Genetics

Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings

Resolution generalization in image generation tasks enables the production of higher-resolution images with lower training resolution overhead. However, a significant challenge in resolution generalization, particularly in the widely used Diffusion Transformers, lies in the mismatch between the positional encodings encountered during testing and those used during training. While existing methods...

High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point c...

Machine Learning-Based Genomic Linguistic Analysis (Gene Sequence Feature Learning): A Case Study on Predicting Heavy Metal Response Genes in Rice

This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa)....

Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences

Deep learning architectures such as convolutional neural networks and Transformers have revolutionized biological sequence modeling, with recent adv...

A deep learning model trained on expressed transcripts across different tissue types reveals cell-type codon-optimization preferences.

Species-specific differences in protein translation can affect the design of protein-based drugs. Consequently, efficient expression of recombinant pr...

DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.

Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a chall...

Genomic data processing with GenomeFlow

Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public reposi...

Machine learning predictions from unpredictable chaos

Chaos is omnipresent in nature, and its understanding provides enormous social and economic benefits. However, the unpredictability of chaotic syste...

Efficient Data Selection for Training Genomic Perturbation Models

Genomic studies, including CRISPR-based PerturbSeq analyses, face a vast hypothesis space, while gene perturbations remain costly and time-consuming...

Functional classification of metabolic networks

Chemical reaction networks underpin biological and physical phenomena across scales, from microbial interactions to planetary atmosphere dynamics. B...

Decision Tree Induction Through LLMs via Semantically-Aware Evolution

Decision trees are a crucial class of models offering robust predictive performance and inherent interpretability across various domains, including ...

Streamlining SIMD ISA Extensions with Takum Arithmetic: A Case Study on Intel AVX10.2

Modern microprocessors extend their instruction set architecture (ISA) with Single Instruction, Multiple Data (SIMD) operations to improve performan...

Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning

How a single fertilized cell gives rise to a complex array of specialized cell types in development is a central question in biology. The cells grow...

Movement Sequencing: A Novel Approach to Quantifying the Building Blocks of Human Gait

By 2050, a quarter of the US population will be over the age of 65 with greater than a 40% risk of developing life-altering neuromusculoskeletal pat...

MT-PCR: Leveraging Modality Transformation for Large-Scale Point Cloud Registration with Limited Overlap

Large-scale scene point cloud registration with limited overlap is a challenging task due to computational load and constrained data acquisition. To...

Cancermorphic Computing Toward Multilevel Machine Intelligence

Despite their potential to address crucial bottlenecks in computing architectures and contribute to the pool of biological inspiration for engineeri...

Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model

Image generative models, particularly diffusion-based models, have surged in popularity due to their remarkable ability to synthesize highly realist...

GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction

Identifying transcription factor binding sites (TFBS) is crucial for understanding gene regulation, as these sites enable transcription factors (TFs...

Towards Learning High-Precision Least Squares Algorithms with Sequence Models

This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of leas...

Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes

Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studi...