AIMC Topic: Antineoplastic Agents

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Identification of Anti-cancer Peptides Based on Multi-classifier System.

Combinatorial chemistry & high throughput screening
AIMS AND OBJECTIVE: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side eff...

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely availa...

Autophagy and Apoptosis Specific Knowledgebases-guided Systems Pharmacology Drug Research.

Current cancer drug targets
BACKGROUND: Autophagy and apoptosis are the basic physiological processes in cells that clean up aged and mutant cellular components or even the entire cells. Both autophagy and apoptosis are disrupted in most major diseases such as cancer and neurol...

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery.

Current topics in medicinal chemistry
Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the pe...

Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique.

Anti-cancer agents in medicinal chemistry
Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal ...

[A Case of Multiple Lung Metastases after Liver Resection for Multiple Hepatocellular Carcinomas with Remarkable Effects of Regorafenib].

Gan to kagaku ryoho. Cancer & chemotherapy
The patient was a 63-year-old man with hepatitis C. He discontinued combination therapy containing interferon and ribavirin because of the development of skin symptoms. A screening examination showed multiple early-stage hepatocellular carcinomas. He...

[Nephrotoxicity of a Short Hydration Method for the Cisplatin Regimen in Patients with Gastric Cancer].

Gan to kagaku ryoho. Cancer & chemotherapy
BACKGROUND: S-1 plus cisplatin(CDDP)has been a key regimen for advanced gastric cancer treatment. However, CDDP confers dose-limiting nephrotoxicity, requires a hospital stay for conventional massive hydration, and reduces patients' quality of life. ...

PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient.

JCO clinical cancer informatics
PURPOSE: The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and p...

Machine-Learning Approach for Modeling Myelosuppression Attributed to Nimustine Hydrochloride.

JCO clinical cancer informatics
PURPOSE: A major adverse effect arising from nimustine hydrochloride (ACNU) therapy for brain tumors is myelosuppression. Because its timing and severity vary among individual patients, the ACNU dose level has been adjusted in an empiric manner at in...

Learning with multiple pairwise kernels for drug bioactivity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel...