Journal of Integrative Bioinformatics

Editors-in-Chief: Falk Schreiber
Falk Schreiber

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and Björn Sommer
Björn Sommer

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Edited by: Jan Baumbach
Jan Baumbach

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, Ming Chen
Ming Chen

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, Yuriy Orlov
Yuriy Orlov

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and Jens Allmer
Jens Allmer

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Editorial Board: Alejandro Giorgetti
Alejandro Giorgetti

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, Andrew Harrison
Andrew Harrison

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, Aleksey Kochetov
Aleksey Kochetov

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, Jens Krüger
Jens Krüger

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, Qi Ma
Qi Ma

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, Hiroshi Matsuno
Hiroshi Matsuno

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, Chanchal K. Mitra
Chanchal K. Mitra

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, Josch K. Pauling
Josch K. Pauling

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, Chris Rawlings
Chris Rawlings

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, Florentino Fdez-Riverola
Florentino Fdez-Riverola

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, Paolo Romano
Paolo Romano

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, Richard Röttger
Richard Röttger

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, Alban Shoshi
Alban Shoshi

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, Siomar de Castro Soares
Siomar de Castro Soares

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, Jan Taubert
Jan Taubert

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, Andreas Tauch
Andreas Tauch

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, Malik Yousef
Malik Yousef

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, Stephan Weise
Stephan Weise

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and Keywan Hassani-Pak
Keywan Hassani-Pak

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Language: English

First published: January 1, 2017

Publication Frequency: 4 issues per year

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About this journal

Objective
Journal of Integrative Bioinformatics (JIB) is an international open access journal publishing original peer-reviewed research articles in all aspects of integrative bioinformatics.

Molecular biology produces huge amounts of data in the post-genomic era. This includes data describing metabolic mechanisms and pathways, structural genomic organization, patterns of regulatory regions; proteomics, transcriptomics, and metabolomics. On the one hand, analysis of this data uses essentially the methods and concepts of computer science; on the other hand, the range of biological tasks solved by researchers determines the range and scope of the data. Currently, there are about 1,000 database systems and various analytical tools available via the Internet which are directed at solving various biological tasks.

The challenge we have is to integrate these list-parts and relationships from genomics and proteomics at novel levels of understanding. Integrative Bioinformatics is a new area of research using the tools of computer science and electronic infrastructure applied to Biotechnology. These tools will also represent the backbone of the concept of a virtual cell.

Topics

Software applications/tools and databases covering the following topics:

Molecular Databases, Information Systems and Data Warehouses
Integration of Data, Methods and Tools
Metabolic and Regulatory Network Modeling and Simulation
Signal Pathways and Cell Control
Network Analysis
Medical Informatics, Biomedicine and Biotechnology
Integrative Approaches for Drug Design
Integrative Data and Text Mining Approaches
Integrative, whole cell and molecular modeling
Visualization and animation

Review papers are also welcome with regard to JIB.tools.

Article formats
Research articles, Review papers, Workshop contributions (if peer-reviewed)

Article processing charges (APCs)

Effective 1^st February, 2025, each unsolicited article, which is accepted for publication in the Journal of Integrative Bioinformatics is subject to an Article Processing Charge of 1,050€.
The Open Access publication of invited articles for Special Issues is sponsored by the editors.

Inquiries concerning APCs should be addressed to the Editorial Office at De Gruyter (see contact details below).

Your Benefits

Dear Authors

We invite your submissions to the Journal of Integrative Bioinformatics (JIB). Please adhere to our instructions for authors and submit your paper online via our submission site at: https://mc.manuscriptcentral.com/dgjib.

Your benefits:

Research using the tools of computer science and electronic infrastructure applied to Biotechnology
High quality single-blind peer-review
International and renowned editorial board
100% Open Access: authors retain the copyright for their work

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History

JIB was launched in 2004 as the official Journal of the IMBio association (FG Informationsmanagement in der Biotechnologie of the German Society of Computer Science).

Open Access
Colon cancer survival prediction from gland shapes within histology slides using deep learning

July 14, 2025

Rawan Gedeon, Atulya Nagar

Article number: 20240052

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This study investigates the application of deep learning techniques for segmenting glands in histopathological images of colorectal cancer. We trained two convolutional neural network models, U-Net and DCAN, on a combination of the GlaS and CRAG datasets to enhance generalization across diverse histological appearances, selecting DCAN for its superior accuracy in delineating gland boundaries. The goal was to achieve robust gland segmentation applicable to whole slide images (WSIs) from The Cancer Genome Atlas (TCGA). Using the segmented glands, we extracted patient-level morphological features and used them to predict survival outcomes. A Cox proportional hazards model was trained on these features and achieved a high concordance index, indicating strong predictive performance. Patients were then stratified into high- and low-risk groups, with significant differences in survival distributions (log-rank p -value: 0.01317). In addition, we benchmarked our models against state-of-the-art gland segmentation methods on GlaS and CRAG, highlighting the trade-off between domain-specific accuracy and cross-dataset robustness.
Open Access
Metagenome and metabolome study on inhaled corticosteroids in asthma patients with side effects

June 24, 2025

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

Article number: 20240062

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This study investigates the gut microbiome and metabolome of asthma patients treated with inhaled corticosteroids (ICS), some of whom experience adverse side effects. We analyzed stool samples from 24 participants, divided into three cohorts: asthma patients with side effects, those without, and healthy controls. Using next-generation sequencing and LC-MS/MS metabolomics, we identified significant differences in bacterial species and metabolites. Multi-Omics Factor Analysis (MOFA) and Global Sensitivity Analysis-Partial Rank Correlation Coefficient (GSA-PRCC) provided insights into key contributors to side effects, such as tryptophan depletion and altered linolenate and glucose-1-phosphate levels. The study proposes dietary or probiotic interventions to mitigate side effects. Despite the limited sample size, these findings provide a basis for personalized asthma management approaches. Further studies are required to confirm initial fundings.
Open Access
Leveraging transformers for semi-supervised pathogenicity prediction with soft labels

June 23, 2025

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

Article number: 20240047

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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.
Open Access
Integrating AI and genomics: predictive CNN models for schizophrenia phenotypes

June 18, 2025

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

Article number: 20240057

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This study explores the use of deep learning to analyze genetic data and predict phenotypic traits associated with schizophrenia, a complex psychiatric disorder with a strong hereditary component yet incomplete genetic characterization. We applied Convolutional Neural Networks models to a large-scale case-control exome sequencing dataset from the Swedish population to identify genetic patterns linked to schizophrenia. To enhance model performance and reduce overfitting, we employed advanced optimization techniques, including dropout layers, learning rate scheduling, batch normalization, and early stopping. Following systematic refinements in data preprocessing, model architecture, and hyperparameter tuning, the final model achieved an accuracy of 80 %. These results demonstrate the potential of deep learning approaches to uncover intricate genotype-phenotype relationships and support their future integration into precision medicine and genetic diagnostics for psychiatric disorders such as schizophrenia.
Open Access
Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques

June 11, 2025

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

Article number: 20240049

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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.
Open Access
Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach

June 10, 2025

Anushka Chaurasia, Deepak Kumar, Yogita

Article number: 20240056

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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 %.
Open Access
A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset

June 4, 2025

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

Article number: 20240048

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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.
Open Access
Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma

June 3, 2025

Jose María Belmonte, Miguel Blanquer, Gregorio Bernabé, Fernando Jiménez, José Manuel García

Article number: 20240053

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This paper investigates the application of Survival Analysis (SA) techniques to forecast outcomes after autologous Hematopoietic Stem Cell Transplantation (aHSCT) for Multiple Myeloma (MM). By leveraging six SA models, we examine their predictive capabilities, measured through the Concordance Index (C-index) metric. Beyond evaluating model performance, we analyze feature importance using permutation and SHAP methods, highlighting key clinical factors such as treatment history, disease stage, and prior disease progression or relapse as critical predictors of survival. The findings suggest that while all models performed well based on the C-index, a detailed examination revealed variations in how each model processed data. Specifically, the Coxnet and Random Survival Forest models exhibited a more thorough use of clinical variables, whereas the gradient boosting models appeared to rely on a narrower range of features, potentially limiting their ability to differentiate between patients with comparable profiles. Risk predictions categorized patients into low, moderate, and high-risk levels. For lower-risk patients, the procedure showed positive outcomes, while higher-risk individuals were predicted to have limited survival benefits, recommending alternative treatments. Lastly, we propose future research to expand these models into time-to-event estimations, offering additional support for decision-making by predicting patient life expectancy post-transplant, considering their pre-transplant clinical attributes.
Open Access
Towards a more accurate and reliable evaluation of machine learning protein–protein interaction prediction model performance in the presence of unavoidable dataset biases

April 2, 2025

Alba Nogueira-Rodríguez, Daniel Glez-Peña, Cristina P. Vieira, Jorge Vieira, Hugo López-Fernández

Article number: 20240054

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The characterization of protein-protein interactions (PPIs) is fundamental to understand cellular functions. Although machine learning methods in this task have historically reported prediction accuracies up to 95 %, including those only using raw protein sequences, it has been highlighted that this could be overestimated due to the use of random splits and metrics that do not take into account potential biases in the datasets. Here, we propose a per-protein utility metric, pp_MCC, able to show a drop in the performance in both random and unseen-protein splits scenarios. We tested ML models based on sequence embeddings. The pp_MCC metric evidences a reduced performance even in a random split, reaching levels similar to those shown by the raw MCC metric computed over an unseen protein split, and drops even further when the pp_MCC is used in an unseen protein split scenario. Thus, the metric is able to give a more realistic performance estimation while allowing to use random splits, which could be interesting for more protein-centric studies. Given the low adjusted performance obtained, there seems to be room for improvement when using only primary sequence information, suggesting the need of inclusion of complementary protein data, accompanied with the use of the pp_MCC metric.
Open Access
Fcodes update: a kinship encoding framework with F-Tree GUI & LLM inference

March 31, 2025

Daniel Pérez-Rodríguez, Roberto C. Agís-Balboa, Hugo López-Fernández

Article number: 20240046

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Family structures play a crucial role in personal development, social dynamics, and mental health. Traditional systems for encoding genealogical data, such as Ahnentafel and the Register System, offer methods to document lineage but face limitations, particularly in accommodating horizontal relationships or handling changes in family datasets. Modern computational systems like LINKAGE and PED, while powerful for genetic analysis, lack human readability and are challenging to apply in fields where unstructured, narrative data is common, such as sociology or psychiatry. This paper aims to bridge this gap by enhancing Fcodes, a flexible and intuitive algorithm for encoding kinship relationships that is suited for both manual and computational use. Building on our previous work, we present improvements to the Fcodes core algorithm and command-line interface (CLI), as well as the development of F-Tree, a new graphical user interface (GUI) to streamline the encoding process. Additionally, we introduce a method for estimating the coefficient of inbreeding using Fcodes and explore the application of artificial intelligence, namely large language models (LLMs), to automatically infer family relationships from narrative text. These advancements highlight the potential of Fcodes in a wide range of research contexts, from social studies to genetics and mental health research.
Open Access
Predicting precursors of plant specialized metabolites using DeepMol automated machine learning

March 20, 2025

João Capela, João Cheixo, Dick de Ridder, Oscar Dias, Miguel Rocha

Article number: 20240050

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Plants produce specialized metabolites, which play critical roles in defending against biotic and abiotic stresses. Due to their chemical diversity and bioactivity, these compounds have significant economic implications, particularly in the pharmaceutical and agrotechnology sectors. Despite their importance, the biosynthetic pathways of these metabolites remain largely unresolved. Automating the prediction of their precursors, derived from primary metabolism, is essential for accelerating pathway discovery. Using DeepMol’s automated machine learning engine, we found that regularized linear classifiers offer optimal, accurate, and interpretable models for this task, outperforming state-of-the-art models while providing chemical insights into their predictions. The pipeline and models are available at the repository: https://github.com/jcapels/SMPrecursorPredictor.
Open Access
Immunoinformatics-guided design of a multiepitope peptide vaccine targeting the receptor-binding domain of SARS-CoV-2 spike glycoprotein: insights from Indonesian samples

January 14, 2025

Irvan Faizal, Darrian Chandra, Tarwadi, Sabar Pambudi, Astutiati Nurhasanah, Rizky Priambodo, Muhammad Yusuf

Article number: 20240025

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The emergence of new variants of SARS-CoV-2, including Alpha, Beta, Gamma, Delta, Omicron variants, and XBB sub-variants, contributes to the number of coronavirus cases worldwide. SARS-CoV-2 is a positive RNA virus with a genome of 29.9 kb that encodes four structural proteins: spike glycoprotein (S), envelope glycoprotein (E), membrane glycoprotein (M), and nucleocapsid glycoprotein (N). These proteins are vital for viral activity, with the S protein facilitating attachment and membrane fusion through the receptor-binding domain (RBD) located in the S1 subunit. The RBD recognizes and binds to the human angiotensin-converting enzyme 2 (ACE-2) protein. An immunoinformatic-aided design of a peptide-based multiepitope vaccine candidate targeting the RBD glycoprotein is constructed from the SARS-CoV-2 sequence data base from various regions of Indonesia (Jakarta, West Java, and Bali). The results show that the RBD region of with accession ID EPI_ISL_15982641 from West Java had the highest antigenicity of 0.5904. This isolate is non-toxic and non-allergenic and shows a total of 18 LBL epitopes, 72 CTL epitopes, and 98 HTL epitopes. The epitope that has the best overall binding affinity was GCHNKCAY for MHC-I and GGCVFSYVGCHNKCAYWV for MHC-II which show a binding affinity of −13.6 and −15.5 (kcal/mol), respectively. Therefore, this study aims to design an epitope vaccine candidate based on samples from Indonesia that has good characteristics and may have the potential to stimulate an immune response against SARS-CoV-2.

Issue 1

Special Issue: 20 Years Journal of Integrative Bioinformatics; Guest Editor: Ralf Hofestädt

Journal of Integrative Bioinformatics publishes special and thematic issues focussed on important and emerging topics in the field of study. The journal has established a rigorous process to ensure that any special issue manuscripts follow the same high-quality standards and peer review processes as regular manuscripts. For further information on the journal’s peer review policy please see the "Instructions for Authors".

Recent Special Issues

Journal Impact Factor	1.8	2024, Journal Citation Reports (Clarivate, 2025)
5-year Journal Impact Factor	2.8	2024, Journal Citation Reports (Clarivate, 2025)
Journal Citation Indicator	0.45	2024, Journal Citation Reports (Clarivate, 2025)
CiteScore	3.3	2024, Scopus (Elsevier B.V., 2025)
SCImago Journal Rank	0.417	2024, SJR (Scimago Lab, 2025; Data Source: Scopus)

More information about journal rankings

Submission
You can easily submit your manuscript online. Simply go to https://mc.manuscriptcentral.com/dgjib and you will be guided through the whole peer-reviewing and publishing process.

Your benefits of publishing with us

High-quality single-blind review process (min. 2 reviewers per manuscript)
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
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Submission process

Submission of your paper via our submission management tool
Peer review process (you will be guided through every step)
Decision on your paper
Online publication

Please note

The journal will only accept papers, which present new tools or new aspects of already JIB published tools. Furthermore, these tools must be available and usable via the internet and free of charge for academician. For accepted review publications you are invited to join JIBtools as an editor for a new topic
In general, the contribution must be clearly structured. This is especially important for long manuscripts. The following arrangement/structure is recommended: Summary, Introduction, Related works, Architecture/Implementation, Application, Discussion, References
It is possible to submit two different types of documents: Microsoft Word or LaTeX, whereas LaTeX is recommended
Before submitting your article please have a look at our Publication Ethics Statement and our Editorial Policy
All authors, agree that the article, if editorially accepted for publication, shall be licensed under the CC BY 4.0 license.
If you have any general questions please visit our FAQ page for authors

We look forward to receiving your manuscript!

Editors-in-Chief
Prof. Dr. Falk Schreiber, University of Konstanz, Konstanz, Germany
Dr. Björn Sommer, Royal College of Art, London, UK

Associate Editors
Jens Allmer, Wageningen University & Research, Wageningen, Netherlands
Jan Baumbach, Technical University of Munich, Munich, Germany
Ming Chen, Zhejiang University, Hangzhou, China
Yuriy L. Orlov, Sechnov University - Medical University, Moscow, Russia

Editorial Board
Alejandro Giorgetti, University of Verona, Verona, Italy
Keywan Hassani-Pak, Rothamsted Research, Harpenden, UK
Andrew Harrison, University of Essex, Essex, UK
Aleksey Kochetov, Institute of Cytology and Genetics, Novosibirsk, Russia
Jens Krüger, Tübingen University, Tübingen, Germany
Qi Ma, University of California, San Diego, USA
Hiroshi Matsuno, Yamaguchi University, Yamaguchi, Japan
Chanchal K. Mitra, University of Hyderabad, Hyderabad, India
Josch K. Pauling, Humboldt University, Berlin, Germany
Chris Rawlings, Rothamsted Research, Harpenden, UK
Florentino Fdez-Riverola, University of Vigo, Vigo, Spain
Paolo Romano, IRCCS AOU San Martino IST, Genoa, Italy
Richard Röttger, University of Southern Denmark, Odense, Denmark
Alban Shoshi, Bielefeld University, Bielefeld, Germany
Siomar de Castro Soares, Universidade Federal do Triângulo Mineiro, Uberaba, Brazil
Björn Sommer, University of Konstanz, Konstanz, Germany
Jan Taubert, KWS SAAT AG, Einbeck, Germany
Andreas Tauch, CeBiTec Bielefeld, Bielefeld, Germany
Stephan Weise, IPK Gatersleben, Gatersleben, Germany
Malik Yousef, Zefat Academic College, Zefat, Israel