Genetics

TCUP – An Open Access Tool to Predict Tissue of Origin and Cancer of Unknown Primary (CUP)

Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We present TCUP, an ensemble learning framework that combines Contrastive Autoencoders (CAE) and Siamese Neural Networks (SNN) with base classifiers and a meta-learning layer to classify and interpret CUP, adding biological insight through Monte-Carlo ab...

Characterization of metabolic phenotypes in breast cancer through the integration of genome-scale metabolic models and machine learning

The metabolic heterogeneity of breast cancer represents a significant challenge for the identification of biomarkers and therapeutic targets. To address this problem, we integrated genome-scale metabolic models with machine learning algorithms, aiming to characterize the metabolic phenotypes associated with the disease. There were 90 specific metabolic models generated from clinical and gene expre...

RNA-seq derived sequence variations are excellent features for cell line identification

Cell lines are indispensable models for analyzing molecular mechanisms underlying human diseases. However, incorrect annotation and cross-contaminatio...

Variant effect prediction with reliability estimation across priority viruses

Viruses pose a significant threat to global health due to their rapid evolution, adaptability, and increasing potential for cross-species transmission...

Semi-supervised detection of natural selection with positive-unlabeled learning

Identifying genomic regions shaped by natural selection is a central goal in evolutionary ge-nomics. Existing machine learning methods for this task a...

Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck Cancer

Patient-derived organoids (PDOs) are emerging as advanced 3D ex vivo novel alternative method (NAM) preclinical models, offering significant advantage...

TissueFormer: a neural network for labeling tissue from grouped single-cell RNA profiles

Single-cell RNA sequencing technologies have enabled unprecedented insights into gene expression and are poised to transform clinical diagnostics. At ...

Mapping antigenic evolution of influenza A virus using deep learning-based prediction of hemagglutination inhibition titers

Seasonal influenza remains a significant public health challenge through unpredictable antigenic drift, where accumulated mutations enable immune evas...

“Frustratingly easy” domain adaptation for cross-species transcription factor binding prediction

Understanding how DNA sequence encodes gene regulation remains a central challenge in genomics. While deep learning models can predict regulatory acti...

Out-of-the-box bioinformatics capabilities of large language models (LLMs)

Large Language Models (LLMs), AI agents and co-scientists promise to accelerate scientific discovery across fields ranging from chemistry to biology. ...

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning

Investigating the lethal effect of multi-gene knockout is essential for discovering novel antibiotics targets and metabolic engineering. Unlike single...

Uncovering Cas9 PAM diversity through metagenomic mining and machine learning

Recognition of protospacer adjacent motifs (PAMs) is crucial for target site recognition by CRISPR–Cas systems. In genome editing applications, the re...

Cytoplasmic dynamics are overlooked in single nuclei RNA-seq but can be rescued by CytoRescue, a generative AI model to recover cytoplasm enriched gene

Single-nucleus RNA sequencing (snRNA-seq) generates single cell data from nuclei. It provides valuable compatibility with frozen or difficult-to-disso...

A Diffusion-Based Autoencoder for Learning Patient-Level Representations from Single-Cell Data

Single-cell RNA sequencing (scRNA-seq) offers insights into cellular heterogeneity and tissue composition, yet leveraging this data for patient-level ...

Cost-effective genomic prediction for fertility traits: A comparison of machine learning and conventional models using low-coverage sequencing in Holstein heifers

Fertility is one of the major factors affecting the efficiency of dairy herd, and genomic selection (GS) on milk yield, while ignoring fertility, has ...

Neural Network-Enhanced Investigation of Ferroptosis and Druggability in Early-Onset Alzheimer’s Disease

Alzheimer’s disease (AD) is a complex neurodegenerative disorder which is multifactorial in nature. Some of its characteristics are slow cognitive dec...

Improving RNA Secondary Structure Prediction Through Expanded Training Data

In recent years, deep learning has revolutionized protein structure prediction, achieving remarkable speed and accuracy. RNA structure prediction, how...

A contextualised protein language model reveals the functional syntax of bacterial evolution

Bacteria have evolved a vast diversity of functions and behaviours which are currently incompletely understood and poorly predicted from DNA sequence ...

An Agentic AI Framework for Ingestion and Standardization of Single-Cell RNA-seq Data Analysis

The proliferation of publicly available single-cell RNA sequencing (scRNA-seq) data has created significant opportunities in biomedical research. Howe...

Reengineering the antigen optimization process for superior neoantigen vaccine design

Identifying effective neoantigen sequences is essential for enhancing anti-tumor immunity. However, the vast sequence space (>109 possible peptides) a...