Patterns最新文献

As the geopolitical superpowers race to regulate the digital realm, their divergent rights-centered, market-driven, and social-control-based approaches require a global compact on digital regulation. If diverse regulatory jurisdictions remain, forms of domination entailed by cultural imposition and hermeneutical injustice related to AI legislation and AI systems will follow. We argue for consensual regulation on shared substantive issues, accompanied by proper standardization and coordination. Failure to attain consensus will fragment global digital regulation, enable regulatory capture by authoritarian powers or bad corporate actors, and deepen the historical geopolitical power asymmetries between the global South and the global North. To prevent an unjust regulatory landscape where the global South's cultural and hermeneutic resources are absent, two principles for the Global Digital Compact to counter these prospective harms are proposed and discussed: (1) "recognitive consensus on key substantive benefits and harms" and (2) "procedural consensus on global coordination and essential standards."

Benchmarks and leaderboards are commonly used to track the fairness impacts of artificial intelligence (AI) models. Many critics argue against this practice, since it incentivizes optimizing for metrics in an attempt to build the "most fair" AI model. However, this is an inherently impossible task since different applications have different considerations. While we agree with the critiques against leaderboards, we believe that the use of benchmarks can be reformed. Thus far, the critiques of leaderboards and benchmarks have become unhelpfully entangled. However, benchmarks, when not used for leaderboards, offer important tools for understanding a model. We advocate for collecting benchmarks into carefully curated "benchmark suites," which can provide researchers and practitioners with tools for understanding the wide range of potential harms and trade-offs among different aspects of fairness. We describe the research needed to build these benchmark suites so that they can better assess different usage modalities, cover potential harms, and reflect diverse perspectives. By moving away from leaderboards and instead thoughtfully designing and compiling benchmark suites, we can better monitor and improve the fairness impacts of AI technology.

We discuss the real-world application of federated learning (FL) in the healthcare and life sciences industry, noting a tipping point in its adoption beyond academia. Sharing our experiences with multi-hospital and multi-pharma collaborations, we highlight the importance of involving key stakeholders to develop production-grade FL solutions that are fully compliant with stringent privacy and security standards.

This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.75%, an area under the receiver operating characteristic curve (AUROC) of 80.64%, and correctly identified MDD subtypes. The system further discovered distinct brain connectivity patterns in MDD, including reduced functional connectivity between the left gyrus rectus and right cerebellar lobule VIIB, and increased connectivity between the left Rolandic operculum and right hippocampus. Anatomically, MDD is associated with thickness changes of the gray and white matter interface, indicating potential neuropathological conditions or brain injuries.

Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes. Importantly, we found this model to successfully compress unseen transcriptomic changes from 25 independent disease datasets. We decoded disease-specific signals from the latent space and found them to contain more disease-specific genes than the corresponding differential expression analysis in 20 of 25 cases. Finally, we matched these disease signals with known drug targets and extracted sets of known and potential pharmaceutical candidates. In summary, our study demonstrates how data-driven representation learning enables the arithmetic deconstruction of the latent space, facilitating the dissection of disease mechanisms and drug targets.

The long-term complications of COVID-19, known as the post-acute sequelae of SARS-CoV-2 infection (PASC), significantly burden healthcare resources. Quantifying the demand for post-acute healthcare is essential for understanding patients' needs and optimizing the allocation of valuable medical resources for disease management. Driven by this need, we developed a heterogeneous latent transfer learning framework (Latent-TL) to generate critical insights for individual health systems in a distributed research network. Latent-TL enhances learning in a specific health system by borrowing information from all other health systems in the network in a data-driven fashion. By identifying subpopulations with varying healthcare needs, our Latent-TL framework can provide more effective guidance for decision-making. Applying Latent-TL to electronic health record (EHR) data from eight health systems in PEDSnet, a national learning health system in the US, revealed four distinct patient subpopulations with heterogeneous post-acute healthcare demands following COVID-19 infections, varying across subpopulations and hospitals.

The Gene Expression Omnibus (GEO) has millions of samples from thousands of studies. While users of GEO can search the metadata describing studies, there is a need for methods to search GEO at the data level. RummaGEO is a gene expression signature search engine for human and mouse RNA sequencing perturbation studies extracted from GEO. To develop RummaGEO, we automatically identified groups of samples and computed differential expressions to extract gene sets from each study. The contents of RummaGEO are served for gene set, PubMed, and metadata search. Next, we analyzed the contents of RummaGEO to identify patterns and perform global analyses. Overall, RummaGEO provides a resource that is enabling users to identify relevant GEO studies based on their own gene expression results. Users of RummaGEO can incorporate RummaGEO into their analysis workflows for integrative analyses and hypothesis generation.

Scientific writing is an essential skill for researchers to publish their work in respected peer-reviewed journals. While using AI-assisted tools can help researchers with spelling checks, grammar corrections, and even rephrasing of paragraphs to improve the language and meet journal standards, unethical use of these tools may raise research integrity concerns during this process. In this piece, three Patterns authors share their thoughts on three questions: how do you use AI tools ethically during manuscript writing? What benefits and risks do you believe AI tools will bring to scientific writing? Do you have any recommendations for better policies regulating AI tools' use in scientific writing?

There are new realities in society and the workplace that necessitate the evolution of student skills. AI now creates highly usable text, requiring students to shift their focus to different skills such as editing, AI literacy, and critical thinking so that they may effectively work with AI and succeed in the modern world.