-18 DOI: 10.1515/jib-2024-0057

Guilherme Henriques, Maryam Abbasi, Daniel Martins, Joel P Arrais

引用次数: 0

Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques. 使用Faster R-CNN和染色技术在染色的组织病理学图像中自动检测有丝分裂。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-06-11 DOI: 10.1515/jib-2024-0049

Jesús García-Salmerón, José Manuel García, Gregorio Bernabé, Pilar González-Férez

{"title":"Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques.","authors":"Jesús García-Salmerón, José Manuel García, Gregorio Bernabé, Pilar González-Férez","doi":"10.1515/jib-2024-0049","DOIUrl":"https://doi.org/10.1515/jib-2024-0049","url":null,"abstract":"Accurate mitosis detection is essential for cancer diagnosis and treatment. Traditional manual counting by pathologists is time-consuming and may cause errors. This research investigates automated mitosis detection in stained histopathological images using Deep Learning (DL) techniques, particularly object detection models. We propose a two-stage object detection model based on Faster R-CNN to effectively detect mitosis within histopathological images. The stain augmentation and normalization techniques are also applied to address the significant challenge of domain shift in histopathological image analysis. The experiments are conducted using the MIDOG++ dataset, the most recent dataset from the MIDOG challenge. This research builds on our previous work, in which two one-stage frameworks, in particular on RetinaNet using fastai and PyTorch, are proposed. Our results indicate favorable F1-scores across various scenarios and tumor types, demonstrating the effectiveness of the object detection models. In addition, Faster R-CNN with stain techniques provides the most accurate and reliable mitosis detection, while RetinaNet models exhibit faster performance. Our results highlight the importance of handling domain shifts and the number of mitotic figures for robust diagnostic tools.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach. 稀疏PCA和叠加集合法预测ddi诱导妊娠和新生儿不良反应。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-06-10 DOI: 10.1515/jib-2024-0056

Anushka Chaurasia, Deepak Kumar, Yogita

{"title":"Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach.","authors":"Anushka Chaurasia, Deepak Kumar, Yogita","doi":"10.1515/jib-2024-0056","DOIUrl":"https://doi.org/10.1515/jib-2024-0056","url":null,"abstract":"Predicting Drug-Drug interaction (DDI)-induced adverse drug reactions (ADRs) using computational methods is challenging due to the availability of limited data samples, data sparsity, and high dimensionality. The issue of class imbalance further increases the intricacy of prediction. Different computational techniques have been presented for predicting DDI-induced ADRs in the general population; however, the area of DDI-induced pregnancy and neonatal ADRs has been underexplored. In the present work, a sparse ensemble-based computational approach is proposed that leverages SMILES strings as features, addresses high-dimensional and sparse data using Sparse Principal Component Analysis (SPCA), mitigates class imbalance with the Multilabel Synthetic Minority Oversampling Technique (MLSMOTE), and predicts pregnancy and neonatal ADRs through a stacking ensemble model. The SPCA has been evaluated for handling sparse data and shown 2.67 %-5.45 % improvement compared to PCA. The proposed stacking ensemble model has outperformed six state-of-the-art predictors regarding micro and macro scores for True Positive Rate (TPR), F1 Score, False Positive Rate (FPR), Precision, Hamming Loss, and ROC-AUC Score with 1.16 %-14.94 %.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset. 一个基于vitunet的模型，使用YOLOv8进行高效的LVNC诊断和数据集的自动清理。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-06-04 DOI: 10.1515/jib-2024-0048

Salvador de Haro, Gregorio Bernabé, José Manuel García, Pilar González-Férez

{"title":"A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset.","authors":"Salvador de Haro, Gregorio Bernabé, José Manuel García, Pilar González-Férez","doi":"10.1515/jib-2024-0048","DOIUrl":"https://doi.org/10.1515/jib-2024-0048","url":null,"abstract":"Left ventricular non-compaction is a cardiac condition marked by excessive trabeculae in the left ventricle's inner wall. Although various methods exist to measure these structures, the medical community still lacks consensus on the best approach. Previously, we developed DL-LVTQ, a tool based on a UNet neural network, to quantify trabeculae in this region. In this study, we expand the dataset to include new patients with Titin cardiomyopathy and healthy individuals with fewer trabeculae, requiring retraining of our models to enhance predictions. We also propose ViTUNeT, a neural network architecture combining U-Net and Vision Transformers to segment the left ventricle more accurately. Additionally, we train a YOLOv8 model to detect the ventricle and integrate it with ViTUNeT model to focus on the region of interest. Results from ViTUNet and YOLOv8 are similar to DL-LVTQ, suggesting dataset quality limits further accuracy improvements. To test this, we analyze MRI images and develop a method using two YOLOv8 models to identify and remove problematic images, leading to better results. Combining YOLOv8 with deep learning networks offers a promising approach for improving cardiac image analysis and segmentation.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0