AIMC Topic: Neoplasms

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Selecting precise reference normal tissue samples for cancer research using a deep learning approach.

BMC medical genomics
BACKGROUND: Normal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resource...

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

BMC medical genomics
BACKGROUND: Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features.

A survey of neural network-based cancer prediction models from microarray data.

Artificial intelligence in medicine
Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identi...

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

Human genetics
In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intellig...

A novel gene selection algorithm for cancer classification using microarray datasets.

BMC medical genomics
BACKGROUND: Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to t...

Computational Prediction of Human Disease- Associated circRNAs Based on Manifold Regularization Learning Framework.

IEEE journal of biomedical and health informatics
The accumulating evidences regarding circular RNAs (circRNAs) indicate that they play crucial roles in a wide range of biological processes and participate in tumorigenesis and progression. The number of newly discovered circRNAs have increased drama...

Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy.

Pharmaceutical research
PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the i...

Clinical intelligence: New machine learning techniques for predicting clinical drug response.

Computers in biology and medicine
Predicting the response, or sensitivity, of a clinical drug to a specific cancer type is an important research problem. By predicting the clinical drug response correctly, clinicians are able to understand patient-to-patient differences in drug sensi...

State-of-the-art review on deep learning in medical imaging.

Frontiers in bioscience (Landmark edition)
Deep learning (DL) is affecting each and every sphere of public and private lives and becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the activities of neurons in the neocortex of human brain where the thought...

Discovering functional impacts of miRNAs in cancers using a causal deep learning model.

BMC medical genomics
BACKGROUND: Micro-RNAs (miRNAs) play a significant role in regulating gene expression under physiological and pathological conditions such as cancers. However, it remains a challenging problem to discover the target messenger RNAs (mRNAs) of a miRNA ...