Biology subjects

Chen, E. Z.

Publications and source records attributed to Chen, E. Z..

4 recordsLinked to original sources

bioRxiv2026

SEME enables programmable transcript reconstruction through synthetic microexon incorporation

Precise correction of pathogenic mutations remains challenging for therapeutic applications because genome editing within protein-coding exons can generate unintended insertion-deletion byproducts that disrupt coding integrity and protein function. To address this limitation, we developed the Spliceable Editable Microexon Element (SEME), an intron-targeting platform that enables programmable microexon incorporation through endogenous splicing. In induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), SEME corrected aberrant splicing caused by the dilated cardiomyopathy-associated FLNC c.2003A>G variant by restoring the five nucleotides missing from exon 12, thereby recovering filamin C expression. Together, these findings establish programmable microexon incorporation as a proof-of-concept strategy for correcting diverse disease-causing transcript defects.

bioengineering↗

bioRxiv2018

Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations

Skin lesion is a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of skin lesion is extremely challenging manually visualization. Hence, reliable automatic classification of skin lesions is meaningful to improve pathologists accuracy and efficiency. In this paper, we proposed a two-stage method to combine deep learning features and clinical criteria representations to address skin lesion automated diagnosis task. Stage 1 - feature encoding: Modified deep convolutional neural networks (CNNs, in this paper, we used Dense201 and Res50) were fine-tuned to extract rich image global features. To avoid hair noisy, we developed a lesion segmentation U-Net to mask out the decisive regions and used the masked image as CNNs inputs. In addition, color features, texture features and morphological features were exacted based on clinical criteria; Stage 2 - features fusion: LightGBM was used to select the salient features and model parameters, predicting diagnosis confidence for each category. The proposed deep learning frameworks were evaluated on the ISIC 2018 dataset. Experimental results show the promising accuracies of our frameworks were achieved.

bioinformatics↗

bioRxiv2018

Skin Lesion Segmentation with Improved C-UNet Networks

--This paper proposes an innovative method for Part1, skin lesion segmentation of the ISIC 2018 Challenge. Our network C-UNet is based on UNet network, we combined several methods on this basic network which made some improvements on Jaccard Index ultimately, our method yield an average Jaccard Index of 0.77 on the On-line validation dataset.

bioengineering↗

bioRxiv2018

Lesion Attributes Segmentation for Melanoma Detection with Deep Learning

Melanoma is the most deadly form of skin cancer worldwide. Many efforts have been made for early detection of melanoma. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to help the diagnosis of melanoma based on dermoscopic images. In this paper, we describe our solutions for the task 2 of ISIC 2018 Challenges. We present two deep learning approaches to automatically detect lesion attributes of melanoma, one is a multi-task U-Net model and the other is a Mask R-CNN based model. Our multi-task U-Net model achieved a Jaccard index of 0.433 on official test data, which ranks the 5th place on the final leaderboard. The code for our solutions is publicly available.

bioinformatics↗