Centralized and Federated Models for the Analysis of Clinical Data.

The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components.

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science.

Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Environmental Protection Agency's Adverse Outcome Pathway Database (AOP-DB; aopdb.epa.gov) and the UK Biobank's genetic data repository. We use the AOP-DB to extract disease-specific AOPs and build graph neural networks used in our final analyses. We use the UK Biobank to retrieve real-world genotype and phenotype data, where genotypes are based on single nucleotide polymorphism data extracted from the AOP-DB, and phenotypes are case/control cohorts for the disease of interest (liver cancer) corresponding to those adverse outcome pathways. We also use propensity score matching to appropriately sample based on important covariates (demographics, comorbidities, and social deprivation indices) and to balance the case and control populations in our machine language training/testing datasets. Finally, we describe a novel putative risk factor for LC that depends on genetic variation in both the aryl-hydrocarbon receptor (AHR) and ATP binding cassette subfamily B member 11 (ABCB11) genes.

Strategic actions to advance multipurpose prevention technologies in low- and middle-income countries.

Background: HIV, other sexually transmitted infections (STIs) and unintended pregnancies are critical and interlinked health risks for millions of women of reproductive age worldwide. Multipurpose prevention technologies (MPTs) offer an innovative approach for expanding combined pregnancy and/or disease prevention. So far, MPT development efforts have focused mostly on HIV prevention, but about half of product candidates comprise compounds active against non-HIV STIs as well. This review aims to provide a framework that promotes the efficient advancement of the most promising preclinical products through the development pathway and into the hands of end-users, with a focus on women in low- and middle-income countries (L/MICs). Methods: This mini review provides a summary of the current landscape of the MPT field. It comprises a landscape assessment of MPTs in development, complemented by a series of 28 in-depth, semi-structured key informant interviews (KIIs) with experts representing different L/MIC perspectives. Main results: We identified six primary action strategies to advance MPTs for L/MICs, including identification of key research gaps and priorities. For each action strategy, progress to date and key recommendations are included. Conclusions: To realize the life-saving potential of MPTs and maximize the momentum made to date, a strategic, collaborative and well-funded response to the gaps and next steps outlined in this paper is critical. A coordinated response can add rigor and efficiency to the development process, to successfully advance the most promising MPT products to the hands of end-users.

Knowledge graph aids comprehensive explanation of drug and chemical toxicity.

In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State-of-the-art models are either limited by low accuracy, or lack of interpretability due to their black-box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical-gene connections, gene-pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.

Discovering Venom-Derived Drug Candidates Using Differential Gene Expression.

Venoms are a diverse and complex group of natural toxins that have been adapted to treat many types of human disease, but rigorous computational approaches for discovering new therapeutic activities are scarce. We have designed and validated a new platform-named VenomSeq-to systematically identify putative associations between venoms and drugs/diseases via high-throughput transcriptomics and perturbational differential gene expression analysis. In this study, we describe the architecture of VenomSeq and its evaluation using the crude venoms from 25 diverse animal species and 9 purified teretoxin peptides. By integrating comparisons to public repositories of differential expression, associations between regulatory networks and disease, and existing knowledge of venom activity, we provide a number of new therapeutic hypotheses linking venoms to human diseases supported by multiple layers of preliminary evidence.

A clinical model to predict postoperative improvement in sub-domains of the modified Japanese Orthopedic Association score for degenerative cervical myelopathy.

PURPOSE: The modified Japanese Orthopedic Association (mJOA) score consists of six sub-domains and is used to quantify the severity of cervical myelopathy. The current study aimed to assess for predictors of postoperative mJOA sub-domains scores following elective surgical management for patients with cervical myelopathy and develop the first clinical prediction model for 12-month mJOA sub-domain scores.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Byron F.] Last name [Stephens], Author 2 Given name: [Lydia J.] Last name [McKeithan], Author 3 Given name: [W. Hunter] Last name [Waddell], Author 4 Given name: [Anthony M.] Last name [Steinle], Author 5 Given name: [Wilson E.] Last name [Vaughan], Author 6 Given name: [Jacquelyn S.] Last name [Pennings], Author 7 Given name: [Jacquelyn S.] Last name [Pennings], Author 8 Given name: [Scott L.] Last name [Zuckerman], Author 9 Given name: [Kristin R.] Last name [Archer], Author 10 Given name: [Amir M.] Last name [Abtahi] Also, kindly confirm the details in the metadata are correct.Last Author listed should be Kristin R. Archer METHODS: A multivariable proportional odds ordinal regression model was developed for patients with cervical myelopathy. The model included patient demographic, clinical, and surgery covariates along with baseline sub-domain scores. The model was internally validated using bootstrap resampling to estimate the likely performance on a new sample of patients. RESULTS: The model identified mJOA baseline sub-domains to be the strongest predictors of 12-month scores, with numbness in legs and ability to walk predicting five of the six mJOA items. Additional covariates predicting three or more items included age, preoperative anxiety/depression, gender, race, employment status, duration of symptoms, smoking status, and radiographic presence of listhesis. Surgical approach, presence of motor deficits, number of surgical levels involved, history of diabetes mellitus, workers' compensation claim, and patient insurance had no impact on 12-month mJOA scores. CONCLUSION: Our study developed and validated a clinical prediction model for improvement in mJOA scores at 12 months following surgery. The results highlight the importance of assessing preoperative numbness, walking ability, modifiable variables of anxiety/depression, and smoking status. This model has the potential to assist surgeons, patients, and families when considering surgery for cervical myelopathy. LEVEL OF EVIDENCE: Level III.

Knowledge-guided deep learning models of drug toxicity improve interpretation.

In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. The black-box nature of conventional classification models has limited their utility in identifying toxicity pathways. Here we developed DTox (deep learning for toxicology), an interpretation framework for knowledge-guided neural networks, which can predict compound response to toxicity assays and infer toxicity pathways of individual compounds. We demonstrate that DTox can achieve the same level of predictive performance as conventional models with a significant improvement in interpretability. Using DTox, we were able to rediscover mechanisms of transcription activation by three nuclear receptors, recapitulate cellular activities induced by aromatase inhibitors and pregnane X receptor (PXR) agonists, and differentiate distinctive mechanisms leading to HepG2 cytotoxicity. Virtual screening by DTox revealed that compounds with predicted cytotoxicity are at higher risk for clinical hepatic phenotypes. In summary, DTox provides a framework for deciphering cellular mechanisms of toxicity in silico.