AIMC Topic: Neoplasms

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Ensemble-Based Somatic Mutation Calling in Cancer Genomes.

Methods in molecular biology (Clifton, N.J.)
Identification of somatic mutations in tumor tissue is challenged by both technical artifacts, diverse somatic mutational processes, and genetic heterogeneity in the tumors. Indeed, recent independent benchmark studies have revealed low concordance b...

Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides.

Current protein & peptide science
Peptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anti...

Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured...

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing.

Clinical chemistry
BACKGROUND: Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories r...

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Cell
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipelin...

A Deep Learning Framework for Predicting Response to Therapy in Cancer.

Cell reports
A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and asse...

Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images.

Journal of biomedical optics
We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in...

Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models.

Nanoscale
Nano-systems for cancer co-therapy including vitamins or vitamin derivatives have showed adequate results to continue with further research studies to better understand them. However, the number of different combinations of drugs, vitamins, nanoparti...