SMILE: systems metabolomics using interpretable learning and evolution

Multivariate analyses and machine learning link sex and age with antibody responses to SARS-CoV-2 and vaccination

Prevention of negative COVID-19 infection outcomes is associated with the quality of antibody responses, whose variance by age and sex is poorly understood. Network approaches identified sex and age effects in antibody responses and neutralization potential of de novo infection and vaccination throughout the COVID-19 pandemic. Neutralization values followed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific receptor binding immunoglobulin G (RIgG), spike immunoglobulin G (SIgG) and spike and receptor immunoglobulin G (S, and RIgA) levels based on COVID-19 status. Serum immunoglobulin A (IgA) antibody titers correlated with neutralization only in females 40-60 years old (y.o.). Network analysis found males could improve IgA responses after vaccination dose 2. Complex correlation analyses found vaccination induced less antibody isotype switching and neutralization in older persons, especially in females. Sex-dependent antibody and neutralization decayed the fastest in older males. Shown sex and age characterization can direct studies integrating cell-mediated responses to define yet elusive correlates of protection and inform age and sex precision-focused vaccine design.

Investigating the Potential of Essential Oils from Citrus reticulata Leaves in Mitigating Memory Decline and Oxidative Stress in the Scopolamine-Treated Zebrafish Model

Petitgrain essential oil (PGEO) is derived from the water distillation process on mandarin (Citrus reticulata) leaves. The chemical constituents of PGEO were analyzed by gas chromatography/mass spectrometry (GC/MS) method which revealed the presence of six compounds (100%). The major peaks were for methyl-N-methyl anthranilate (89.93%) and γ-terpinene (6.25%). Over 19 days, zebrafish (Tubingen strain) received PGEO (25, 150, and 300 μL/L) before induction of cognitive impairment with scopolamine immersion (SCOP, 100 μM). Anxiety-like behavior and memory of the zebrafish were assessed by a novel tank diving test (NTT), Y-maze test, and novel object recognition test (NOR). Additionally, the activity of acetylcholinesterase (AChE) and the extent of the brain's oxidative stress were explored. In conjunction, in silico forecasts were used to determine the pharmacokinetic properties of the principal compounds discovered in PGEO, employing platforms such as SwissADME, Molininspiration, and pKCSM. The findings provided evidence that PGEO possesses the capability to enhance memory by AChE inhibition, alleviate SCOP-induced anxiety during behavioral tasks, and diminish brain oxidative stress.

Interpretable machine learning methods for predictions in systems biology from omics data

Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability?” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome

OBJECTIVE

The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.

METHODS

The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model.

RESULTS

Eighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase.

CONCLUSION

The model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.

Machine learning predictive models for acute pancreatitis: A systematic review

INTRODUCTION

Acute pancreatitis (AP) is a common clinical pancreatic disease. Patients with different severity levels have different clinical outcomes. With the advantages of algorithms, machine learning (ML) has gradually emerged in the field of disease prediction, assisting doctors in decision-making.

METHODS

A systematic review was conducted using the PubMed, Web of Science, Scopus, and Embase databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Publication time was limited from inception to 29 May 2021. Studies that have used ML to establish predictive tools for AP were eligible for inclusion. Quality assessment of the included studies was conducted in accordance with the IJMEDI checklist.

RESULTS

In this systematic review, 24 of 2,913 articles, with a total of 8,327 patients and 47 models, were included. The studies could be divided into five categories: 10 studies (42%) reported severity prediction; 10 studies (42%), complication prediction; 3 studies (13%), mortality prediction; 2 studies (8%), recurrence prediction; and 2 studies (8%), surgery timing prediction. ML showed great accuracy in several prediction tasks. However, most of the included studies were retrospective in nature, conducted at a single centre, based on database data, and lacked external validation. According to the IJMEDI checklist and our scoring criteria, two studies were considered to be of high quality. Most studies had an obvious bias in the quality of data preparation, validation, and deployment dimensions.

CONCLUSION

In the prediction tasks for AP, ML has shown great potential in assisting decision-making. However, the existing studies still have some deficiencies in the process of model construction. Future studies need to optimize the deficiencies and further evaluate the comparability of the ML systems and model performance, so as to consequently develop high-quality ML-based models that can be used in clinical practice.