Biology subjects

Naderi, N.

Publications and source records attributed to Naderi, N..

3 recordsLinked to original sources

bioRxiv2024

Comprehensive Transcriptomic Analysis of Hepatocellular Carcinoma: Uncovering Shared and Unique Molecular Signatures Across Diverse Etiologies

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified over 9,000 differentially expressed genes (DEGs), with 125 genes consistently altered across multiple datasets, underscoring their potential as critical biomarkers for HCC. Notably, we observed significant dysregulation in pathways related to cell cycle regulation, immune response, and metabolic processes. The integration of these DEGs across various HCC subtypes provides novel insights into the molecular heterogeneity of HCC, offering promising avenues for the development of targeted therapies and personalized medicine. This extensive repository of DEGs serves as a valuable resource for the scientific community, facilitating further research into the underlying mechanisms of HCC and the pursuit of improved diagnostic and therapeutic strategies.

bioinformatics↗

bioRxiv2023

BioWiC: An Evaluation Benchmark for Biomedical Concept Representation

Due to the complexity of the biomedical domain, the ability to capture semantically meaningful representations of terms in context is a long-standing challenge. Despite important progress in the past years, no evaluation benchmark has been developed to evaluate how well language models represent biomedical concepts according to their corresponding context. Inspired by the Word-in-Context (WiC) benchmark, in which word sense disambiguation is reformulated as a binary classification task, we propose a novel dataset, BioWiC, to evaluate the ability of language models to encode biomedical terms in context. We evaluate BioWiC both intrinsically and extrinsically and show that it could be used as a reliable benchmark for evaluating context-dependent embeddings in biomedical corpora. In addition, we conduct several experiments using a variety of discriminative and generative large language models to establish robust baselines that can serve as a foundation for future research.

bioinformatics↗

bioRxiv2019

UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications

In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliography in UniProt, we investigate a Convolution Neural Network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge to categorize publications at the accession annotation level is that the same publication can be annotated with multiple proteins, and thus be associated to different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achieved a F1-score of 0.72, outperforming baseline models based on logistic regression and support vector machine by up to 22 and 18 percentage points, respectively. We believe that such approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators to decide whether a publication is relevant for further curation for a protein accession.

bioinformatics↗