Broad-scale shifts in biodiversity demand a deeper understanding of both historical and future time trends across the Tree of Life. Retrospective analyses indicate that some groups, such as birds, fishes and insects, have experienced significant decl...
The construction of multicellular mechanistic models in systems biology typically requires months of literature research, programming expertise, and deep knowledge of specialized computational tools. Here we present intelligent tool orchestration thr...
A fundamental challenge in neuroscience and AI is understanding how physical space is mapped into neural representations. While artificial neural networks can generate brain-like spatial representations, such as place and grid cells, their “black-box...
Cooperation is a hallmark of social species, enabling individuals to achieve goals that are unattainable alone. Across species, cooperative behaviors are often organized by distinct social roles such as leaders and followers, yet the neural mechanism...
The brain seamlessly transforms sensory information into precisely-timed movements, enabling us to type familiar words, play musical instruments, or perform complex motor routines with millisecond precision. This process often involves organizing act...
Understanding the multi-scale organization of protein-protein interactions (PPIs) is fundamental to deciphering cellular signaling, allosteric regulation, and disease mechanisms, yet existing computational approaches fail to simultaneously resolve at...
Antimicrobial resistance (AMR) prediction from bacterial genomes remains a major challenge for clinical microbiology and surveillance. We developed deep learning models based on Frequency Chaos Game Representation (FCGR) and a ResNet-18 architecture ...
In early detection of cancer, the use of circulating cell-free DNA (cirDNA) obtained from blood samples is notable for its minimally invasive nature. We have developed an algorithm designed to discriminate cancer patients and healthy individuals base...
Physiological time-series data, like electroencephalography (EEG), are vulnerable to motion, ocular, and muscle artifacts that hinder real-time inference and bias offline analyses. We present the Minds AI Filter: a lightweight, physics-based, sensor-...
Cerebral cortical networks in the mammalian brain exhibit a non-random organization that systematically avoids strong reciprocal projections, particularly in sensory hierarchies. This “no-strong-loops” principle is thought to prevent runaway excitati...
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