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Genomics

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The ability to classify patients based on gene-expression data varies by algorithm and performance metric.

PLoS computational biology
By classifying patients into subgroups, clinicians can provide more effective care than using a uniform approach for all patients. Such subgroups might include patients with a particular disease subtype, patients with a good (or poor) prognosis, or p...

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress.

International journal of molecular sciences
Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the acc...

TADA-a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs.

Genome biology
Few methods have been developed to investigate copy number variants (CNVs) based on their predicted pathogenicity. We introduce TADA, a method to prioritise pathogenic CNVs through assisted manual filtering and automated classification, based on an e...

FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.

STAR protocols
Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes ...

A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI).

Computers in biology and medicine
BACKGROUND: Although copy number variations (CNVs) are infrequent, each anomaly is unique, and multiple CNVs can appear simultaneously. Growing evidence suggests that CNVs contribute to a wide range of diseases. When CNVs are detected, assessment of ...

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Artificial intelligence in medicine
Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-mod...

Impact of Clinical and Genomic Factors on COVID-19 Disease Severity.

AMIA ... Annual Symposium proceedings. AMIA Symposium
To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on ...

From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.

Human genomics
Identification of genomic signals as indicators for functional genomic elements is one of the areas that received early and widespread application of machine learning methods. With time, the methods applied grew in variety and generally exhibited a t...