AIMC Topic: Databases, Genetic

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Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

American journal of human genetics
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is th...

Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning.

IEEE transactions on neural networks and learning systems
As a group of complex neurodevelopmental disorders, autism spectrum disorder (ASD) has been reported to have a high overall prevalence, showing an unprecedented spurt since 2000. Due to the unclear pathomechanism of ASD, it is challenging to diagnose...

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.

Genome medicine
BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly...

Gene Ontology representation for transcription factor functions.

Biochimica et biophysica acta. Gene regulatory mechanisms
Transcription plays a central role in defining the identity and functionalities of cells, as well as in their responses to changes in the cellular environment. The Gene Ontology (GO) provides a rigorously defined set of concepts that describe the fun...

KEGG mapping tools for uncovering hidden features in biological data.

Protein science : a publication of the Protein Society
In contrast to artificial intelligence and machine learning approaches, KEGG (https://www.kegg.jp) has relied on human intelligence to develop "models" of biological systems, especially in the form of KEGG pathway maps that are manually created by ca...

Using machine learning and big data to explore the drug resistance landscape in HIV.

PLoS computational biology
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive...

Identification of biomarkers for acute leukemia via machine learning-based stemness index.

Gene
Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transgenic or knock-in methods. However, there are some potential pitfalls in using this method, such as r...

Machine learning-based identification and characterization of 15 novel pathogenic SUOX missense mutations.

Molecular genetics and metabolism
Isolated sulfite oxidase deficiency (ISOD) is a rare hereditary metabolic disease caused by absence of functional sulfite oxidase (SO) due to mutations of the SUOX gene. SO oxidizes toxic sulfite and sulfite accumulation is associated with neurologic...

Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets.

IEEE/ACM transactions on computational biology and bioinformatics
Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to...