AIMC Topic: Neoplasms

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Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis.

Diagnostic cytopathology
BACKGROUND: The role of retrospective analysis has been evolved greatly in cancer research. We undertook this meta-analysis to evaluate the diagnostic value of Neural networks (NNs) in Fine needle aspiration cytological (FNAC) image of cancer.

Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network.

Oncogene
Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mutagenesis forces. Decoding the mutational processes by examining their unique patterns has successfu...

How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab.

Journal of the American Society for Mass Spectrometry
As the field of mass spectrometry imaging continues to grow, so too do its needs for optimal methods of data analysis. One general need in image analysis is the ability to classify the underlying regions within an image, as healthy or diseased, for e...

Matrix factorization with neural network for predicting circRNA-RBP interactions.

BMC bioinformatics
BACKGROUND: Circular RNA (circRNA) has been extensively identified in cells and tissues, and plays crucial roles in human diseases and biological processes. circRNA could act as dynamic scaffolding molecules that modulate protein-protein interactions...

The emerging roles of artificial intelligence in cancer drug development and precision therapy.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Artificial intelligence (AI) has strong logical reasoning ability and independent learning ability, which can simulate the thinking process of the human brain. AI technologies such as machine learning can profoundly optimize the existing mode of anti...

Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.

BMC bioinformatics
BACKGROUND: The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purpose of understanding the mech...

Neural networks for open and closed Literature-based Discovery.

PloS one
Literature-based Discovery (LBD) aims to discover new knowledge automatically from large collections of literature. Scientific literature is growing at an exponential rate, making it difficult for researchers to stay current in their discipline and e...

Assessing reproducibility and veracity across machine learning techniques in biomedicine: A case study using TCGA data.

International journal of medical informatics
BACKGROUND: Many studies that aim to identify gene biomarkers using statistical methods and translate them into FDA-approved drugs have faced challenges due to lack of clinical validity and methodological reproducibility. Since genomic data analysis ...

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review).

International journal of oncology
The new era of artificial intelligence (AI) has introduced revolutionary data‑driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision‑support systems. These advances...