Genetics

Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection

Domain generalization (DG) for object detection aims to enhance detectors' performance in unseen scenarios. This task remains challenging due to complex variations in real-world applications. Recently, diffusion models have demonstrated remarkable capabilities in diverse scene generation, which inspires us to explore their potential for improving DG tasks. Instead of generating images, our metho...

Primer C-VAE: An interpretable deep learning primer design method to detect emerging virus variants

Motivation: PCR is more economical and quicker than Next Generation Sequencing for detecting target organisms, with primer design being a critical step. In epidemiology with rapidly mutating viruses, designing effective primers is challenging. Traditional methods require substantial manual intervention and struggle to ensure effective primer design across different strains. For organisms with la...

Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective

Meta-learning has been proposed as a promising machine learning topic in recent years, with important applications to image classification, robotics...

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.

Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologi...

Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.

Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insigh...

Integration of machine learning and genome-wide association study to explore the genomic prediction accuracy of agronomic trait in oats (Avena sativa L.).

Machine learning (ML) has garnered significant attention for its potential to enhance the accuracy of genomic predictions (GPs) in various economic cr...

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To captur...

[Prophylactic surgery and genetic counselling: What impact of the artificial intelligence?].

In the area of cancer predisposition, certain situations may lead to the discussion of prophylactic surgery. This is rarely strictly recommended and d...

COVID-19 Patients Benefitting From Remdesivir for Improved Survival: A Neural Network-Based Approach.

Conflicting results from randomized trials regarding the efficacy of remdesivir for COVID-19 have been reported. We aimed to develop a neural network ...

Enhancing single-cell classification accuracy using image conversion and deep learning.

Single-cell transcriptome sequencing (scRNA-seq) is widely used in the fields of animal and plant developmental biology and important trait analysis b...

Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma.

Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycoli...

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key comp...

stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.

Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spati...

Towards an AI co-scientist

Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we int...

Enhancing DNA Foundation Models to Address Masking Inefficiencies

Masked language modelling (MLM) as a pretraining objective has been widely adopted in genomic sequence modelling. While pretrained models can succes...

Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models

Self-supervised learning (SSL) vision encoders learn high-quality image representations and thus have become a vital part of developing vision modal...

Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam

This paper comprehensively evaluates several recently proposed optimizers for 4-bit training, revealing that low-bit precision amplifies sensitivity...

[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].

Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesio...

Predicting gene essentiality and drug response from perturbation screens in preclinical cancer models with LEAP: Layered Ensemble of Autoencoders and Predictors

Preclinical perturbation screens, where the effects of genetic, chemical, or environmental perturbations are systematically tested on disease models...

UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction

Object extraction and segmentation from remote sensing (RS) images is a critical yet challenging task in urban environment monitoring. Urban morphol...