AI Medical Compendium Topic:
Mutation

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ARIADNA: machine learning method for ancient DNA variant discovery.

DNA research : an international journal for rapid publication of reports on genes and genomes
Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and desp...

A systematic exploration of [Formula: see text] cutoff ranges in machine learning models for protein mutation stability prediction.

Journal of bioinformatics and computational biology
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies ar...

A machine learning approach for somatic mutation discovery.

Science translational medicine
Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning t...

Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributi...

Enhancing fluorescent protein photostability through robot-assisted photobleaching.

Integrative biology : quantitative biosciences from nano to macro
Improving fluorescent proteins through the use of directed evolution requires robust techniques for screening large libraries of genetic variants. Here we describe an effective and relatively low-cost system for screening libraries of fluorescent pro...

Classifying tumors by supervised network propagation.

Bioinformatics (Oxford, England)
MOTIVATION: Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion ...

Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

Bioinformatics (Oxford, England)
MOTIVATION: As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergenc...

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...

Numerical Models and In Vitro Assays to Study Odorant Receptors.

Methods in molecular biology (Clifton, N.J.)
Unraveling the sense of smell relies on understanding how odorant receptors recognize odorant molecules. Given the vastness of the odorant chemical space and the complexity of the odorant receptor space, computational methods are in line to propose r...

Pycnodysostosis with novel gene mutation and sporadic medullary thyroid carcinoma: A case report.

Medicine
RATIONALE: Pycnodysostosis is a rare autosomal recessive skeletal dysplasia caused by a mutation in the cathepsin K encoded by cathepsin K gene (CTSK). Medullary thyroid carcinoma (MTC) is also a relatively rare type of primary thyroid carcinoma.