AI Medical Compendium Topic

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Genome, Human

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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...

Predicting geographic location from genetic variation with deep neural networks.

eLife
Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of kn...

Acute myeloid leukemia and artificial intelligence, algorithms and new scores.

Best practice & research. Clinical haematology
Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex datasets. Advances in computer hardware and software have led to breakthroughs in multiple sectors of ou...

Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening.

SLAS discovery : advancing life sciences R & D
There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for bu...

Opening the Black Box: Interpretable Machine Learning for Geneticists.

Trends in genetics : TIG
Because of its ability to find complex patterns in high dimensional and heterogeneous data, machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available. While the complexity of ML ...

Mb-level CpG and TFBS islands visualized by AI and their roles in the nuclear organization of the human genome.

Genes & genetic systems
Unsupervised machine learning that can discover novel knowledge from big sequence data without prior knowledge or particular models is highly desirable for current genome study. We previously established a batch-learning self-organizing map (BLSOM) f...

Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments.

BioMed research international
BACKGROUND: Next-generation sequencing enables massively parallel processing, allowing lower cost than the other sequencing technologies. In the subsequent analysis with the NGS data, one of the major concerns is the reliability of variant calls. Alt...

PANDA: Prioritization of autism-genes using network-based deep-learning approach.

Genetic epidemiology
Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time-consuming and expensive experiments given a large number of ...

Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network analysis, to develop a diagnostic gene signature for the diagnosis of ventilator-associated pneumonia ...

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

Nature communications
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Can...