AIMC Topic: Databases, Genetic

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Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology.

Circulation. Genomic and precision medicine
BACKGROUND: A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene On...

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery.

Nucleic acids research
Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited...

Gramene 2018: unifying comparative genomics and pathway resources for plant research.

Nucleic acids research
Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,...

miRandola 2017: a curated knowledge base of non-invasive biomarkers.

Nucleic acids research
miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows ...

Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have propose...

Deep Integrative Analysis for Survival Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Survival prediction is very important in medical treatment. However, recent leading research is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample sizes are relatively small. To solve the above challenges, we d...

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the...

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important...

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders.

American journal of human genetics
In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background nois...