Genetics

MolGene-E: Inverse Molecular Design to Modulate Single Cell Transcriptomics

Designing drugs that can restore a diseased cell to its healthy state is an emerging approach in systems pharmacology to address medical needs that conventional target-based drug discovery paradigms have failed to meet. Single-cell transcriptomics can comprehensively map the differences between diseased and healthy cellular states, making it a valuable technique for systems pharmacology. However, ...

Linearizing Vision Transformer with Test-Time Training

While linear-complexity attention mechanisms offer a promising alternative to Softmax attention for overcoming the quadratic bottleneck, training such models from scratch remains prohibitively expensive. Inheriting weights from pretrained Transformers provides an appealing shortcut, yet the fundamental representational gap between Softmax and linear attention prevents effective weight transfer. In...

DoFormer: Causal Transformer for Gene Perturbation

Learning causal gene regulatory mechanisms from single-cell data, and thereby predicting the effects of unseen perturbations, remains challenging. Obs...

Mitigating Family Effects in RNA Secondary-Structure Prediction with Latent-Space Continual Learning

Accurate RNA secondary-structure prediction remains difficult despite decades of thermodynamics-based algorithms and the advent of deep-learning archi...

Deep learning cell type classification using nuclear DNA patterns

Multicellular organisms comprise various types of cells, which are characterized by gene expression through interactions between chromosomal DNA and n...

Species-specific transformer models of bacterial gene order and content for genomic surveillance tasks

Transformer models enable functionally meaningful representation of complex biological data, such as nucleotide or protein sequences. Existing foundat...

Systems Pharmacology Reveals Type I Interferon and Myeloid-Like B Cell Reprogramming as Druggable Axes in Antiphospholipid Syndrome

Antiphospholipid syndrome (APS) lacks targeted therapies beyond anticoagulation, and its molecular heterogeneity remains poorly characterized. We empl...

Overcoming systematic data biases enables accurate prediction of enzyme kcat fold-changes for computational protein design

Machine learning is increasingly used to guide protein engineering by predicting how mutations affect desired properties. Recent models for the turnov...

Molecular Translators as a Computational Primitive for Biomarker Discovery: Learnability Gains Under Conserved Information Ceilings

Virtual molecular mapping systems such as MISO and GigaTIME introduce a potentially transformative primitive in computational pathology: translation o...

Hybrid CNN and Multi-Head Attention Model for Analyzing Epigenetic Mechanisms and Gene Expression Across Fungal Phylogenetic Distances

Understanding gene expression is crucial for optimizing biological processes in bioeconomic processes, human health, and environmental regulation. Epi...

Multidimensional Mechanistic Study of Panax Notoginseng Saponins in the Treatment of Alcohol-Induced Osteonecrosis of the Femoral Head: Integrating Network Pharmacology, Molecular Dynamics Simulation and In Vivo Validation

Background: Alcohol-induced osteonecrosis of the femoral head (AIONFH) is an orthopedic disorder from chronic alcohol abuse, characterized by disrupte...

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computat...

DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures

Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal componen...

Advancing ab initio genome annotation with OrionGeno

The rapid expansion of eukaryotic genome sequencing has created an urgent demand for scalable and accurate gene annotation, particularly for large-sca...

Scalable machine learning improves resistance prediction and identifies novel determinants in Mycobacterium tuberculosis

Multidrug-resistant and extensively drug-resistant Mycobacterium tuberculosis (MTB) represents a growing global health crisis, characterized by limite...

KAYRA: A Microservice Architecture for AI-Assisted Karyotyping with Cloud and On-Premise Deployment

We present KAYRA, an end-to-end karyotyping system that operates inside the operational constraints of a clinical cytogenetic laboratory. KAYRA is arc...

Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport

We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine t...

A Multi-modal LLM-Knowledge Fusion Framework for Predicting Single-cell Genetic Perturbation Effects

Understanding cellular responses to genetic perturbations is fundamental for drug discovery, yet experimental approaches face significant limitations ...

Accurate ab initio gene prediction in eukaryotes with Tiberius in multiple clades

Eukaryotic genome annotation is currently bottlenecked by limitations in the generality, scalability and accuracy of computational methods. Deep learn...

Learning dynamics of unsupervised deep learning reveal epoch-specific genetic architectures of brain morphology

Representation learning is an emerging paradigm for deriving phenotypes from complex measurements (e.g., imaging) for genetic discovery. However, the ...