AIMC Topic: Genomics

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The Path to and Impact of Disease Recognition with AI.

IEEE pulse
The Process of rare disease identification by clinical geneticists is closely associated with the ability to correlate the phenotype of a patient with the relevant genetic syndromes. In order to perform this correlation, the phenotype has to be descr...

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.

Methods in molecular biology (Clifton, N.J.)
A standard strategy to discover somatic mutations in a cancer genome is to use next-generation sequencing (NGS) technologies to sequence the tumor tissue and its matched normal (commonly blood or adjacent normal tissue) for side-by-side comparison. H...

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...

AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but ...

PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The integration of multi-modal data, such as histopathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions in cancer study. His...

Development of Genome-Derived Tumor Type Prediction to Inform Clinical Cancer Care.

JAMA oncology
IMPORTANCE: Diagnosing the site of origin for cancer is a pillar of disease classification that has directed clinical care for more than a century. Even in an era of precision oncologic practice, in which treatment is increasingly informed by the pre...

Machine learning approaches to study glioblastoma: A review of the last decade of applications.

Cancer reports (Hoboken, N.J.)
BACKGROUND: Glioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re-incidence and therapy resistance. Thus, it is urge...

Applications of artificial intelligence in neuro-oncology.

Current opinion in neurology
PURPOSE OF REVIEW: To discuss recent applications of artificial intelligence within the field of neuro-oncology and highlight emerging challenges in integrating artificial intelligence within clinical practice.