Joint embedding-classifier learning for interpretable collaborative filtering.

Background: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion.

Results: We introduce the novel Joint Embedding Learning-classifier for improved Interpretability (JELI).

Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing.

Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities.

Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters.

Introduction: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the current Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, and possibly the implementation of additional criteria, is needed.

Aim: This study aimed to identify previously unknown and potentially specific features of malnutrition in patients with different chronic gastrointestinal diseases and to validate the relevance of the GLIM criteria for clinical practice using machine learning (ML).

Regulatory Role of IL6 in Immune-Related Adverse Events during Checkpoint Inhibitor Treatment in Melanoma.

The landscape of clinical management for metastatic melanoma (MM) and other solid tumors has been modernized by the advent of immune checkpoint inhibitors (ICI), including programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors. While these agents demonstrate efficacy in suppressing tumor growth, they also lead to immune-related adverse events (irAEs), resulting in the exacerbation of autoimmune diseases such as rheumatoid arthritis (RA), ulcerative colitis (UC), and Crohn's disease (CD). The immune checkpoint inhibitors offer promising advancements in the treatment of melanoma and other cancers, but they also present significant challenges related to irAEs and autoimmune diseases.

Bitter peptide prediction using graph neural networks.

Bitter taste is an unpleasant taste modality that affects food consumption. Bitter peptides are generated during enzymatic processes that produce functional, bioactive protein hydrolysates or during the aging process of fermented products such as cheese, soybean protein, and wine. Understanding the underlying peptide sequences responsible for bitter taste can pave the way for more efficient identification of these peptides.

Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

Background: Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers.

Methods: We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach.

Sunset Yellow protects against oxidative damage and exhibits chemoprevention in chemically induced skin cancer model.

Skin cancer and other skin-related inflammatory pathologies are rising due to heightened exposure to environmental pollutants and carcinogens. In this context, natural products and repurposed compounds hold promise as novel therapeutic and preventive agents. Strengthening the skin's antioxidant defense mechanisms is pivotal in neutralizing reactive oxygen species (ROS) and mitigating oxidative stress.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms.

Exploring Thiazolopyridine AV25R: Unraveling of Biological Activities, Selective Anti-Cancer Properties and In Silico Target and Binding Prediction in Hematological Neoplasms.

Thiazolopyridines are a highly relevant class of small molecules, which have previously shown a wide range of biological activities. Besides their anti-tubercular, anti-microbial and anti-viral activities, they also show anti-cancerogenic properties, and play a role as inhibitors of cancer-related proteins. Herein, the biological effects of the thiazolopyridine AV25R, a novel small molecule with unknown biological effects, were characterized.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components.

Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma.

The discovery of potential disease-causing genes can aid medical progress. The post-genomic era has made this a more difficult task. Modern high-throughput methods have not solved the problem of identifying disease genes.

CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation.

Background: The immune response is a crucial factor for mediating the benefit of cardiac cell therapies. Our previous research showed that cardiomyocyte transplantation alters the cardiac immune response and, when combined with short-term pharmacological CCR2 inhibition, resulted in diminished functional benefit. However, the specific role of innate immune cells, especially CCR2 macrophages on the outcome of cardiomyocyte transplantation, is unclear.

Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others.

Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut.

Introduction: Obese rodents e.g., the leptin-deficient (ob/ob) mouse exhibit remarkable behavioral changes and are therefore ideal models for evaluating mental disorders resulting from obesity.

Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model.

Background: Modifying the acute inflammatory response has wide clinical benefits. Current options include non-steroidal anti-inflammatory drugs (NSAIDs) and therapies that may resolve inflammation. Acute inflammation involves multiple cell types and various processes.

Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System.

Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown.

Uncoupling protein 2 deficiency of non-cancerous tissues inhibits the progression of pancreatic cancer in mice.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a disease of the elderly mostly because its development from preneoplastic lesions depends on the accumulation of gene mutations and epigenetic alterations over time. How aging of non-cancerous tissues of the host affects tumor progression, however, remains largely unknown.

Methods: We took advantage of a model of accelerated aging, uncoupling protein 2-deficient (Ucp2 knockout, Ucp2 KO) mice, to investigate the growth of orthotopically transplanted Ucp2 wild-type (WT) PDAC cells (cell lines Panc02 and 6606PDA) in vivo and to study strain-dependent differences of the PDAC microenvironment.

investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia.

Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient's nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level.

Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology.

Is data-driven analysis sufficient for understanding the COVID-19 pandemic and for justifying public health regulations? In this paper, we argue that such analysis is insufficient. Rather what is needed is the identification and implementation of over-arching hypothesis-related and/or theory-based rationales to conduct effective SARS-CoV2/COVID-19 (Corona) research. To that end, we analyse and compare several published recommendations for conceptual and methodological frameworks in medical research (e.

Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care.

AI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized decision-making support based on patient data has driven the motivation of researchers in the medical domain for more than a decade, but the overall sparsity and scarcity of data are still major limitations. This is in contrast to currently applied technology that allows us to generate and analyze patient data in diverse forms, such as tabular data on health records, medical images, genomics data, or even audio and video.

The Effects of Hypoxic Preconditioned Murine Mesenchymal Stem Cells on Post-Infarct Arrhythmias in the Mouse Model.

Ventricular arrhythmias associated with myocardial infarction (MI) have a significant impact on mortality in patients following heart attack. Therefore, targeted reduction of arrhythmia represents a therapeutic approach for the prevention and treatment of severe events after infarction. Recent research transplanting mesenchymal stem cells (MSC) showed their potential in MI therapy.