Latest AI and machine learning research in genetics for healthcare professionals.
For 3D face modeling, the recently developed 3D-aware neural rendering methods are able to render photorealistic face images with arbitrary viewing directions. The training of the parametric controllable 3D-aware face models, however, still relies on a large-scale dataset that is lab-collected. To address this issue, this paper introduces "StyleMorpheus", the first style-based neural 3D Morphabl...
Spasticity is a common movement disorder symptom in individuals with cerebral palsy, hereditary spastic paraplegia, spinal cord injury and stroke, being one of the most disabling features in the progression of these diseases. Despite the potential benefit of using wearable robots to treat spasticity, their use is not currently recommended to subjects with a level of spasticity above ${1^+}$ on t...
Current RGBT tracking methods often overlook the impact of fusion location on mitigating modality gap, which is key factor to effective tracking. Ou...
Genotype-to-Phenotype (G2P) prediction plays a pivotal role in crop breeding, enabling the identification of superior genotypes based on genomic dat...
Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional ne...
Hi-C technology measures genome-wide interaction frequencies, providing a powerful tool for studying the 3D genomic structure within the nucleus. Ho...
In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development E...
Open-vocabulary object detection (OVD) aims to detect objects beyond the training annotations, where detectors are usually aligned to a pre-trained ...
Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims ...
The integrative analysis of histopathological images and genomic data has received increasing attention for survival prediction of human cancers. Ho...
Heterogeneous tabular data poses unique challenges in generative modelling due to its fundamentally different underlying data structure compared to ...
Single-cell RNA sequencing (scRNA-seq) offers detailed insights into cellular heterogeneity. Recent advancements leverage single-cell large language...
Repetitive DNA sequences underpin genome architecture and evolutionary processes, yet they remain challenging to classify accurately. Terrier is a d...
Code localization--identifying precisely where in a codebase changes need to be made--is a fundamental yet challenging task in software maintenance....
Securing image data in IoT networks and other insecure information channels is a matter of critical concern. This paper presents a new image encrypt...
Neurosymbolic (NeSy) AI studies the integration of neural networks (NNs) and symbolic reasoning based on logic. Usually, NeSy techniques focus on le...
Cis-regulatory elements (CREs), such as promoters and enhancers, are relatively short DNA sequences that directly regulate gene expression. The fitn...
While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding...
Accurate, noninvasive glioma characterization is crucial for effective clinical management. Traditional methods, dependent on invasive tissue sampli...
Model merging is a technique that combines multiple finetuned models into a single model without additional training, allowing a free-rider to cheap...