AIMC Topic: Neoplasms

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FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Briefings in bioinformatics
Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of can...

scCancer: a package for automated processing of single-cell RNA-seq data in cancer.

Briefings in bioinformatics
Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenv...

AntiCP 2.0: an updated model for predicting anticancer peptides.

Briefings in bioinformatics
Increasing use of therapeutic peptides for treating cancer has received considerable attention of the scientific community in the recent years. The present study describes the in silico model developed for predicting and designing anticancer peptides...

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

Briefings in bioinformatics
The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in...

Predictors of bleeding event among elderly patients with mechanical valve replacement using random forest model: A retrospective study.

Medicine
Available classification tools and risk factors predicting bleeding events in elderly patients after mechanical valve replacement may not be suitable in Asian populations. Thus, we aimed to identify an accurate model for predicting bleeding in elderl...

[Aid of artificial intelligence for the anatomo-pathological diagnosis of tumours].

Revue medicale de Liege
The anatomo-pathological diagnosis of tumors is based on many criteria related mainly to image analysis. Currently, in most pathology laboratories, tissues or cells are placed on glass slides and directly analyzed with an optical microscope. Because ...

[Concepts of medical imaging in the work up and follow-up of cancer : oncological imaging at a glance].

Revue medicale de Liege
Oncological imaging is a subspecialty of medical imaging and focuses on the workup and the follow-up of cancer. Oncological imaging takes into account all the specificities of cancer diseases, which is a constantly evolving field, especially in the e...

Automated Identification of Patients With Immune-Related Adverse Events From Clinical Notes Using Word Embedding and Machine Learning.

JCO clinical cancer informatics
PURPOSE: Although immune checkpoint inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies, they are associated with a unique spectrum of side effects termed immune-related adverse events (irAEs). To ensure trea...

Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning.

Anticancer research
BACKGROUND/AIM: Many cancer patients face multiple primary cancers. It is challenging to find an anticancer therapy that covers both cancer types in such patients. In personalized medicine, drug response is predicted using genomic information, which ...

Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.

JCO clinical cancer informatics
PURPOSE: Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lack...