Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

Regime shifts on tropical coral reef ecosystems: future trajectories to animal-dominated states in response to anthropogenic stressors

Despite the global focus on the occurrence of regime shifts on shallow-water tropical coral reefs over the last two decades, most of this research continues to focus on changes to algal-dominated states. Here, we review recent reports (in approximately the last decade) of regime shifts to states dominated by animal groups other than zooxanthellate Scleractinian corals. We found that while there have been new reports of regime shifts to reefs dominated by Ascidacea, Porifera, Octocorallia, Zoantharia, Actiniaria and azooxanthellate Scleractinian corals, some of these changes occurred many decades ago, but have only just been reported in the literature. In most cases, these reports are over small to medium spatial scales (<4 × 104 m2 and 4 × 104 to 2 × 106 m2, respectively). Importantly, from the few studies where we were able to collect information on the persistence of the regime shifts, we determined that these non-scleractinian states are generally unstable, with further changes since the original regime shift. However, these changes were not generally back to coral dominance. While there has been some research to understand how sponge- and octocoral-dominated systems may function, there is still limited information on what ecosystem services have been disrupted or lost as a result of these shifts. Given that many coral reefs across the world are on the edge of tipping points due to increasing anthropogenic stress, we urgently need to understand the consequences of non-algal coral reef regime shifts.

From imaging a single cell to implementing precision medicine: an exciting new era

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

Artificial intelligence unifies knowledge and actions in drug repositioning

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.

Common computational tools for analyzing CRISPR screens

CRISPR–Cas technology offers a versatile toolbox for genome editing, with applications in various cancer-related fields such as functional genomics, immunotherapy, synthetic lethality and drug resistance, metastasis, genome regulation, chromatic accessibility and RNA-targeting. The variety of screening platforms and questions in which they are used have caused the development of a wide array of analytical methods for CRISPR analysis. In this review, we focus on the algorithms and frameworks used in the computational analysis of pooled CRISPR knockout (KO) screens and highlight some of the most significant target discoveries made using these methods. Lastly, we offer perspectives on the design and analysis of state-of-art multiplex screening for genetic interactions.

Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

A targeted cancer therapy is only useful if there is a way to accurately identify the tumors that are susceptible to that therapy. Thus rapid expansion in the number of available targeted cancer treatments has been accompanied by a robust effort to subdivide the traditional histological and anatomical tumor classifications into molecularly defined subtypes. This review highlights the history of the paired evolution of targeted therapies and biomarkers, reviews currently used methods for subtype identification, and discusses challenges to the implementation of precision oncology as well as possible solutions.

Will coral reefs survive by adaptive bleaching?

Some reef-building corals form symbioses with multiple algal partners that differ in ecologically important traits like heat tolerance. Coral bleaching and recovery can drive symbiont community turnover toward more heat-tolerant partners, and this ‘adaptive bleaching’ response can increase future bleaching thresholds by 1–2°C, aiding survival in warming oceans. However, this mechanism of rapid acclimatization only occurs in corals that are compatible with multiple symbionts, and only when the disturbance regime and competitive dynamics among symbionts are sufficient to bring about community turnover. The full scope of coral taxa and ecological scenarios in which symbiont shuffling occurs remains poorly understood, though its prevalence is likely to increase as warming oceans boost the competitive advantage of heat-tolerant symbionts, increase the frequency of bleaching events, and strengthen metacommunity feedbacks. Still, the constraints, limitations, and potential tradeoffs of symbiont shuffling suggest it will not save coral reef ecosystems; however, it may significantly improve the survival trajectories of some, or perhaps many, coral species. Interventions to manipulate coral symbionts and symbiont communities may expand the scope of their adaptive potential, which may boost coral survival until climate change is addressed.

Radiomics, deep learning and early diagnosis in oncology

Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists’ task and help with challenging cases, computer-aided diagnosis has been developing rapidly in the past decade, pioneered by radiomics early on, and more recently, driven by deep learning. In this mini-review, I use breast cancer as an example and review how medical imaging and its quantitative modeling, including radiomics and deep learning, have improved the early detection and treatment response prediction of breast cancer. I also outline what radiomics and deep learning share in common and how they differ in terms of modeling procedure, sample size requirement, and computational implementation. Finally, I discuss the challenges and efforts entailed to integrate deep learning models and software in clinical practice.

Molecular-based precision oncology clinical decision making augmented by artificial intelligence

The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians’ decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.