AIMC Topic: Mutation

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Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.

Biomolecules Aug 28, 2020
Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second...
Adenocarcinoma Diagnosis, Differential Mutation Humans Neural Networks, Computer Carcinoma, Squamous Cell Reproducibility of Results Diagnosis, Computer-Assisted Lung Neoplasms
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A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.

BMC cancer Aug 28, 2020
BACKGROUND: Cell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy...
Machine Learning Aged Aged, 80 and over Adult Humans Sequence Analysis, DNA Prostatic Neoplasms Mutation Whole Genome Sequencing Biomarkers, Tumor Male Middle Aged Cohort Studies Base Sequence Genome, Human Circulating Tumor DNA
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Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.

International journal of molecular sciences Aug 14, 2020
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well...
ROC Curve Prostatic Neoplasms Neural Networks, Computer Mutation Transcription, Genetic Receptors, Androgen Humans Male Androgen Receptor Antagonists Deep Learning Cell Line, Tumor
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A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation.

Biochimica et biophysica acta. Molecular basis of disease Aug 7, 2020
Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United States. Heterogeneity and metastasis of cancer brings great difficulties to the follow-up diagnosis and ...
Neoplasms, Unknown Primary Sequence Analysis, DNA DNA Mutation Machine Learning Positron-Emission Tomography Algorithms
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Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.

Genes Aug 4, 2020
With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy o...
Humans Neural Networks, Computer Software Genomics Female DNA Methylation Breast Neoplasms DNA Copy Number Variations Transcriptome Mutation Gene Regulatory Networks
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

Nature cancer Jul 27, 2020
We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approac...
Hematoxylin Prognosis Humans Neoplasms Deep Learning Mutation
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Pan-cancer image-based detection of clinically actionable genetic alterations.

Nature cancer Jul 27, 2020
Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learnin...
Mutation Eosine Yellowish-(YS) Humans Hematoxylin Neoplasms Deep Learning
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Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

British journal of cancer Jul 21, 2020
BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the current study, we aimed to predict the genetic information of pancreatic tumours in a simple, inexpensive, and non-invasive manner, using cancer imaging...
Aged, 80 and over Immunohistochemistry Pancreatic Neoplasms Humans Male Retrospective Studies Mutation Middle Aged Machine Learning Tomography, X-Ray Computed Imaging Genomics Carcinoma, Pancreatic Ductal B7-H1 Antigen Prognosis Female Tumor Suppressor Protein p53 Aged
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Cross-species regulatory sequence activity prediction.

PLoS computational biology Jul 20, 2020
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, mod...
Epigenomics Models, Genetic Databases, Genetic Sequence Analysis, DNA Models, Statistical Mutation Hepatocytes Genetic Variation Animals Machine Learning Mice Software Gene Expression Regulation Humans Genomics Algorithms Genome, Human Neural Networks, Computer Computational Biology Species Specificity Quantitative Trait Loci
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A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Scientific reports Jul 16, 2020
Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have i...
Chromosomes, Human, Pair 7 X-linked Nuclear Protein Image Interpretation, Computer-Assisted Isocitrate Dehydrogenase Preoperative Care Chromosomes, Human, Pair 10 Gene Expression Mutation Aneuploidy Sensitivity and Specificity Humans Magnetic Resonance Imaging Biomarkers, Tumor Artificial Intelligence Brain Neoplasms Cyclin-Dependent Kinase Inhibitor p16 Retrospective Studies Male Decision Trees Female Glioblastoma
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