Latest AI and machine learning research in genetics for healthcare professionals.
In recent years, tensor decomposition-based approaches for hyperspectral anomaly detection (HAD) have gained significant attention in the field of remote sensing. However, existing methods often fail to fully leverage both the global correlations and local smoothness of the background components in hyperspectral images (HSIs), which exist in both the spectral and spatial domains. This limitation...
As the marginal cost of scaling computation (data and parameters) during model pre-training continues to increase substantially, test-time scaling (TTS) has emerged as a promising direction for improving generative model performance by allocating additional computation at inference time. While TTS has demonstrated significant success across multiple language tasks, there remains a notable gap in...
De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inher...
De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inher...
BACKGROUND: This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy...
Reverse transcription-polymerase chain reaction (RT-PCR) is the reference standard for COVID-19 diagnosis, but the need for rapid, reproducible, and c...
Evolution and learning are two distinct yet complementary forms of adaptation. While evolutionary processes operate across generations via the selec...
Large language models (LLMs) have revolutionized natural language processing and are increasingly applied to other sequential data types, including ...
Sequencing a genome to determine an individual's DNA produces an enormous number of short nucleotide subsequences known as reads, which must be reas...
Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introdu...
Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and cell identity dysregulation involving diseases. Her...
Chromosome analysis is vital for diagnosing genetic disorders and guiding cancer therapy decisions through the identification of somatic clonal aber...
Causal discovery in multi-omic datasets is crucial for understanding the bigger picture of gene regulatory mechanisms, but remains challenging due t...
Adversarial attacks aim to generate malicious inputs that mislead deep models, but beyond causing model failure, they cannot provide certain interpr...
Nonhuman primate (NHP) neuroanatomy and cognitive complexity make NHPs ideal models to study human neurobiology and disease. However, NHP circuit-func...
Friedreich ataxia (FRDA) is a rare, inherited progressive movement disorder for which there is currently no cure. The field urgently requires more sen...
Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by bi...
Many familial diseases are caused by genetic accidents, which affect both the genome and its epigenetic environment, expressed as an interaction gra...
Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human e...
Vision-Language Models (VLMs) have demonstrated impressive capabilities across a range of tasks, yet concerns about their potential biases exist. Th...