Latest AI and machine learning research in genetics for healthcare professionals.
The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instance, lncRNAs are predominantly associated with chromatin and act as regulators of gene transcription and chromatin structure, while mRNAs are distributed across the nucleus and ...
BACKGROUND: mAm is a specific RNA modification that plays an important role in regulating mRNA stability, translational efficiency, and cellular stress response. mAm's precise identification is essential to gain insight into its functional mechanisms at transcriptional and post-transcriptional levels. Due to the limitations of experimental assays, the development of efficient computational tools t...
Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but its complexity, which is marked by high dimen...
Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, ...
To overcome antimalarial drug resistance, carbohydrate derivatives as selective PfHT1 inhibitor have been suggested in recent experimental work with...
Telomere-related genes (TRGs) are vital in diverse tumor types. Nevertheless, there is a notable lack of in-depth research concerning their significan...
BACKGROUND: Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechan...
Recent advances in applying deep learning in genomics include DNA-language and single-cell foundation models. However, these models take only one da...
Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contex...
Objective: This study investigates the potential of Large Language Models (LLMs) as an alternative to human expert elicitation for extracting struct...
Even though Deep Neural Networks are extremely powerful for image restoration tasks, they have several limitations. They are poorly understood and s...
Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited. Typically, current open-source...
This paper introduces the class of grey-scale image stack operators as those that (a) map binary-images into binary-images and (b) commute in averag...
Single-molecule RNA imaging has been made possible with the recent advances in microscopy methods. However, systematic analysis of these images has ...
The presented study contributes to ongoing research that aims to overcome challenges in predicting the bio-applicability of nanoparticles. The appro...
Deep learning techniques have driven significant progress in various analytical tasks within 3D genomics in computational biology. However, a holist...
This paper addresses emulation algorithms for matrix multiplication. General Matrix-Matrix Multiplication (GEMM), a fundamental operation in the Bas...
Phylogenetic trees are simple models of evolutionary processes. They describe conditionally independent divergent evolution of taxa from common ance...
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict ...
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To une...