Genetics

Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification

The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. While modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains under-explored. In this paper, we follow the guidance of the central dogma to redesign both the ...

GENERator: A Long-Context Generative Genomic Foundation Model

Advancements in DNA sequencing technologies have significantly improved our ability to decode genomic sequences. However, the prediction and interpretation of these sequences remain challenging due to the intricate nature of genetic material. Large language models (LLMs) have introduced new opportunities for biological sequence analysis. Recent developments in genomic language models have unders...

A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer

The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and sele...

KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical ...

Universal Approximation of Visual Autoregressive Transformers

We investigate the fundamental limits of transformer-based foundation models, extending our analysis to include Visual Autoregressive (VAR) transfor...

QuantumDNA: A Python Package for Analyzing Quantum Charge Dynamics in DNA and Exploring Its Biological Relevance

The study of DNA charge dynamics is a highly interdisciplinary field that bridges physics, chemistry, biology, and medicine, and plays a critical ro...

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.

To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on lar...

Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data.

Rates of transcription elongation vary within and across eukaryotic gene bodies. Here, we introduce new methods for predicting elongation rates from n...

Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time.

In infected individuals, viruses are present as a population consisting of dominant and minor variant genomes. Most databases contain information on t...

G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion

Understanding how genes influence phenotype across species is a fundamental challenge in genetic engineering, which will facilitate advances in vari...

Unravelling Causal Genetic Biomarkers of Alzheimer's Disease via Neuron to Gene-token Backtracking in Neural Architecture: A Groundbreaking Reverse-Gene-Finder Approach

Alzheimer's Disease (AD) affects over 55 million people globally, yet the key genetic contributors remain poorly understood. Leveraging recent advan...

Diffusion-empowered AutoPrompt MedSAM

MedSAM, a medical foundation model derived from the SAM architecture, has demonstrated notable success across diverse medical domains. However, its ...

Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning

Large Language Models (LLMs) demonstrate remarkable generalizability across diverse tasks, yet genomic foundation models (GFMs) still require separa...

Diagnosis of Alzheimer's disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data.

Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of Alzheimer's disease is beneficial for its prevention ...

Position: Stop Acting Like Language Model Agents Are Normal Agents

Language Model Agents (LMAs) are increasingly treated as capable of autonomously navigating interactions with humans and tools. Their design and dep...

scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis

Functional MRI (fMRI) and single-cell transcriptomics are pivotal in Alzheimer's disease (AD) research, each providing unique insights into neural f...

Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data

The exploration of cellular heterogeneity within the tumor microenvironment (TME) via single-cell RNA sequencing (scRNA-seq) is essential for unders...

Graph Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a widely used technique for estimating associations between two sets of multi-dimensional variables. Recent ...

TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites

Transcription factors are proteins that regulate the expression of genes by binding to specific genomic regions known as Transcription Factor Bindin...

Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics

Recent technological advances have introduced new high-throughput methods for studying host-virus interactions, but testing synergistic interactions...