Genetics

Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions

The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolecules. Almost all research on large-scale biosequence transformers has focused on one domain at a time (single-omic), usually DNA/RNA or proteins. These models have seen incredible success in downstream tasks in each domain, and have achieved particul...

A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis

The rapid advancement of single-cell ATAC sequencing (scATAC-seq) technologies holds great promise for investigating the heterogeneity of epigenetic landscapes at the cellular level. The amplification process in scATAC-seq experiments often introduces noise due to dropout events, which results in extreme sparsity that hinders accurate analysis. Consequently, there is a significant demand for the...

HEK-Omics: The promise of omics to optimize HEK293 for recombinant adeno-associated virus (rAAV) gene therapy manufacturing

Gene therapy is poised to transition from niche to mainstream medicine, with recombinant adeno-associated virus (rAAV) as the vector of choice. Howe...

Personalised Medicine: Establishing predictive machine learning models for drug responses in patient derived cell culture

The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for p...

Wave-LSTM: Multi-scale analysis of somatic whole genome copy number profiles

Changes in the number of copies of certain parts of the genome, known as copy number alterations (CNAs), due to somatic mutation processes are a hal...

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas.

Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable c...

Single-cell Curriculum Learning-based Deep Graph Embedding Clustering

The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell ...

Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection

Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. ...

Screen Them All: High-Throughput Pan-Cancer Genetic and Phenotypic Biomarker Screening from H&E Whole Slide Images

Many molecular alterations serve as clinically prognostic or therapy-predictive biomarkers, typically detected using single or multi-gene molecular ...

Quantum Annealing for Enhanced Feature Selection in Single-Cell RNA Sequencing Data Analysis

Feature selection is vital for identifying relevant variables in classification and regression models, especially in single-cell RNA sequencing (scR...

Predicting the genetic component of gene expression using gene regulatory networks

Gene expression prediction plays a vital role in transcriptome-wide association studies (TWAS), which seek to establish associations between tissue ...

Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding

Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely gen...

Federated Fairness Analytics: Quantifying Fairness in Federated Learning

Federated Learning (FL) is a privacy-enhancing technology for distributed ML. By training models locally and aggregating updates - a federation lear...

Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks

The application of machine learning to transcriptomics data has led to significant advances in cancer research. However, the high dimensionality and...

Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder

Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia. By combinin...

PhaGO: Protein function annotation for bacteriophages by integrating the genomic context

Bacteriophages are viruses that target bacteria, playing a crucial role in microbial ecology. Phage proteins are important in understanding phage bi...

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nu...

scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data

Single-cell RNA sequencing (scRNA-seq) data analysis is pivotal for understanding cellular heterogeneity. However, the high sparsity and complex noi...

High-Throughput Phenotyping of Clinical Text Using Large Language Models

High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This s...

DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 on-target editing efficiency in specific cellular contexts.

MOTIVATION: CRISPR/Cas9 technology has been revolutionizing the field of gene editing. Guide RNAs (gRNAs) enable Cas9 proteins to target specific geno...