Interpretable machine learning for genomics

High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines

Examining the effect of explanation on satisfaction and trust in AI diagnostic systems

Background: Artificial Intelligence has the potential to revolutionize healthcare, and it is increasingly being deployed to support and assist medical diagnosis. One potential application of AI is as the first point of contact for patients, replacing initial diagnoses prior to sending a patient to a specialist, allowing health care professionals to focus on more challenging and critical aspects of treatment. But for AI systems to succeed in this role, it will not be enough for them to merely provide accurate diagnoses and predictions. In addition, it will need to provide explanations (both to physicians and patients) about why the diagnoses are made. Without this, accurate and correct diagnoses and treatments might otherwise be ignored or rejected. Method: It is important to evaluate the effectiveness of these explanations and understand the relative effectiveness of different kinds of explanations. In this paper, we examine this problem across two simulation experiments. For the first experiment, we tested a re-diagnosis scenario to understand the effect of local and global explanations. In a second simulation experiment, we implemented different forms of explanation in a similar diagnosis scenario. Results: Results show that explanation helps improve satisfaction measures during the critical re-diagnosis period but had little effect before re-diagnosis (when initial treatment was taking place) or after (when an alternate diagnosis resolved the case successfully). Furthermore, initial “global” explanations about the process had no impact on immediate satisfaction but improved later judgments of understanding about the AI. Results of the second experiment show that visual and example-based explanations integrated with rationales had a significantly better impact on patient satisfaction and trust than no explanations, or with text-based rationales alone. As in Experiment 1, these explanations had their effect primarily on immediate measures of satisfaction during the re-diagnosis crisis, with little advantage prior to re-diagnosis or once the diagnosis was successfully resolved. Conclusion: These two studies help us to draw several conclusions about how patient-facing explanatory diagnostic systems may succeed or fail. Based on these studies and the review of the literature, we will provide some design recommendations for the explanations offered for AI systems in the healthcare domain

Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance.

The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered

Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. The aim of this review article is to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.Comment: 13 pages, 3 figure

Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines

QSAR Modeling: Where Have You Been? Where Are You Going To?

Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making

Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China

Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using novel data-mining methods. Most isolates within same hosts possessed same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Machine learning revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials and primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs

Bugs, Drugs and Data: Antibiotic Resistance, Prevalence and Prediction of Bug-Drug Mismatch using Electronic Health Records (EHR) Data

Title from PDF of title page viewed December 14, 2021Dissertation advisor: An-Lin ChengVitaIncludes bibliographical references (pages 92-130)Thesis (Ph.D.)--School of Medicine, School of Computing and Engineering, and School of Biological and Chemical Sciences. University of Missouri--Kansas City, 2021Bug-Drug Mismatch (BDM) occurrences are an important and modifiable category of inappropriate antibiotic therapy (IAAT) that increases adverse outcomes for patients and drives overall antibiotic resistance (AR). Surveillance of baseline AR, emerging trends in resistance among priority bacterial pathogens and prevalence of BDM with respect to the age of the patients and the type of health care-setting are required due to differences in antimicrobial need and use in these populations. Additionally, very little is known about the risk factors associated with BDM occurrence. We performed a retrospective study using de-identified, electronic health record (EHR) data in the Cerner Health Facts™ data warehouse. We assessed antibiotic susceptibility data between the years 2012 to 2017 and visualized the slope coefficient from linear regression to compare changes in resistance over time. We examined the prevalence of BDM for critically important antibiotics and clinically relevant pathogens between the year 2009 to 2017 in four groups of patients: adults; children; children treated in freestanding pediatric facilities and children treated in blended facilities (adults and children). We implemented multiple logistic regression as a reference model to identify risk factors for BDM occurrences and compared the predictive performance measure with 4 machine learning models (logistic regression with lasso regularization, random forest, gradient boosted decision tree and deep neural network). The trends in resistance rates to clinically relevant antibiotics were influenced by age and care setting. BDM prevalence for several critically important antibiotics differed between children and adults as well as within pediatric and blended facilities. Risk factors such as age of the patient, patient comorbidities and size of the facility were significantly associated with BDM occurrence. Additionally, the machine learning models developed in our study has a high predictive ability (C-statistic), higher sensitivity, specificity, positive predictive value and positive likelihood ratio to identify BDM occurrence than the reference model. This study describes the utility of data visualization to interpret large scale EHR data on the trends of AR, prevalence and risk factors of BDM which are critical in tailoring antibiotic stewardship efforts to improving appropriate antibiotic prescribing and ultimately reduce AR.Introduction -- Background -- Variation in antibiotic resistance patterns for children and adults treated at 166 non-affiliated facilities -- Differences in the prevalence of definitive bug-drug mismatch (BDM) therapy between adults and children by care setting -- Predicting bug-drug mismatch (BDM) occurrence in EHR data using machine Learning models -- Conclusio