Machine intelligence in single-cell data analysis: advances and new challenges
The rapid development of single-cell technologies allows for dissecting cellular
heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of …
heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of …
A review of computational strategies for denoising and imputation of single-cell transcriptomic data
Motivation The advancements of single-cell sequencing methods have paved the way for
the characterization of cellular states at unprecedented resolution, revolutionizing the …
the characterization of cellular states at unprecedented resolution, revolutionizing the …
A weighted bilinear neural collaborative filtering approach for drug repositioning
Drug repositioning is an efficient and promising strategy for traditional drug discovery and
development. Many research efforts are focused on utilizing deep-learning approaches …
development. Many research efforts are focused on utilizing deep-learning approaches …
Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data
Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the
precise gene expression of individual cells and identify cell heterogeneity and …
precise gene expression of individual cells and identify cell heterogeneity and …
iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor
Motivation Enhancers are non-coding DNA fragments with high position variability and free
scattering. They play an important role in controlling gene expression. As machine learning …
scattering. They play an important role in controlling gene expression. As machine learning …
Drug repositioning based on weighted local information augmented graph neural network
Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is
pivotal in accelerating drug discovery. While many studies have engaged in modeling …
pivotal in accelerating drug discovery. While many studies have engaged in modeling …
Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning
K Huang, B Lin, J Liu, Y Liu, J Li, G Tian, J Yang - Bioinformatics, 2022 - academic.oup.com
Motivation Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of
immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB …
immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB …
Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder
Single-cell RNA sequencing (scRNA-seq) is a revolutionary breakthrough that determines
the precise gene expressions on individual cells and deciphers cell heterogeneity and …
the precise gene expressions on individual cells and deciphers cell heterogeneity and …
Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies
X Liu, P Yuan, R Li, D Zhang, J An, J Ju, C Liu… - Computers in biology …, 2022 - Elsevier
Abstract About 30%–40% breast cancer patients suffer from recurrence and metastasis,
even after targeted therapy like trastuzumab. Since breast cancer recurrence and metastasis …
even after targeted therapy like trastuzumab. Since breast cancer recurrence and metastasis …
ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data
Y Yao, Y Lv, L Tong, Y Liang, S Xi, B Ji… - Briefings in …, 2022 - academic.oup.com
Breast cancer patients often have recurrence and metastasis after surgery. Predicting the
risk of recurrence and metastasis for a breast cancer patient is essential for the development …
risk of recurrence and metastasis for a breast cancer patient is essential for the development …