AIMC Topic: Neoplasms

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Protecting the Privacy of Cancer Patients Using Fuzzy Association Rule Hiding.

Asian Pacific journal of cancer prevention : APJCP
Objective: Privacy protection in the medical field means the protection of individuals from being associated with undesirable conditions, diagnoses or treatments (Sensitive Attributes). The problem of knowledge discovery from health care data by appl...

THPep: A machine learning-based approach for predicting tumor homing peptides.

Computational biology and chemistry
In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agent...

IILS: Intelligent imaging layout system for automatic imaging report standardization and intra-interdisciplinary clinical workflow optimization.

EBioMedicine
BACKGROUND: To achieve imaging report standardization and improve the quality and efficiency of the intra-interdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiqui...

Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics.

IEEE transactions on nanobioscience
Machine learning is becoming a powerful tool for cancer diagnosis and prognosis based on classification using high dimensional molecular data. However, extracting classification features from high-dimensional datasets remains a challenging problem. P...

Single-cell approaches to cell competition: High-throughput imaging, machine learning and simulations.

Seminars in cancer biology
Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either ...

Cancer taxonomy: pathology beyond pathology.

European journal of cancer (Oxford, England : 1990)
The way we categorise and classify cancer types dictates not only the way we diagnose and treat patients but also many of our decisions on biomarker and drug development. In addition, cancer taxonomy proves the ground truth for future discoveries in ...

NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.

BMC medical genomics
BACKGROUND: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue,...

Artificial intelligence (AI) and cancer prevention: the potential application of AI in cancer control programming needs to be explored in population laboratories such as COMPASS.

Cancer causes & control : CCC
Understanding the risk factors that initiate cancer is essential for reducing the future cancer burden. Much of our current cancer control insight is from cohort studies and newer large-scale population laboratories designed to advance the science ar...

Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders.

IEEE transactions on medical imaging
Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relat...

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.

International journal of molecular sciences
Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNA...