Genetics

Genetics

Latest AI and machine learning research in genetics for healthcare professionals.

11,255 articles

Subcategories: Genetics

Showing 8275-8295 of 11,255 articles

ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics

Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic...

Nov 2024 http://arxiv.org/abs/2411.16793v1

Prob-cGAN: A Probabilistic Conditional Generative Adversarial Network for LSD1 Inhibitor Activity Prediction

The inhibition of Lysine-Specific Histone Demethylase 1 (LSD1) is a promising strategy for cancer ...

Nov 2024 http://arxiv.org/abs/2411.15483v1

BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.

Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for under...

Nov 2024 39584702

FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs.

Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular...

Nov 2024 39602828

Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer ...

Nov 2024 39701601

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin i...

Nov 2024 39708837

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A ...

Nov 2024 39708840

KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for...

Nov 2024 39749663

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.

Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identificatio...

Nov 2024 39757606

Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.

Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities...

Nov 2024 39800872

Deep learning in integrating spatial transcriptomics with other modalities.

Spatial transcriptomics technologies have been extensively applied in biological research, enabling ...

Nov 2024 39800876

scGO: interpretable deep neural network for cell status annotation and disease diagnosis.

Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in sing...

Nov 2024 39820437

scHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.

Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chro...

Nov 2024 39831891

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised ...

Nov 2024 39879386

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ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics

Prob-cGAN: A Probabilistic Conditional Generative Adversarial Network for LSD1 Inhibitor Activity Prediction

BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.

FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs.

Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.

Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.

Deep learning in integrating spatial transcriptomics with other modalities.

scGO: interpretable deep neural network for cell status annotation and disease diagnosis.

scHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

Classification-based pathway analysis using GPNet with novel P-value computation.

Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.

Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.

DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile.

Noninvasive fetal genotyping using deep neural networks.

Reducibility among NP-Hard graph problems and boundary classes

An Evolutional Neural Network Framework for Classification of Microarray Data

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