AIMC Topic: Neoplasms

Clear Filters Showing 2131 to 2140 of 2356 articles

DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Briefings in bioinformatics
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...

SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.

Briefings in bioinformatics
Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biologica...

A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Briefings in bioinformatics
Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to un...

Anticancer peptides prediction with deep representation learning features.

Briefings in bioinformatics
Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, ...

MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

Briefings in bioinformatics
MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues a...

Identification of haploinsufficient genes from epigenomic data using deep forest.

Briefings in bioinformatics
Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been develop...

Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology.

JCO clinical cancer informatics
PURPOSE: Machine learning models developed from electronic health records data have been increasingly used to predict risk of mortality for general oncology patients. But these models may have suboptimal performance because of patient heterogeneity. ...

Customizable Natural Language Processing Biomarker Extraction Tool.

JCO clinical cancer informatics
PURPOSE: Natural language processing (NLP) in pathology reports to extract biomarker information is an ongoing area of research. MetaMap is a natural language processing tool developed and funded by the National Library of Medicine to map biomedical ...

[Application of neural network autoencoder algorithm in the cancer informatics research].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand c...

A graph auto-encoder model for miRNA-disease associations prediction.

Briefings in bioinformatics
Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and ...