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.

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.

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.

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.

Automating Predictive Toxicology Using ComptoxAI.

ComptoxAI is a new data infrastructure for computational and artificial intelligence research in predictive toxicology. Here, we describe and showcase ComptoxAI's graph-structured knowledge base in the context of three real-world use-cases, demonstrating that it can rapidly answer complex questions about toxicology that are infeasible using previous technologies and data resources. These use-cases each demonstrate a tool for information retrieval from the knowledge base being used to solve a specific task: The "shortest path" module is used to identify mechanistic links between perfluorooctanoic acid (PFOA) exposure and nonalcoholic fatty liver disease; the "expand network" module identifies communities that are linked to dioxin toxicity; and the quantitative structure-activity relationship (QSAR) dataset generator predicts pregnane X receptor agonism in a set of 4,021 pesticide ingredients. The contents of ComptoxAI's source data are rigorously aggregated from a diverse array of public third-party databases, and ComptoxAI is designed as a free, public, and open-source toolkit to enable diverse classes of users including biomedical researchers, public health and regulatory officials, and the general public to predict toxicology of unknowns and modes of action.

Outcomes Following Direct Versus Indirect Decompression Techniques for Lumbar Spondylolisthesis: A Propensity-Matched Analysis.

STUDY DESIGN: Retrospective review. OBJECTIVE: The aim was to compare outcomes at 3 and 12 months for patients with lumbar spondylolisthesis treated with direct decompression (DD) versus indirect decompression (ID) techniques. SUMMARY OF BACKGROUND DATA: Debate persists regarding the optimal surgical strategy to treat lumbar spondylolisthesis. Novel techniques relying on ID have shown superior radiographic outcomes compared to DD, however, doubt remains regarding their effectiveness in achieving adequate decompression. Currently, there is a paucity of data comparing the clinical efficacy of DD to ID. METHODS: The Quality Outcomes Database (QOD), a national, multicenter prospective spine registry, was queried for patients who underwent DD and ID between April 2013 and January 2019. Propensity scores for each treatment were estimated using logistic regression dependent on baseline covariates potentially associated with outcomes. The propensity scores were used to exclude nonsimilar patients. Multivariable regression analysis was performed with the treatment and covariate as independent variables and outcomes as dependent variables. RESULTS: A total of 4163 patients were included in the DD group and 86 in the ID group. The ID group had significantly lower odds of having a longer hospital stay and for achieving 30% improvement in back and leg pain at 3 months. These trends were not statistically significant at 12 months. There were no differences in ED5D scores or Oswestry disability index 30% improvement scores at 3 or 12 months. ID patient had a significantly higher rate of undergoing a repeat operation at 3 months (4.9% vs. 1.5%, P =0.015). CONCLUSION: Our study suggests that both DD and ID for the treatment of lumbar spondylolisthesis result in similar clinical outcomes, with the exception that those treated with ID experienced a lower reduction in back and leg pain at 3 months and a higher 3-month reoperation rate. This data can provide surgeons with additional information when counseling patients on the pros and cons of ID versus DD surgery.

Drug-releasing vaginal rings for HIV/STI and pregnancy prevention: a review of recent advances and clinical applications.

INTRODUCTION: Adolescent girls and young women (AGYW), as well as pre- and post-menopausal women globally would benefit from expanded choice to address their sexual and reproductive health (SRH) needs related to Human Immunodeficiency Virus (HIV), sexually transmitted infections (STIs) and pregnancy prevention. Lack of adequate preventative vaccines for HIV/STIs reinforces public health prioritization for options women may use to mitigate risk for infectious disease and unplanned pregnancy. Drug releasing intravaginal rings (IVRs) represent one such technology that has garnered attention based on the modality's success as a pre-exposure prophylaxis (PrEP) delivery option in HIV risk reduction. AREAS COVERED: This article provides a synopsis of three IVR technologies in active clinical development for prevention of HIV, STI, and unintended pregnancy demonstrating advancements in terms of compatibility with a wide range of drug types with a focus on dapivirine-based silicone rings (International Partnership for Microbicides (IPM), tenofovir-based polyurethane rings (Conrad), and pod-based rings (Oak Crest Institute of Science)). EXPERT OPINION: The goals of IVR research are to reduce burdens of HIV/STIs and unplanned pregnancies. Through the evolution of IVR technologies, the potential exists to trigger integration of health-care services through formulation of products with multiple indications.

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods.

MOTIVATION: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. RESULTS: This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. AVAILABILITY AND IMPLEMENTATION: PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

The promise of automated machine learning for the genetic analysis of complex traits.

The genetic analysis of complex traits has been dominated by parametric statistical methods due to their theoretical properties, ease of use, computational efficiency, and intuitive interpretation. However, there are likely to be patterns arising from complex genetic architectures which are more easily detected and modeled using machine learning methods. Unfortunately, selecting the right machine learning algorithm and tuning its hyperparameters can be daunting for experts and non-experts alike. The goal of automated machine learning (AutoML) is to let a computer algorithm identify the right algorithms and hyperparameters thus taking the guesswork out of the optimization process. We review the promises and challenges of AutoML for the genetic analysis of complex traits and give an overview of several approaches and some example applications to omics data. It is our hope that this review will motivate studies to develop and evaluate novel AutoML methods and software in the genetics and genomics space. The promise of AutoML is to enable anyone, regardless of training or expertise, to apply machine learning as part of their genetic analysis strategy.

Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks.

Quantitative Structure-Activity Relationship (QSAR) modeling is a common computational technique for predicting chemical toxicity, but a lack of new methodological innovations has impeded QSAR performance on many tasks. We show that contemporary QSAR modeling for predictive toxicology can be substantially improved by incorporating semantic graph data aggregated from open-access public databases, and analyzing those data in the context of graph neural networks (GNNs). Furthermore, we introspect the GNNs to demonstrate how they can lead to more interpretable applications of QSAR, and use ablation analysis to explore the contribution of different data elements to the final models' performance.

Tenofovir Alafenamide for HIV Prevention: Review of the Proceedings from the Gates Foundation Long-Acting TAF Product Development Meeting.

The ability to successfully develop a safe and effective vaccine for the prevention of HIV infection has proven challenging. Consequently, alternative approaches to HIV infection prevention have been pursued, and there have been a number of successes with differing levels of efficacy. At present, only two oral preexposure prophylaxis (PrEP) products are available, Truvada and Descovy. Descovy is a newer product not yet indicated in individuals at risk of HIV-1 infection from receptive vaginal sex, because it still needs to be evaluated in this population. A topical dapivirine vaginal ring is currently under regulatory review, and a long-acting (LA) injectable cabotegravir product shows strong promise. Although demonstrably effective, daily oral PrEP presents adherence challenges for many users, particularly adolescent girls and young women, key target populations. This limitation has triggered development efforts in LA HIV prevention options. This article reviews efforts supported by the Bill & Melinda Gates Foundation, as well as similar work by other groups, to identify and develop optimal LA HIV prevention products. Specifically, this article is a summary review of a meeting convened by the foundation in early 2020 that focused on the development of LA products designed for extended delivery of tenofovir alafenamide (TAF) for HIV prevention. The review broadly serves as technical guidance for preclinical development of LA HIV prevention products. The meeting examined the technical feasibility of multiple delivery technologies, in vivo pharmacokinetics, and safety of subcutaneous (SC) delivery of TAF in animal models. Ultimately, the foundation concluded that there are technologies available for long-term delivery of TAF. However, because of potentially limited efficacy and possible toxicity issues with SC delivery, the foundation will not continue investing in the development of LA, SC delivery of TAF products for HIV prevention.

Ten simple rules for writing a paper about scientific software.

Papers describing software are an important part of computational fields of scientific research. These "software papers" are unique in a number of ways, and they require special consideration to improve their impact on the scientific community and their efficacy at conveying important information. Here, we discuss 10 specific rules for writing software papers, covering some of the different scenarios and publication types that might be encountered, and important questions from which all computational researchers would benefit by asking along the way.