From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.

A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.

In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 heralds a transformative approach, advocating for the integration of alternative methods to conventional animal testing, including cell-based assays that employ human induced pluripotent stem cell (iPSC)-derived organoids, and organ-on-a-chip technologies, in conjunction with sophisticated artificial intelligence (AI) methodologies. Our review explores the innovative capacity of iPSC-derived clinical trial in a dish models designed for cardiovascular disease research. We also highlight how integrating iPSC technology with AI can accelerate the identification of viable therapeutic candidates, streamline drug screening, and pave the way toward more personalized medicine. Through this, we provide a comprehensive overview of the current landscape and future implications of iPSC and AI applications being navigated by the research community and pharmaceutical industry.

Language models for biological research: a primer.

Language models are playing an increasingly important role in many areas of artificial intelligence (AI) and computational biology. In this primer, we discuss the ways in which language models, both those based on natural language and those based on biological sequences, can be applied to biological research. This primer is primarily intended for biologists interested in using these cutting-edge AI technologies in their applications. We provide guidance on best practices and key resources for adapting language models for biology.

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN.

Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.

ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries.

MOTIVATION: The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). RESULTS: We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest public ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 h. AVAILABILITY AND IMPLEMENTATION: The ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418.

Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Clinical trials in-a-dish for cardiovascular medicine.

Cardiovascular diseases persist as a global health challenge that requires methodological innovation for effective drug development. Conventional pipelines relying on animal models suffer from high failure rates due to significant interspecies variation between humans and animal models. In response, the recently enacted Food and Drug Administration Modernization Act 2.0 encourages alternative approaches including induced pluripotent stem cells (iPSCs). Human iPSCs provide a patient-specific, precise, and screenable platform for drug testing, paving the way for cardiovascular precision medicine. This review discusses milestones in iPSC differentiation and their applications from disease modelling to drug discovery in cardiovascular medicine. It then explores challenges and emerging opportunities for the implementation of 'clinical trials in-a-dish'. Concluding, this review proposes a framework for future clinical trial design with strategic incorporations of iPSC technology, microphysiological systems, clinical pan-omics, and artificial intelligence to improve success rates and advance cardiovascular healthcare.

Microbial communities are indicators of parasite infection status.

Growing evidence suggests that microbiomes have been shaping the evolutionary pathways of macroorganisms for millennia and that these tiny symbionts can influence, and possibly even control, species interactions like host-parasite relationships. Yet, while studies have investigated host-parasites and microbiomes separately, little has been done to understand all three groups synergistically. Here, we collected infected and uninfected Eurypanopeus depressus crab hosts from a coastal North Carolina oyster reef three times over 4 months. Infected crabs demonstrated an external stage of the rhizocephalan parasite, Loxothylacus panopaei. Community analyses revealed that microbial richness and diversity were significantly different among tissue types (uninfected crab, infected crab, parasite externae and parasite larvae) and over time (summer and fall). Specifically, the microbial communities from parasite externae and larvae had similar microbiomes that were consistent through time. Infected crabs demonstrated microbial communities spanning those of their host and parasite, while uninfected crabs showed more distinctive communities with greater variability over time. Microbial communities were also found to be indicators of early-stage infections. Resolving the microbial community composition of a host and its parasite is an important step in understanding the microbiome's role in the host-parasite relationship and determining how this tripartite relationship impacts coevolutionary processes.

Invasion of the body snatchers: the role of parasite introduction in host distribution and response to salinity in invaded estuaries.

In dynamic systems, organisms are faced with variable selective forces that may impose trade-offs. In estuaries, salinity is a strong driver of organismal diversity, while parasites shape species distributions and demography. We tested for trade-offs between low-salinity stress and parasitism in an invasive castrating parasite and its mud crab host along salinity gradients of two North Carolina rivers. We performed field surveys every six to eight weeks over 3 years to determine factors influencing parasite prevalence, host abundance, and associated taxa diversity. We also looked for signatures of low-salinity stress in the host by examining its response (time-to-right and gene expression) to salinity. We found salinity and temperature significantly affected parasite prevalence, with low-salinity sites (less than 10 practical salinity units (PSU)) lacking infection, and populations in moderate salinities at warmer temperatures reaching prevalence as high as 60%. Host abundance was negatively associated with parasite prevalence. Host gene expression was plastic to acclimation salinity, but several osmoregulatory and immune-related genes demonstrated source-dependent salinity response. We identified a genetic marker that was strongly associated with salinity against a backdrop of no neutral genetic structure, suggesting possible selection on standing variation. Our study illuminates how selective trade-offs in naturally dynamic systems may shape host evolutionary ecology.

Deep learning for automated classification and characterization of amorphous materials.

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction.

Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources. The need for UQ is especially acute for neural models, which are becoming increasingly standard yet are challenging to interpret. While several approaches to UQ have been proposed in the literature, there is no clear consensus on the comparative performance of these models. In this paper, we study this question in the context of regression tasks. We systematically evaluate several methods on five regression data sets using multiple complementary performance metrics. Our experiments show that none of the methods we tested is unequivocally superior to all others, and none produces a particularly reliable ranking of errors across multiple data sets. While we believe that these results show that existing UQ methods are not sufficient for all common use cases and further research is needed, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation.

Purpose To develop a deep learning (DL) algorithm to assess mammographic breast density. Materials and Methods In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) breast density based on the original interpretation by an experienced radiologist of 41 479 digital screening mammograms obtained in 27 684 women from January 2009 to May 2011. The resulting algorithm was tested on a held-out test set of 8677 mammograms in 5741 women. In addition, five radiologists performed a reader study on 500 mammograms randomly selected from the test set. Finally, the algorithm was implemented in routine clinical practice, where eight radiologists reviewed 10 763 consecutive mammograms assessed with the model. Agreement on BI-RADS category for the DL model and for three sets of readings-(a) radiologists in the test set, (b) radiologists working in consensus in the reader study set, and (c) radiologists in the clinical implementation set-were estimated with linear-weighted κ statistics and were compared across 5000 bootstrap samples to assess significance. Results The DL model showed good agreement with radiologists in the test set (κ = 0.67; 95% confidence interval [CI]: 0.66, 0.68) and with radiologists in consensus in the reader study set (κ = 0.78; 95% CI: 0.73, 0.82). There was very good agreement (κ = 0.85; 95% CI: 0.84, 0.86) with radiologists in the clinical implementation set; for binary categorization of dense or nondense breasts, 10 149 of 10 763 (94%; 95% CI: 94%, 95%) DL assessments were accepted by the interpreting radiologist. Conclusion This DL model can be used to assess mammographic breast density at the level of an experienced mammographer. © RSNA, 2018 Online supplemental material is available for this article . See also the editorial by Chan and Helvie in this issue.

Analyzing Learned Molecular Representations for Property Prediction.

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature.

As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR) [Ney ER (1959) Nature 183:451-452]. Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research [Duplissy J, et al. (2010) Atmos Chem Phys 10:1635-1647; Kirkby J, et al. (2011) Nature 476(7361):429-433] and elsewhere [Svensmark H, Pedersen JOP, Marsh ND, Enghoff MB, Uggerhøj UI (2007) Proc R Soc A 463:385-396; Enghoff MB, Pedersen JOP, Uggerhoj UI, Paling SM, Svensmark H (2011) Geophys Res Lett 38:L09805], demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we find no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. However, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.

Microsurgical resection of tumors of the lateral and third ventricles: operative corridors for difficult-to-reach lesions.

Tumors of the lateral and third ventricles are cradled on all sides by vital vascular and eloquent neural structures. Microsurgical resection, which always requires attentive planning, plays a critical role in the contemporary management of these lesions. This article provides an overview of the open microsurgical approaches to the region highlighting key clinical perspectives.

Supracerebellar transtentorial approach to the tentorial incisura and beyond.

The supracerebellar transtentorial approach via a suboccipital craniotomy provides a corridor to reach lesions of the tentorial incisura and supratentorial lesions of the posterior medial basal temporal lobe, such as lesions of the posterior parahippocampal and fusiform gyri. The supracerebellar transtentorial approach obviates the need for either retraction of eloquent cortex or a transcortical route to reach lesions in this region. We present three cases that demonstrate the utility of this approach: a left-sided tentorial meningioma with superior projection, a left-sided posterior parahippocampal cavernous malformation, and a left-sided posterior parahippocampal grade 2 oligodendroglioma. The video can be found here: https://youtu.be/OLnzUGZfUqk .

Iatrogenic intracranial placement of nasopharyngeal airway after trauma.

CT images of an 18-year-old woman who had sustained head trauma after a motor vehicle accident are presented demonstrating the iatrogenic intracranial placement of a nasopharyngeal airway. Treatment required a decompressive craniectomy, removal of the nasopharyngeal airway under direct vision, and duraplasty. The patient made a good neurological recovery, but did require ongoing medical treatment for diabetes insipidus. The case illustrates the importance of avoiding intranasal placement of any object in a patient with head trauma and suspected skull base fractures prior to diagnostic imaging.

Awake Neurophysiologically Guided versus Asleep MRI-Guided STN DBS for Parkinson Disease: A Comparison of Outcomes Using Levodopa Equivalents.

BACKGROUND: Deep brain stimulation (DBS) for Parkinson's disease (PD) has traditionally been performed in awake patients. Some patients are unable to tolerate awake surgery or extensive time off their medication to allow for neurophysiological testing during traditional DBS implantation, which has previously limited surgical options for these patients. Recently, asleep image-guided lead placement using intraoperative MRI or CT for verification has been proposed as an alternative for patients unable or unwilling to undergo awake DBS surgery. METHODS: We conducted a retrospective chart review comparing PD patients who underwent asleep MRI-guided subthalamic nucleus (STN) DBS lead placement (n = 14) and awake neurophysiologically guided STN DBS lead placement (n = 23) at our institution. Both groups' levodopa equivalent daily doses (LEDDs) and complications at approximately 6 months of follow-up were compared, along with operative times. RESULTS: Both groups showed statistically similar reductions in LEDD at 6 months of therapy (38.27% for awake, 49.27% for asleep; p = 0.4447), and similar complications. Operative times were initially longer for MRI-guided DBS but improved with surgical experience. CONCLUSION: Asleep MRI-guided DBS is a viable option for PD patients unable or unwilling to undergo awake placement, with similar results in terms of LEDD reduction and complications.