AIMC Topic: Neoplasms

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Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

Briefings in bioinformatics
The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that qua...

Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.

Briefings in bioinformatics
Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evalua...

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network.

Briefings in bioinformatics
It is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistical or machine learning approaches have been developed to detect somatic small variants. However, the...

Identification of pan-cancer Ras pathway activation with deep learning.

Briefings in bioinformatics
The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several...

Prediction of driver variants in the cancer genome via machine learning methodologies.

Briefings in bioinformatics
Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, an...

Prognostic outcome prediction by semi-supervised least squares classification.

Briefings in bioinformatics
Although great progress has been made in prognostic outcome prediction, small sample size remains a challenge in obtaining accurate and robust classifiers. We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a join...

Automated model versus treating physician for predicting survival time of patients with metastatic cancer.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few pred...

Natural Language Processing for Patient Selection in Phase I or II Oncology Clinical Trials.

JCO clinical cancer informatics
PURPOSE: Early discontinuation affects more than one third of patients enrolled in early-phase oncology clinical trials. Early discontinuation is deleterious both for the patient and for the study, by inflating its duration and associated costs. We a...

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches.

Briefings in bioinformatics
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tum...