Genetics

DeepSS2GO: protein function prediction from secondary structure.

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the ...

BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance nee...

Optimal fusion of genotype and drug embeddings in predicting cancer drug response.

Predicting cancer drug response using both genomics and drug features has shown some success compared to using genomics features alone. However, there...

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wid...

Prediction of DNA i-motifs via machine learning.

i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative...

NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosi...

Understanding gut microbiome-based machine learning platforms: A review on therapeutic approaches using deep learning.

Human beings possess trillions of microbial cells in a symbiotic relationship. This relationship benefits both partners for a long time. The gut micro...

[CHARACTERIZATION OF THE ADAPTIVE IMMUNE REPERTOIRE USING NEXT GENERATION SEQUENCING: RECENT DISCOVERIES IN THE FIELD OF PRIMARY IMMUNODEFICIENCY, AND THE UPCOMING FUTURE].

A powerful adaptive immune system, which includes cellular (T lymphocytes) and humoral (B lymphocytes) immunity, depends on its ability to recognize a...

Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

BACKGROUND: Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familia...

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarbox...

Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.

BACKGROUND: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achi...

Global trends and hotspots of ulcerative colitis based on bibliometric and visual analysis from 1993 to 2022.

Ulcerative colitis (UC) has seen a significant increase over the past 3 decades. However, our understanding of its etiology, pathogenesis, and pharmac...

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.

MOTIVATION: Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although po...

scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.

MOTIVATION: Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been deve...

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging adva...

scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.

Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understan...

Deqformer: high-definition and scalable deep learning probe design method.

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing...

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical lim...

CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction.

CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative techn...

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.

Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for unde...