Acute Respiratory Distress Syndrome (ARDS) is an illness that typically develops in people who are significantly ill or have serious injuries. ARDS is characterized by fluid build-up that occurs in the alveoli. T-cells are implicated as playing a role in the modulation of the aberrant response leading to excessive tissue damage and, eventually, ARDS. Complementarity Determining Region 3 (CDR3) sequences derived from T-cells are key players in the adaptive immune response. This response is governed by an elaborate specificity for distinct molecules and the ability to recognize and vigorously respond to repeated exposures to the same molecules. Most of the diversity in T-cell receptors (TCRs) is contained in the CDR3 regions of the heterodimeric cell-surface receptors. For this study, we employed the novel technology of immune sequencing to assess lung edema fluid. Our goal was to explore the landscape of CDR3 clonal sequences found within these samples. We obtained more than 3615 CDR3 sequences across samples in the study. Our data demonstrate that: (1) CDR3 sequences from lung edema fluid exhibit distinct clonal populations, and (2) CDR3 sequences can be further characterized based on biochemical features. Analysis of these CDR3 sequences offers insight into the CDR3-driven T-cell repertoire of ARDS. These findings represent the first step towards applications of this technology with these types of biological samples in the context of ARDS.

SARS-CoV-2 is a newly discovered virus which causes COVID-19 (coronavirus disease of 2019), initially documented as a human pathogen in 2019 in the city of Wuhan China, has now quickly spread across the globe with an urgency to develop effective treatments for the virus and emerging variants. Therefore, to identify potential therapeutics, an antiviral catalogue of compounds from the CAS registry, a division of the American Chemical Society was evaluated using a pharmacoinformatics approach. A total of 49,431 compounds were initially recovered. After a biological and chemical curation, only 23,575 remained. A machine learning approach was then used to identify potential compounds as inhibitors of SARS-CoV-2 based on a training dataset of molecular descriptors and fingerprints of known reported compounds to have favorable interactions with SARS-CoV-2. This approach identified 178 compounds, however, a molecular docking analysis revealed only 39 compounds with strong binding to active sites. Downstream molecular analysis of four of these compounds revealed various non-covalent interactions along with simultaneous modulation between ligand and protein active site pockets. The pharmacological profiles of these compounds showed potential drug-likeness properties. Our work provides a list of candidate anti-viral compounds that may be used as a guide for further investigation and therapeutic development against SARS-CoV-2.

No-boundary thinking enables the scientific community to reflect in a thoughtful manner and discover new opportunities, create innovative solutions, and break through barriers that might have otherwise constrained their progress. This concept encourages thinking without being confined by traditional rules, limitations, or established norms, and a mindset that is not limited by previous work, leading to fresh perspectives and innovative outcomes. So, where do we see the field of artificial intelligence (AI) in bioinformatics going in the next 30 years? That was the theme of a “No-Boundary Thinking” Session as part of the Mid-South Computational Bioinformatics Society’s (MCBIOS) 19th annual meeting in Irving, Texas. This session addressed various areas of AI in an open discussion and raised some perspectives on how popular tools like ChatGPT can be integrated into bioinformatics, communicating with scientists in different fields to properly utilize the potential of these algorithms, and how to continue educational outreach to further interest of data science and informatics to the next-generation of scientists.

A Data Report on the Curation and Development of a Database of Genes for Acute Respiratory Distress Syndrome

The purpose of the meeting described in this review was to decide how best to ensure the sustainability of the Network for Integrating Bioinformatics into Life Science Education (NIBLSE; pronounced “nibbles”). Biology research today generates large and complex datasets, and the analysis of these datasets is becoming increasingly critical to progress in the field. The long-term goal of NIBLSE is to address this need and achieve the full integration of bioinformatics into undergraduate life sciences education. Meeting participants supported several next steps for NIBLSE, including further development and dissemination of bioinformatics learning resources through our novel incubators and Faculty Mentoring Networks, vigorously pursuing assessment strategies for our learning resources, connecting learning resources with open educational resource (OER) textbooks, learning more about barriers to bioinformatics implementation for underrepresented groups, and developing future workshops and meetings. About half the participants at the meeting were newcomers to NIBLSE, a positive sign for the future. NIBLSE has many exciting opportunities available, and we welcome life science educators with any level of bioinformatics expertise as new members.

The integration of Machine Learning (ML) and Artificial Intelligence (AI) is rapidly transforming biological research, providing sophisticated tools to analyze complex data, enhance precision, and navigate ethical considerations. This editorial summarizes five critical areas where AI is driving advancement, from foundational ethical shifts to deep prognostic insights in oncology.Manju V et al. discussed the foundational role of AI in ethical biomedical research. AI's role transcends mere computational efficiency; it is a chief facilitator in ensuring humane and efficacious science by adhering to the "3Rs": Replacement, Reduction, and Refinement, of animal-based research. This paper describes how traditional animal models have inherent limitations, including translational gaps, regulatory issues, and ethical controversies. AI provides the sophisticated analytical power necessary for predictions, simulations, and validations, minimizing reliance on animal subjects. By processing massive, complex datasets, machine and deep learning algorithms can simulate human biology, forecast therapy outcomes, and discover candidate drugs, thereby supporting Replacement and promoting Reduction through maximized experimental designs. This transition, however, necessitates strict validation requirements and ethical controls to ensure the reliability and integrity of the resulting models.Carreira et al. focused their research work at driving precision diagnostics in Polymicrobial Diseases. One immediate challenge in biomedicine is the accurate classification of polymicrobial diseases caused by microbial community imbalance (dysbiosis), where 16S rRNA gene sequence data is highly dimensional and heterogeneous. To address this, the curated pipeline EPheClass was developed, utilizing ensemble-based ML models (including k-nearest neighbours (kNN), Random Forest (RF), Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)) for binary phenotype classification. The methodology described in this article emphasizes rigorous procedures for reliability and reproducibility, unlike earlier studies criticized for insufficient sample size or lack of proper validation. Key data processing steps include Centred Log-Ratio transformation (CLR) for compositional data and Recursive Feature Elimination (RFE) for feature selection. This approach prioritizes model parsimony, demonstrating high predictive performance with a dramatically reduced number of features. For instance, using the Dynamic Ensemble Selection-Performance (DES-P) technique, EPheClass achieved an impressive Area Under the Curve (AUC) of 0.973 for diagnosing periodontal disease (PD) in saliva samples using just 13 features. The pipeline's versatility was confirmed by successfully diagnosing Inflammatory Bowel Disease (IBD) (using 26 features) and classifying antibiotic exposure (DA) (using 22 features), demonstrating its generalization across different phenotypes and sample types.The goal of this research was to unravel cross-omics interactions, specifically Predicting miRNA from mRNA. The authors addressed a gap within the lack of publicly available paired datasets containing both miRNA and mRNA expression profiles. The authors' evaluation process consisted of seven paired datasets related to viral infections, specifically West Nile Virus (WNV) and Human Immunodeficiency Virus (HIV). Overall, both DNNs and LASSO models achieved strong correlations at the level of individual samples. However, DNNs proved superior in capturing predictive changes relevant to differential expression analysis (DEA). Specifically, cross-study validation using HIV datasets yielded strong correlations for log-fold changes (log2FCs) derived from DEA (R=0.59), demonstrating the model's ability to generalize to independent data of the same tissue type. Furthermore, data augmentation, specifically adding Gaussian noise, consistently improved the performance of the neural networks, helping mitigate the challenge of small sample sizes. Conversely, linear LASSO models, despite their strong sample-level performance, struggled to translate this accuracy into meaningful correlations for DEA log2FCs, suggesting the non-linear capability of DNNs is better suited for complex cross-omics relationships.The authors presented a powerful computational framework for Lung Adenocarcinoma (LUAD) Prognosis. This framework integrated multi-omics data (transcriptomic, DNA methylation, and somatic mutation data) with 10 clustering algorithms to identify three robust molecular subtypes (CS1, CS2, and CS3) associated with distinct clinical prognoses (CS3 having the best prognosis). Leveraging 10 ML algorithms in 101 unique combinations, researchers constructed the PIGRS (Lasso + GBM ensemble) prognostic model based on 15 immune-associated programmed cell death genes (PIRGs). PIGRS demonstrated strong prognostic efficacy across multiple cohorts, outperforming almost all previously published LUAD prognostic models. The model linked high PIGRS scores to increased genomic instability, including higher Tumor Mutational Burden (TMB) and intra-tumor heterogeneity (MATH scores), and suggested a relationship with immune escape. Subsequent experimental validation showed that knockdown of PSME3, significantly inhibited LUAD cell proliferation, migration, and invasion, and promoted apoptosis likely by affecting the PI3K/AKT/Bcl-2 signaling pathway.The authors focused on innate immune cell barrier-related genes to inform prognosis for pancreatic cancer (PC). Using 14 machine learning algorithms, the CDRG-RSF model (Random Survival Forest trained on risk genes) was established as the most robust prognostic tool, achieving excellent long-term predictive performance with 3-year and 5-year AUCs exceeding 0.7 in validation cohorts. High-risk PC patients exhibited elevated TMB and reduced infiltration of anti-tumor cytotoxic cells, specifically NK and CD8+ T cells. The model offered...