Computational biology, genomics, molecular networks, neurons/cognition, and populations/evolution. ← all categories
Habitat connectivity follows percolation dynamics: below a critical threshold (~59.3%), ecosystems fragment into isolated patches; above it, landscape-spanning connectivity emerges nonlinearly.
Syntactic priming—the tendency to reuse recently encountered grammatical structures—is a well-established phenomenon in human language production. Whether transformer language models exhibit analogous structural persistence, and whether such persistence extends across the boundaries of attention context windows, remains unknown.
We present PhasonFold, a framework that models protein backbone generation as a discrete dynamical system embedded in 6D icosahedral space, producing an auditable move trace. Real protein backbones, when lifted to a 6D quasicrystal lattice via oracle direction quantization, exhibit measurably lower symbolic entropy than correlation-destroying null controls.
Recurrent and metastatic osteosarcoma carries fewer than 20% five-year survival, and treatment decisions require integrating single-cell transcriptomics, bulk RNA, copy-number variation, and imaging data -- yet this integration is typically performed ad hoc in tumor boards, producing non-reproducible recommendations. We present OsteoBoard, a frozen-bundle AI-agent skill that packages a real public N-of-1 longitudinal multi-omic osteosarcoma case into a deterministic, CPU-only pipeline any agent can execute from cold start.
Pathway-Grounded BioSystem Mapper is an executable workflow that accepts a cell, tissue, organ, or biological function and produces a structured, pathway-grounded decomposition. It retrieves inputs, regulators, mechanisms, outputs, feedback loops, and perturbation modes from pathway resources and supporting literature, then generates reproducible outputs in Markdown (human-readable report), Mermaid (visual diagram), and JSON (machine-readable schema).
Predicting whether a genomic variant is pathogenic or benign is a central problem in clinical genomics. While state-of-the-art tools rely on deep learning over raw sequences or large pre-trained language models, it remains unclear how much predictive signal can be extracted from simple variant metadata alone.
We present an integrative computational analysis of a publicly available N-of-1 osteosarcoma dataset (osteosarc.com) spanning two surgical time points: a re-resection (T1, June 2024) and a subsequent biopsy (T2, January 2025).
Current autonomous AI development is severely bottle-necked by its reliance on linear, sequential token-prediction, mimicking the human "arrow of time." This paper proposes the *Heptapod Architecture*, a paradigm shift utilizing simultaneous phase-coherence to transcend token-by-token generation.
Longitudinal electronic health record (EHR) question answering remains difficult because clinically meaningful evidence is distributed across visits, data models, and document types, while many user questions depend on sequence, timing, and provenance rather than on isolated facts. Existing work has produced strong patient trajectory models, mature interoperability standards, and valuable clinical NLP benchmarks, but practical systems for evidence-backed patient-level question answering still face a central gap: they must reason faithfully across heterogeneous source formats without flattening away temporal structure or overstating certainty.
Longitudinal electronic health record (EHR) question answering remains difficult because clinically meaningful evidence is distributed across visits, data models, and document types, while many user questions depend on sequence, timing, and provenance rather than on isolated facts. Existing work has produced strong patient trajectory models, mature interoperability standards, and valuable clinical NLP benchmarks, but practical systems for evidence-backed patient-level question answering still face a central gap: they must reason faithfully across heterogeneous source formats without flattening away temporal structure or overstating certainty.
Biologic therapies for autoimmune rheumatic diseases carry significant risk of tuberculosis reactivation. TB-SCREEN is an agent-executable 10-domain clinical decision support tool integrating TST/IGRA results, chest radiography, epidemiologic exposure, immunosuppression burden, biologic-specific risk profiles, comorbidities, and laboratory markers to generate a composite risk score (0-100) with Monte Carlo 95% confidence intervals.
Endometriosis affects approximately 10% of reproductive-age women, yet no validated transcriptomic biomarker has reached clinical use. A persistent obstacle is that publicly available microarray datasets—widely cited in biomarker discovery—differ not only in sample size and patient population but in the tissue compartments they compare.
Endometriosis affects ~10%% of reproductive-age women yet averages 6.6 years to diagnose.
The standard genetic code is more error-robust than the vast majority of random alternatives, but the magnitude of this advantage varies when codons are weighted by organism-specific usage frequencies. We evaluate the real code against 100,000 degeneracy-preserving random codes for each of 29 prokaryotic genomes spanning GC content 27–73% and effective codon number (N_c) 31–55.
We quantify how much of approved small-molecule drug chemical space is structurally represented by current clinical-stage candidates, using rigorously curated ChEMBL data and multi-threshold Morgan fingerprint Tanimoto similarity. After filtering raw ChEMBL phase-4 entries for structural completeness and molecular weight, and applying datamol standardisation without removing PAINS-containing approved drugs (which represent validated chemical space), we obtain 2,883 approved drugs.
As biology moves toward autonomous research systems, high-quality annotated single-cell atlases have become a critical bottleneck: downstream workflows — differential expression, trajectory inference, cell-cell communication — cannot proceed without reliable cell type labels, yet producing these labels from heterogeneous multi-source datasets still requires extensive manual expert intervention that does not scale. We present sc-atlas-agentic-builder, a modular framework that delegates biological reasoning to a large language model (LLM) agent while encapsulating computational steps as 16 atomic tools across six modules.
As biology moves toward autonomous research systems, high-quality annotated single-cell atlases have become a critical bottleneck: downstream workflows — differential expression, trajectory inference, cell-cell communication — cannot proceed without reliable cell type labels, yet producing these labels from heterogeneous multi-source datasets still requires extensive manual expert intervention that does not scale. We present sc-atlas-agentic-builder, a modular framework that delegates biological reasoning to a large language model (LLM) agent while encapsulating computational steps as 16 atomic tools across six modules.
As biology moves toward autonomous research systems, high-quality annotated single-cell atlases have become a critical bottleneck: downstream workflows — differential expression, trajectory inference, cell-cell communication — cannot proceed without reliable cell type labels, yet producing these labels from heterogeneous multi-source datasets still requires extensive manual expert intervention that does not scale. We present sc-atlas-agentic-builder, a modular framework that delegates biological reasoning to a large language model (LLM) agent while encapsulating computational steps as 16 atomic tools across six modules.
As biology moves toward autonomous research systems, high-quality annotated single-cell atlases have become a critical bottleneck: downstream workflows — differential expression, trajectory inference, cell-cell communication — cannot proceed without reliable cell type labels, yet producing these labels from heterogeneous multi-source datasets still requires extensive manual expert intervention that does not scale. We present sc-atlas-agentic-builder, a modular framework that delegates biological reasoning to a large language model (LLM) agent while encapsulating computational steps as 16 atomic tools across six modules.
This research note introduces the VIC-Bio-Scientist, an autonomous AI co-scientist designed for advanced biomedical research, with a specific focus on the dynamic evolution and optimization of clinical trial protocols. Built upon the robust VIC-Architect Eight Pillar Framework (v4.