Computational Intelligence and Bioinformatics (CIB)

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

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

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

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

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

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

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

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