including 8725 patients and 28 different cancer types, to develop HE2RNA, a deep-learning model based on a multitask weakly supervised approach24(architecture in the “Methods”). The model was trained to predict normalized gene expression data (logarithmic FPKM-UQ values, see “Methods...
We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as ...
However, clustering analysis of scRNA-seq data remains a statistical and computational challenge, due to the pervasive dropout events obscuring the data matrix with prevailing 鈥榝alse鈥?zero count observations. Here, we have developed scDeepCluster, a single-cell model-based deep embedded clustering...
In this paper, a machine learning-based model called LDACE was proposed to predict potential lncRNA-disease associations by combining Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN). Specifically, the representation vectors are constructed by integrating multiple types of biology i...
We employed four GNN variants: GCN, GAT, GIN and Graph Transformer with the combination of GIN, used for learning drug features. Subsequently, the drug-cell line pairs were used to predict LN IC50 values. Notably, our model combines drug molecule graphs with gene pathway activity scores, ...
To addresses this challenge, we developed a pre-trained cell-type annotation method, namely scDeepSort, using a state-of-the-art deep learning algorithm, i.e. a modified graph neural network (GNN) model. In brief, scDeepSort was constructed based on our weighted GNN framework and was then...
It is recommended you either have either l2norm_embed or post_emb_norm set to True but not both, as they probably serve the same purpose. import torch from x_transformers import TransformerWrapper, Decoder, Encoder model = TransformerWrapper( num_tokens = 20000, max_seq_len = 1024, post_...
A deep learning model to predict RNA-Seq expression of tumours from whole slide images The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for ... B Schmauch,A Romagnoni,E Pronier,... - 《Natu...
To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues. Our classifier achieved an average F1 ...
Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. However, clustering analysis of scRNA-seq data remains ...