日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Biologically explainable multi-omics feature demonstrates greater learning potential by identifying tissue of origin, stages, and subtypes for pan-cancer classification

具有生物学可解释性的多组学特征通过识别组织来源、分期和亚型,展现出更大的学习潜力,可用于泛癌分类。

Munquad, Sana; Dash, Bikash Kumar; Sengupta, Sayan; Singh, Vishal; Ushakiran, Yerra; Das, Asim Bikas

Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma

揭示胶质瘤亚型特异性疾病模块并开发药物反应预测模型

Munquad, Sana; Das, Asim Bikas

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes

基于深度学习的胶质母细胞瘤亚型临床诊断辅助框架

Munquad, Sana; Si, Tapas; Mallik, Saurav; Das, Asim Bikas; Zhao, Zhongming