Fast, memory-efficient genomic interval tokenizers for modern machine learning

arXiv — cs.LGTuesday, November 4, 2025 at 5:00:00 AM
Researchers have developed gtars-tokenizers, a new tool designed to efficiently handle genomic interval data for deep learning applications. As genomic datasets become increasingly complex and voluminous, this innovation is crucial for enabling more effective analysis and interpretation of genetic information. By providing a consistent vocabulary for diverse datasets, gtars-tokenizers could significantly enhance the capabilities of machine learning in genomics, paving the way for breakthroughs in personalized medicine and genetic research.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps
Continue Readings
E-CHUM: Event-based Cameras for Human Detection and Urban Monitoring
NeutralArtificial Intelligence
A recent study titled 'E-CHUM: Event-based Cameras for Human Detection and Urban Monitoring' explores the evolution of urban monitoring technologies, emphasizing the advantages of event-based cameras that capture changes in light intensity. These cameras are particularly effective in low-light conditions, offering a significant improvement over traditional RGB cameras and other sensors.
Parametric Numerical Integration with (Differential) Machine Learning
PositiveArtificial Intelligence
A new methodology utilizing machine and deep learning has been introduced to effectively solve parametric integrals, demonstrating superior performance over traditional methods. This approach incorporates derivative information during training, which enhances its efficiency across various problem classes, including statistical functionals and differential equations.
NeuralOGCM: Differentiable Ocean Modeling with Learnable Physics
PositiveArtificial Intelligence
NeuralOGCM has been introduced as an innovative ocean modeling framework that integrates differentiable programming with deep learning, aiming to enhance scientific simulations by balancing computational efficiency and physical fidelity. This framework features a fully differentiable dynamical solver that utilizes physics knowledge and transforms key physical parameters into learnable components, allowing for autonomous optimization through end-to-end training.
Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
NeutralArtificial Intelligence
A recent study published on arXiv discusses the challenges of generalizing machine learning interatomic potentials (MLIPs) across diverse chemical spaces. The research emphasizes the necessity of long-range corrections to enhance both in-distribution performance and transferability to previously unseen chemical environments.
CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
NeutralArtificial Intelligence
A new framework called CORL has been introduced to enhance the performance of mixed integer linear programs (MILPs) through reinforcement learning (RL), addressing the limitations of traditional branch and bound (B&B) methods. This approach allows for fine-tuning MILP schemes using real-world data, aiming to improve decision-making quality in complex scenarios.
DoDo-Code: an Efficient Levenshtein Distance Embedding-based Code for 4-ary IDS Channel
PositiveArtificial Intelligence
A novel method for designing high-code-rate single-IDS-correcting codewords has been introduced, leveraging deep Levenshtein distance embedding to enhance the efficiency of the 4-ary IDS channel. This development addresses the challenges posed by insertion, deletion, and substitution errors in data transmission, which have gained attention due to evolving storage and communication technologies.
To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
NeutralArtificial Intelligence
The Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm is under scrutiny as researchers explore the implications of shuffling training data, a method that has gained popularity due to its efficiency and lower computational costs. However, the challenge remains in establishing accurate theoretical privacy guarantees when using shuffling, leading to potential discrepancies in privacy assessments compared to traditional Poisson subsampling methods.
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
PositiveArtificial Intelligence
A new framework named HEIST has been introduced to enhance the analysis of spatial transcriptomics and proteomics data, addressing the limitations of existing models that overlook spatial information and complex cellular programs. This model aims to provide insights into cellular heterogeneity and gene expression at the single-cell level by incorporating spatial coordinates and intra-cellular counts.

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about