Computational Intelligence and Bioinformatics (CIB)

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
BioMedInformatics biomedinformatics	-	1.7	2021	22 Days	CHF 1000	Submit
BioTech biotech	2.7	3.7	2012	26.1 Days	CHF 1600	Submit
Genes genes	2.8	5.2	2010	14.9 Days	CHF 2600	Submit
Computation computation	1.9	3.5	2013	18.6 Days	CHF 1800	Submit

18 pages, 4984 KiB

Open AccessArticle

A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases

by Diego Mariano

Appl. Sci. 2025, 15(9), 4839; https://doi.org/10.3390/app15094839 - 27 Apr 2025

Viewed by 67

Thermostable β-glucosidases (E.C. 3.2.1.21) are essential enzymes used in second-generation biofuel production. However, little is known about the structural characteristics that lead to their thermostability. In this study, I used graph-based structural signatures to represent three-dimensional structures of β-glucosidase enzymes extracted from thermophilic [...] Read more.

Thermostable β-glucosidases (E.C. 3.2.1.21) are essential enzymes used in second-generation biofuel production. However, little is known about the structural characteristics that lead to their thermostability. In this study, I used graph-based structural signatures to represent three-dimensional structures of β-glucosidase enzymes extracted from thermophilic organisms. I collected 1717 structures from thermophilic (n = 890) and non-thermophilic (n = 827) organisms and divided them into two datasets: training (n = 1134) and test (n = 583). I then used seven machine learning algorithms to classify them. The best model achieved 77.1% accuracy using logistic regression in training with 10-fold cross-validation and 81.6% accuracy in testing using the CatBoost algorithm. I hypothesize that the signature model proposed here can help understand the structural patterns in thermostable enzymes and shed light on the design of more efficient enzymes for biofuel production. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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18 pages, 1301 KiB

Open AccessArticle

Prediction of Lupus Classification Criteria via Generative AI Medical Record Profiling

by Sandeep Nair, Gerald H. Lushington, Mohan Purushothaman, Bernard Rubin, Eldon Jupe and Santosh Gattam

BioTech 2025, 14(1), 15; https://doi.org/10.3390/biotech14010015 - 6 Mar 2025

Viewed by 607

Abstract

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that poses serious long-term patient burdens. (1) Background: SLE patient classification and care are often complicated by case heterogeneity (diverse variations in symptoms and severity). Large language models (LLMs) and generative artificial intelligence (genAI) [...] Read more.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that poses serious long-term patient burdens. (1) Background: SLE patient classification and care are often complicated by case heterogeneity (diverse variations in symptoms and severity). Large language models (LLMs) and generative artificial intelligence (genAI) may mitigate this challenge by profiling medical records to assess key medical criteria. (2) Methods: To demonstrate genAI-based profiling, ACR (American College of Rheumatology) 1997 SLE classification criteria were used to define medically relevant LLM prompts. Records from 78 previously studied patients (45 classified as having SLE; 33 indeterminate or negative) were computationally profiled, via five genAI replicate runs. (3) Results: GenAI determinations of the “Discoid Rash” and “Pleuritis or Pericarditis” classification criteria yielded perfect concurrence with clinical classification, while some factors such as “Immunologic Disorder” (56% accuracy) were statistically unreliable. Compared to clinical classification, our genAI approach achieved a 72% predictive success rate. (4) Conclusions: GenAI classifications may prove sufficiently predictive to aid medical professionals in evaluating SLE patients and structuring care strategies. For individual criteria, accuracy seems to correlate inversely with complexities in clinical determination, implying that improvements in AI patient profiling tools may emerge from continued advances in clinical classification efficacy. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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36 pages, 25720 KiB

Open AccessArticle

Early Breast Cancer Detection Based on Deep Learning: An Ensemble Approach Applied to Mammograms

by Youness Khourdifi, Alae El Alami, Mounia Zaydi, Yassine Maleh and Omar Er-Remyly

BioMedInformatics 2024, 4(4), 2338-2373; https://doi.org/10.3390/biomedinformatics4040127 - 13 Dec 2024

Viewed by 2183

Abstract

Background: Breast cancer is one of the leading causes of death in women, making early detection through mammography crucial for improving survival rates. However, human interpretation of mammograms is often prone to diagnostic errors. This study addresses the challenge of improving the [...] Read more.

Background: Breast cancer is one of the leading causes of death in women, making early detection through mammography crucial for improving survival rates. However, human interpretation of mammograms is often prone to diagnostic errors. This study addresses the challenge of improving the accuracy of breast cancer detection by leveraging advanced machine learning techniques. Methods: We propose an extended ensemble deep learning model that integrates three state-of-the-art convolutional neural network (CNN) architectures: VGG16, DenseNet121, and InceptionV3. The model utilizes multi-scale feature extraction to enhance the detection of both benign and malignant masses in mammograms. This ensemble approach is evaluated on two benchmark datasets: INbreast and CBIS-DDSM. Results: The proposed ensemble model achieved significant performance improvements. On the INbreast dataset, the ensemble model attained an accuracy of 90.1%, recall of 88.3%, and an F1-score of 89.1%. For the CBIS-DDSM dataset, the model reached 89.5% accuracy and 90.2% specificity. The ensemble method outperformed each individual CNN model, reducing both false positives and false negatives, thereby providing more reliable diagnostic results. Conclusions: The ensemble deep learning model demonstrated strong potential as a decision support tool for radiologists, offering more accurate and earlier detection of breast cancer. By leveraging the complementary strengths of multiple CNN architectures, this approach can improve clinical decision making and enhance the accessibility of high-quality breast cancer screening. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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21 pages, 1695 KiB

Open AccessCommunication

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

by Ankush U. Patel, Qiangqiang Gu, Ronda Esper, Danielle Maeser and Nicole Maeser

BioMedInformatics 2024, 4(2), 1363-1383; https://doi.org/10.3390/biomedinformatics4020075 - 17 May 2024

Cited by 7 | Viewed by 4674

Abstract

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly [...] Read more.

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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25 pages, 3665 KiB

Open AccessArticle

Survey of Multimodal Medical Question Answering

by Hilmi Demirhan and Wlodek Zadrozny

BioMedInformatics 2024, 4(1), 50-74; https://doi.org/10.3390/biomedinformatics4010004 - 31 Dec 2023

Cited by 8 | Viewed by 4174

Abstract

Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, applying bibliometric analysis to the publications and datasets used in [...] Read more.

Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, applying bibliometric analysis to the publications and datasets used in these studies. Our analysis uncovers the increasing interest in MMQA over time, with diverse domains such as natural language processing, computer vision, and large language models contributing to the research. The AI methods used in multimodal question answering in the medical domain are a prominent focus, accompanied by applicability of MMQA to the medical field. MMQA in the medical field has its unique challenges due to the sensitive nature of medicine as a science dealing with human health. The survey reveals MMQA research to be in an exploratory stage, discussing different methods, datasets, and potential business models. Future research is expected to focus on application development by big tech companies, such as MedPalm. The survey aims to provide insights into the current state of multimodal medical question answering, highlighting the growing interest from academia and industry. The identified research gaps and trends will guide future investigations and encourage collaborative efforts to advance this transformative field. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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18 pages, 9093 KiB

Open AccessArticle

Identifying the Role of Disulfidptosis in Endometrial Cancer via Machine Learning Methods

by Fei Fu, Xuesong Lu, Zhushanying Zhang, Zhi Li and Qinlan Xie

BioMedInformatics 2023, 3(4), 908-925; https://doi.org/10.3390/biomedinformatics3040056 - 13 Oct 2023

Cited by 1 | Viewed by 1474

Abstract

Uterine corpus endometrial carcinoma (UCEC) is the second most common gynecological cancer in the world. With the increased occurrence of UCEC and the stagnation of research in the field, there is a pressing need to identify novel UCEC biomarkers. Disulfidptosis is a novel [...] Read more.

Uterine corpus endometrial carcinoma (UCEC) is the second most common gynecological cancer in the world. With the increased occurrence of UCEC and the stagnation of research in the field, there is a pressing need to identify novel UCEC biomarkers. Disulfidptosis is a novel form of cell death, but its role in UCEC is unclear. We integrate differential analysis and the XGBoost algorithm to determine a disulfidptosis-related characteristic gene (DRCG), namely LRPPRC. By prediction and verification based on online databases, we construct a regulatory network of ceRNA in line with the scientific hypothesis, including a ceRNA regulatory axis and two mRNA-miRNA regulatory axes, i.e., mRNA LRPPRC/miRNA hsa-miR-616-5p/lncRNA TSPEAR-AS2, mRNA LRPPRC/miRNA hsa-miR-4658, and mRNA LRPPRC/miRNA hsa-miR-6783-5p. We use machine learning methods such as GBM to screen out seven disulfidptosis-related characteristic lncRNAs (DRCLs) as predictors, and build a risk prediction model with good prediction ability. SCORE = (1.136*LINC02449) + (−2.173*KIF9-AS1) +

(-

0.235*ACBD3-AS1) + (1.830*AL354892.3) + (−1.314*AC093677.2) + (0.636*AC113361.1) + (−0.589*CDC37L1-DT). The ROC curve shows that in the training set samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.804, 0.724, 0.719, and 0.846, respectively. In the test set samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.615, 0.657, 0.687, and 0.702, respectively. In all samples, the AUCs for predicting 1-, 3-, 6-, and 10-year OS are 0.752, 0.706, 0.705, and 0.834, respectively. CP724714 has been screened as a potential therapy option for individuals who have a high risk of developing UCEC. Two subtypes of disulfidptosis-related genes (DRGs) and two subtypes of DRCLs are obtained by NMF method. We find that subtype N1 of DRGs is mainly enriched in various metabolic pathways, and subtype N1 may play a significant role in the process of disulfidptosis. Our study confirms for the first time that disulfidptosis plays a role in UCEC. Our findings help improve the prognosis and treatment of UCEC. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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23 pages, 9945 KiB

Open AccessArticle

Identification of a New Drug Binding Site in the RNA-Dependent-RNA-Polymerase (RdRp) Domain

by Aparna S. Gana and James N. Baraniuk

BioMedInformatics 2023, 3(4), 885-907; https://doi.org/10.3390/biomedinformatics3040055 - 10 Oct 2023

Cited by 3 | Viewed by 2404

Abstract

We hypothesize that in silico structural biology approaches can discover novel drug binding sites for RNA-dependent-RNA-polymerases (RdRp) of positive sense single-strand RNA (ss(+)RNA) virus species. RdRps have a structurally conserved active site with seven motifs (A to G), despite low sequence similarity. We [...] Read more.

We hypothesize that in silico structural biology approaches can discover novel drug binding sites for RNA-dependent-RNA-polymerases (RdRp) of positive sense single-strand RNA (ss(+)RNA) virus species. RdRps have a structurally conserved active site with seven motifs (A to G), despite low sequence similarity. We refined this architecture further to describe a conserved structural domain consisting of motifs A, B, C and F. These motifs were used to realign 24 RdRp structures in an innovative manner to search for novel drug binding sites. The aligned motifs from the enzymes were then docked with 833 FDA-approved drugs (Set 1) and 85 FDA-approved antivirals (Set 2) using the Molecular Operating Environment (MOE) docking 2020.09 software. Sirolimus (rapamycin), an immunosuppressant that targets the mammalian mTOR pathway, was one of the top ten drugs for all 24 RdRp proteins. The sirolimus docking site was in the nucleotide triphosphate entry tunnel between motifs A and F but distinct from the active site in motif C. This original finding supports our hypothesis that structural biology approaches based on RdRp motifs that are conserved across evolution can define new drug binding locations and infer potential broad-spectrum inhibitors for SARS-CoV-2 and other ss(+)RNA viruses. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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17 pages, 2455 KiB

Open AccessReview

DNA Data Storage

by Tomasz Buko, Nella Tuczko and Takao Ishikawa

BioTech 2023, 12(2), 44; https://doi.org/10.3390/biotech12020044 - 1 Jun 2023

Cited by 4 | Viewed by 8724

Abstract

The demand for data storage is growing at an unprecedented rate, and current methods are not sufficient to accommodate such rapid growth due to their cost, space requirements, and energy consumption. Therefore, there is a need for a new, long-lasting data storage medium [...] Read more.

The demand for data storage is growing at an unprecedented rate, and current methods are not sufficient to accommodate such rapid growth due to their cost, space requirements, and energy consumption. Therefore, there is a need for a new, long-lasting data storage medium with high capacity, high data density, and high durability against extreme conditions. DNA is one of the most promising next-generation data carriers, with a storage density of 10¹⁹ bits of data per cubic centimeter, and its three-dimensional structure makes it about eight orders of magnitude denser than other storage media. DNA amplification during PCR or replication during cell proliferation enables the quick and inexpensive copying of vast amounts of data. In addition, DNA can possibly endure millions of years if stored in optimal conditions and dehydrated, making it useful for data storage. Numerous space experiments on microorganisms have also proven their extraordinary durability in extreme conditions, which suggests that DNA could be a durable storage medium for data. Despite some remaining challenges, such as the need to refine methods for the fast and error-free synthesis of oligonucleotides, DNA is a promising candidate for future data storage. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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17 pages, 14497 KiB

Open AccessArticle

A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites

by Xueru Zhao, Furong Chang, Hehe Lv, Guobing Zou and Bofeng Zhang

Appl. Sci. 2023, 13(5), 3247; https://doi.org/10.3390/app13053247 - 3 Mar 2023

Cited by 2 | Viewed by 3898

Abstract

The cell cycle and biological processes rely on RNA and RNA-binding protein (RBP) interactions. It is crucial to identify the binding sites on RNA. Various deep-learning methods have been used for RNA-binding site prediction. However, they cannot extract the hierarchical features of the [...] Read more.

The cell cycle and biological processes rely on RNA and RNA-binding protein (RBP) interactions. It is crucial to identify the binding sites on RNA. Various deep-learning methods have been used for RNA-binding site prediction. However, they cannot extract the hierarchical features of the RNA secondary structure. Therefore, this paper proposes HPNet, which can automatically identify RNA-binding sites and -binding preferences. HPNet performs feature learning from the two perspectives of the RNA sequence and the RNA secondary structure. A convolutional neural network (CNN), a deep-learning method, is used to learn RNA sequence features in HPNet. To capture the hierarchical information for RNA, we introduced DiffPool into HPNet, a differentiable pooling graph neural network (GNN). A CNN and DiffPool were combined to improve the binding site prediction accuracy by leveraging both RNA sequence features and hierarchical features of the RNA secondary structure. Binding preferences can be extracted based on model outputs and parameters. Overall, the experimental results showed that HPNet achieved a mean area under the curve (AUC) of 94.5% for the benchmark dataset, which was more accurate than the state-of-the-art methods. Moreover, these results demonstrate that the hierarchical features of RNA secondary structure play an essential role in selecting RNA-binding sites. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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16 pages, 10779 KiB

Open AccessArticle

Identification of Potential Antimalarial Drug Candidates Targeting Falcipain-2 Protein of Malaria Parasite—A Computational Strategy

by Shrikant Nema, Kanika Verma, Ashutosh Mani, Neha Shree Maurya, Archana Tiwari and Praveen Kumar Bharti

BioTech 2022, 11(4), 54; https://doi.org/10.3390/biotech11040054 - 30 Nov 2022

Cited by 6 | Viewed by 4497

Abstract

Falcipain-2 (FP-2) is one of the main haemoglobinase of P. falciparum which is an important molecular target for the treatment of malaria. In this study, we have screened alkaloids to identify potential inhibitors against FP-2 since alkaloids possess great potential as anti-malarial agents. [...] Read more.

Falcipain-2 (FP-2) is one of the main haemoglobinase of P. falciparum which is an important molecular target for the treatment of malaria. In this study, we have screened alkaloids to identify potential inhibitors against FP-2 since alkaloids possess great potential as anti-malarial agents. A total of 340 alkaloids were considered for the study using a series of computational pipelines. Initially, pharmacokinetics and toxicity risk assessment parameters were applied to screen compounds. Subsequently, molecular docking algorithms were utilised to understand the binding efficiency of alkaloids against FP-2. Further, oral toxicity prediction was done using the pkCSM tool, and 3D pharmacophore features were analysed using the PharmaGist server. Finally, MD simulation was performed for Artemisinin and the top 3 drug candidates (Noscapine, Reticuline, Aclidinium) based on docking scores to understand the functional impact of the complexes, followed by a binding site interaction residues study. Overall analysis suggests that Noscapine conceded good pharmacokinetics and oral bioavailability properties. Also, it showed better binding efficiency with FP-2 when compared to Artemisinin. Interestingly, structure alignment analysis with artemisinin revealed that Noscapine, Reticuline, and Aclidinium might possess similar biological action. Molecular dynamics and free energy calculations revealed that Noscapine could be a potent antimalarial agent targeting FP-2 that can be used for the treatment of malaria and need to be studied experimentally in the future. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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10 pages, 1941 KiB

Open AccessArticle

Identification of Key Endoplasmic Reticulum Stress-Related Genes in Non-Alcoholic Fatty Liver Disease

by Zhuang Li, Haozhen Yu and Jun Li

BioMedInformatics 2022, 2(3), 424-433; https://doi.org/10.3390/biomedinformatics2030027 - 19 Aug 2022

Cited by 1 | Viewed by 2629

Abstract

Background: Endoplasmic reticulum stress (ERS) is involved in the etiology of non-alcoholic fatty liver disease (NAFLD). Thus, the current study was designed to identify key ERS-associated genes in NAFLD. Methods: RNA-Seq data of NAFLD and controls were sourced from the Gene Expression Omnibus [...] Read more.

Background: Endoplasmic reticulum stress (ERS) is involved in the etiology of non-alcoholic fatty liver disease (NAFLD). Thus, the current study was designed to identify key ERS-associated genes in NAFLD. Methods: RNA-Seq data of NAFLD and controls were sourced from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in NAFLD and controls were identified by limma. By overlapping DEGs and ERS-related genes, ERS-related DEGs were identified. The function of ERS-related DEGs was characterized by clusterProfiler. Next, the protein–protein interaction (PPI) network was created using the Cytoscape software and the STRING database to identify key ERS-related genes in NAFLD. Furthermore, the correlations among key ERS-related genes were calculated. Results: A total of 8965 DEGs were identified between NAFLD and controls in the GSE126848 dataset. After overlapping these DEGs and ERS-related genes, 20 genes were identified as ERS-related DEGs in NAFLD. Functional analysis revealed that the genes mainly participated in ER-related functions, such as the ER–nucleus signaling pathway, regulation of ERS response, and protein processing in ER. The PPI network revealed the interactions among 17 ERS-related DEGs, including ERN1, ATF6, and EIF2S1 as the key genes. The expressions of ERN1, ATF6, and EIF2S1 were significantly down-regulated in NAFLD and were strongly positively correlated with each other. Further, the expression of ERN1 and ATFA6 was also similar in the GSE89632 datasets. Conclusion: The present study identified ERN1, ATF6, and EIF2S1 as key ERS-related genes in NAFLD. These findings may provide a molecular basis for the role of ERS in NAFLD. Full article

(This article belongs to the Topic Computational Intelligence and Bioinformatics (CIB))

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