Patterns, a Cell Press journal

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A peer-reviewed #openaccess data science journal from @cellpress.bsky.social Editor-in-Chief: Andrew L Hufton (@alhufton.bsky.social) Visit us online at https://www.cell.com/patterns/

The cover image depicts a time series, stylized as a grid of colored rectangles, disrupted by a dark void, representing missing data. A flashlight explores it and a beam of light reveals hidden patterns within the darkness. By shifting the perspective from “filling the gaps” to “learning from them,” the method presented in the associated research paper by Bellmann et al. improves performance under extreme sparsity. Image credit: Hümeyra Husseini-Wüsthoff, University Medical Center Hamburg-Eppendorf. Modified by Julie Ho Sung, Cell Press.
On the cover: An image highlighting the work by Sun et al. in this month's issue. A brain-shaped silhouette viewed from above reveals a stylized laboratory setting within. The left hemisphere embodies a biological lab for Drosophila experiments; the right hemisphere embodies a silicon computing environment for artificial neural network modeling. A bidirectional bridge emphasizes the reciprocal flow: biological learning inspires AI paradigms, and computational results guide biological validation. The backdrop uses spaced stripes and color gradients to evoke the spacing effect and encoding variability, underscoring the core result that deliberate temporal variation enhances generalization. Artist: August Fireflies Technology Co., Ltd
The cover illustration explores the transformation of chaotic experimental data into precise scientific insights. A tangled web of misaligned, raw measurements is transformed by the touch of a digital hand, representing the implicit neural network. It emerges as a perfectly ordered spiral, illustrating the restoration of 3D structural and chemical information. Image credit: Jizhou Li and Chao Wang.
This month's cover image, related to the study by Kuzikov et al., shows a representative cellular phenotype of drug-induced phospholipidosis. The effect is characterized by the excessive accumulation of phospholipids within cells that also can be induced by cationic amphiphilic drugs. Using the shown high-content imaging, the drug-induced phospholipidosis phenotype was characterized, and over 5,000 repurposed drugs were annotated for the ability to induce phospholipidosis. The results were then used to understand which compounds are likely to induce phospholipidosis based on a drug’s chemical structure, finally resulting in the development of a machine learning model capable of predicting the risk of phospholipidosis. Image provided by Mariya Pereverzeva.
Modeling of longitudinal immune profiles reveals distinct immunogenic signatures following five COVID-19 vaccinations among people living with HIV. In the study by Korosec et al., researchers generate synthetic data trajectories (“virtual patients”) that preserve the statistical properties of real immune measurements by using data-driven models. The illustration, by Andrée Fournier (National Research Council Canada), explores the intersection of human biology and computational modeling. In the foreground, a softly rendered human hand reaches forward, symbolizing real patients and empirical data. Emerging from the background, a semi-transparent wireframe hand, constructed from a digital mesh, represents the virtual patients and the models’ aim to capture the essential features of true biological responses.
On the cover: Inspired by Jia et al. in this issue, the cover visualises how incomplete or incorrect segmentation labels can be filled in with information from the image itself. The grid marks are where labels are absent, while the completed shapes illustrate how visual context helps recover those regions. The robotic hand represents an automated process that reduces the need for manual relabelling. Overall, the image shows how combining images with partial labels can produce more reliable training data for semantic segmentation. Image credit: Phillip Krzeminski.
An image of the first page of the paper mentioned in the post.
On the cover: The central brain in this image represents IoT-LLM, a framework for processing real-world sensor data with large language models, which is described by An et al. in this issue of Patterns. IoT-LLM provides a central reasoning core that connects and interprets heterogeneous sensor inputs grounded in the physical world such as electrocardiography, temperature, motion, camera images, and robotic system sensors. In the image, these data streams converge on the central model, highlighting how IoT-LLM performs retrieval-augmented fusion and structured reasoning across different data types. Systems like IoT-LLM are laying a foundation for future embodied robots operating in real-world environments. Image credit: An Tuo, Nanyang Technological University.
Text on image reads: Patterns, A Cell Press journal, Editors' Pick, Best of 2025, Explore the collection
On the cover: The image, related to the work by Fernandez Bonet et al., shows a series of spatially coherent networks, explored with breadth-first search to differing depths. The emerging predictable scaling pattern revealed by the progression is indicative of geometric consistency, and this feature can be detected in networks as an indication of how “spatial” a network is. Image credit to Ian Hoffecker and David Fernandez Bonet.
A network of nodes and edges encircles the globe, reflecting the worldwide reach of natural language processing as it spreads across languages and regions. From this global mesh, Greek letters emerge. When viewed horizontally, they are mirrored onto a continuously evolving background, suggesting the transformation of language technologies over time. The image highlights the work by Bakagianni et al. in this month’s issue, which offers a perspective on how machine learning for language has expanded dramatically across the world, affecting many languages, and captured here through the lens of Greek. Cover design by Smart Graphics, Ntina Gkanti. Image credit: Artistdesign.13 (shutterstock.com).
Text presented over a blue and orange background reads: Patterns, a Cell Press journal. Call for papers: Reanalysis. Compelling and creative reanalyses of prior works of high importance and broad impact. August 1, 2026.
In the image, proteins are represented as sailboats drifting in the dark. At the center, stands a lighthouse symbolizing the AI method LigUnity. Its beam illuminates several sailboats, guiding them toward glowing buoys, which symbolize ligands with high activity. The image illustrates the work by Feng et al. in this month's issue of Patterns. Image credit: Bin Feng, International Digital Economy Academy.
A photo of Edith Luhanga
The journal's cover image illustrates a futuristic city where all structures are interconnected to support the collective prosperity of the organism. At its center is a highly advanced building that receives comprehensive information from across the city and uses it to evaluate the city’s current status as a global entity, which arises from the interconnected components of such a complex network. Similarly, genes within cells function like dynamic circuits by continuously toggling on and off to generate stable patterns known as “attractors,” which influence and control the behaviour of living organisms. In this issue of Patterns, Rossini et al. present an innovative quantum computing approach that rapidly detects these stable genetic patterns—much like navigating a maze—by harnessing quantum mechanical properties. The integration of quantum computing with biology opens new pathways for better understanding the complexity of life. Image credit: Marietta Hamberger.
Our cover image this month is an artistic rendering of a community garden, seen from above. Each plot in the garden may serve a specific family or group, and the gardeners themselves will have widely varying levels of experience, but across the garden, experience and tools are shared, creating a fertile, cooperative ecosystem that supports a broad community. Image credit: Philip Krzeminski
On the cover: “Sur Incise / On Intersection”: Seeing through layers “Sur Incise” draws us in with fractured surfaces—blocks of ochre, navy, yellow, and green, colliding with purpose. Its title, referencing Boulez's composition and the act of cutting, hints at fragmentation and ambiguity. At the painting's center lies a human figure—nude, abstracted, shifting between identities. Visible in color but elusive in form, it invites questions: what remains unseen when language fails us? This visual tension mirrors findings from Savoldi et al.'s study on gender bias in machine translation, where systems default to binaries and overlook intersectionality. Just as the painting resists clear categorization, translation models struggle with identities that do not conform to fixed labels—flattening complexity and erasing nuance. “Sur Incise” offers no singular reading. Gender, like color, is layered, mutable. The figure is not absent—only obscured. To perceive it, we must move beyond rigid categories and accept that some meanings are felt rather than defined.Image credit: Picture of the painting “Sur Incise / On Intersection” 2025. Acrylic paint on a linen canvas mounted on an aluminum frame. 120 cm × 120 cm × 1.5 cm. © Dirk Vanmassenhove.
On the cover: Extreme disasters, such as earthquakes, floods, hurricanes, and landslides, although rare, can severely damage road transportation networks, potentially leading to significant declines in system performance. Predicting the impact of these “black swan” events and estimating the probability of extreme failures has long been a challenge because of the high computational cost of traditional methods. In this issue, Guo et al. propose an importance sampling method based on graph learning that enhances sampling efficiency for extreme failure scenarios in large-scale road transportation networks. This approach provides valuable technical support for improving disaster resilience in infrastructure systems. The cover image illustrates a graph neural network used to analyze and quantify the criticality of road segments within transportation networks under various extreme disasters, thereby facilitating cost-efficient sampling of extreme failure scenarios in large-scale road transportation networks. Image credit: Tingting Zhao, Beijing Jiaotong University.
On the cover: The complex biology reflected in the tissue surrounding and including tumors has important implications on the evolution of cancer and on the overall prognosis of the patient. However, this microenvironment is very complex and heterogeneous, making it difficult to extract informative quantitative measures that can inform patient outcomes. BiGraph is a method that automatically finds patterns of cell interactions in the tumor microenvironment that characterize different groups of patients with distinct outcomes, leading to prognostic biomarkers. BiGraph builds a graph at the population (macro) level that associates patients by studying their similarity according to the composition of their tumor microenvironments. In turn, each microenvironment is characterized by specific patterns of cell interactions, which are modeled by a cellular (micro) level graph. The work by Wang et al. highlights the power of interpretable data-driven methods in uncovering biologically meaningful features using breast cancer as a case study, while applicable to other diseases more broadly. Image credits: Z. Wang and J. Sulam.
Text in image reads: Cell Press | Forums on Sustainability Land health: Preventing zoonoses spillover Wednesday, March 5, 2025 8:00 a.m. ET | 2 p.m. CET Register now Image: a colorful sphere with protrusions is draw in a way that can evoke either a virus or a planet with trees
On the cover: This image shows a multicolored wave pattern, resembling longitudinal wave patterns or Weibull and other distributions, evoking the presence of data and knowledge everywhere. Image credit: Andriy Onufriyenko/Moment via Getty Images.
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Text overlaid on colorful pattern reads: Patterns Development and application of multimodal AI models Inviting submissions now
A picture of a cityscape with colored lines representing a network of data, overlaid with the text "Patterns, Urban sustainability and data science, Calling for papers now".
Journal cover image: Geometry in art involves the precise arrangement of shapes, lines, and angles to create visually appealing compositions. Here, the artist employed six rotating and overlapping circles, blending them to evoke a sense of dynamic movement while incorporating beautiful gradients that added depth and visual richness to the artwork. Image credit: Naola Leconte.