Kyoungyeul Lee
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View article: Critical assessment of missense variant effect predictors on disease-relevant variant data
Critical assessment of missense variant effect predictors on disease-relevant variant data Open
View article: Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers
Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers Open
Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interp…
View article: Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers
Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers Open
View article: Critical assessment of missense variant effect predictors on disease-relevant variant data
Critical assessment of missense variant effect predictors on disease-relevant variant data Open
Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here,…
View article: A variant prioritization tool leveraging multiple instance learning for rare Mendelian disease genomic testing
A variant prioritization tool leveraging multiple instance learning for rare Mendelian disease genomic testing Open
Background Genomic testing such as exome sequencing and genome sequencing is being widely utilized for diagnosing rare Mendelian disorders. Because of a large number of variants identified by these tests, interpreting the final list of var…
View article: Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders
Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders Open
View article: Disease-causing variant recommendation system for clinical genome interpretation with adjusted scores for artefactual variants
Disease-causing variant recommendation system for clinical genome interpretation with adjusted scores for artefactual variants Open
Background In the process of finding the causative variant of rare diseases (RD), accurate assessment and prioritization of genetic variants is essential. Although quality control (QC) of genetic variants is strictly performed, the presenc…
View article: Case Report: Infantile Cerebellar-Retinal Degeneration With Compound Heterozygous Variants in ACO2 Gene—Long-Term Follow-Up of a Sibling
Case Report: Infantile Cerebellar-Retinal Degeneration With Compound Heterozygous Variants in ACO2 Gene—Long-Term Follow-Up of a Sibling Open
Infantile cerebellar-retinal degeneration (ICRD) is an extremely rare, infantile-onset neuro-degenerative disease, characterized by autosomal recessive inherited, global developmental delay (GDD), progressive cerebellar and cortical atroph…
View article: 3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints
3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints Open
Dataset needed to train and evaluate 3Cnet. See github repository for source code. https://github.com/KyoungYeulLee/3Cnet
View article: 3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints
3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints Open
Dataset needed to train and evaluate 3Cnet. See github repository for source code. https://github.com/KyoungYeulLee/3Cnet
View article: 3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints
3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints Open
Motivation Improvements in next-generation sequencing have enabled genome-based diagnosis for patients with genetic diseases. However, accurate interpretation of human variants requires knowledge from a number of clinical cases. In additio…
View article: 3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks
3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks Open
Thanks to the improvement of Next Generation Sequencing (NGS), genome-based diagnosis for rare disease patients become possible. However, accurate interpretation of human variants requires massive amount of knowledge gathered from previous…
View article: 3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks
3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks Open
DATA UPDATE NOTICE: please see https://zenodo.org/record/6016720 Dataset needed to train and evaluate 3Cnet. See github repository for source code. https://github.com/KyoungYeulLee/3Cnet
View article: 3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks
3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks Open
DATA UPDATE NOTICE: please see https://zenodo.org/record/6016720 Dataset needed to train and evaluate 3Cnet. See github repository for source code. https://github.com/KyoungYeulLee/3Cnet
View article: Hypomorphic Mutations in TONSL Cause SPONASTRIME Dysplasia
Hypomorphic Mutations in TONSL Cause SPONASTRIME Dysplasia Open
View article: Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server
Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server Open
The target models that we built can be used for both predicting the activity of ligands toward each target and ranking candidate targets for a query ligand using a unified scoring scheme. The scores are additionally fitted to the probabili…
View article: Additional file 1: of Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server
Additional file 1: of Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server Open
MySQL codes for bioactivity extraction from ChEMBL database. Variable “molregno” from table “compound_structures” is identification code for ligands while variable “tid” from table “target_dictionary” is identification code for targets. (T…