Biology subjects

Alghamdi, S.

Publications and source records attributed to Alghamdi, S..

3 recordsLinked to original sources

bioRxiv2025

De novo single-cell biological analysis of drug resistance in human melanoma through a novel deep learning-powered approach

Elucidating drug response mechanisms in human melanoma is crucial for improving treatment outcomes. Although scRNA-seq captures gene expression at the individual cell level, existing tools to gain deeper insights into a studied population pertaining to melanoma drug resistance are far from perfect. Therefore, we propose a deep learning (DL)-based approach to unveil molecular mechanisms underlying melanoma drug resistance as follows. First, we processed two single-cell datasets related to human melanoma from GEO (GSE108383_A375 and GSE108383_451Lu) database and trained a fully connected neural network with five adapted methods (L1-Regularization, DeepLIFT, SHAP, IG, LRP) to discriminate between BRAFi-resistant and parental cell lines, followed by identifying top 100 genes. Compared to existing bioinformatics tools from a biological perspective, the presented DL-based methods identified more expressed genes in four well-established melanoma cell lines, including MALME-3M, MDA-MB435, SK-MEL-28, and SK-MEL-5. Moreover, we identified FDA-approved melanoma drugs (e.g., Vemurafenib and Dabrafenib), critical genes such as ARAF, SOX10, DCT, AXL, and key TFs including MITF and TFAP2A. From a classification perspective, we provided gene sets by all methods to three machine learning algorithms, including support vector machine, random forests, and neural networks. Results demonstrate that Integrated gradients (IG) method adapted in our DL approach contributed to 2.2% and 0.5% overall performance improvements over the best-performing baselines when using A375 and 451Lu cell line datasets.

bioinformatics↗

bioRxiv2023

A novel interpretable deep transfer learning combining diverse learnable parameters for improved prediction of single-cell gene regulatory networks

Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect and do not provide appropriate interpretation as a guideline to explain and promote superior performance in the target task. Therefore, we provide an interpretable approach for our presented deep transfer learning (DTL) models to overcome such drawbacks, working as follows. We utilize several pre-trained models including SEResNet152, and SEResNeXt101. Then, we transfer knowledge from pre-trained models via keeping the weights in the convolutional base (i.e., feature extraction part) while modifying the classification part with the use of Adam optimizer to deal with classifying healthy controls and T2D based on single-cell gene regulatory network (SCGRN) images. Another DTL models work in a similar manner but just with keeping weights of the bottom layers in the feature extraction unaltered while updating weights of consecutive layers through training from scratch. Experimental results on the whole 224 SCGRN images using 5-fold cross-validation show that our model (TFeSEResNeXT101) achieving the highest average balanced accuracy (BAC) of 0.97 and thereby significantly outperforming the baseline that resulted in an average BAC of 0.86. Moreover, the simulation study demonstrated that the superiority is attributed to the distributional conformance of model weight parameters obtained with Adam optimizer when coupled with weights from a pre-trained model.

bioinformatics↗

bioRxiv2020

Wt1-expressing cells contribute to mesoderm-derived tissues in intestine and mesentery in two distinct phases during murine embryonic development.

Previously, genetic lineage tracing based on the mesothelial marker Wt1, appeared to show that peritoneal mesothelial cells have a range of differentiative capacities and are the direct progenitors of vascular smooth muscle in the intestine. However, it was not clear whether this was a temporally limited process or continued throughout postnatal life. Here, using a conditional Wt1-based genetic lineage tracing approach, we demonstrate that the postnatal and adult peritoneum covering intestine, mesentery and body wall only maintained itself and failed to contribute to other visceral tissues. Pulse-chase experiments of up to 6 months revealed that Wt1-expressing cells remained confined to the peritoneum and failed to differentiate into cellular components of blood vessels or other tissues underlying the peritoneum. Ablation of Wt1 in adult mice did not result in changes to the intestinal wall architecture. In the heart, we observed that Wt1-expressing cells maintained the epicardium and contributed to coronary vessels in newborn and adult mice. Our results demonstrate that Wt1-expressing cells in the peritoneum have limited differentiation capacities, and that contribution of Wt1-expressing cells to cardiac vasculature is based on organ-specific mechanisms.

developmental biology↗