A review of feature selection methods for machine learning-based disease risk prediction
Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …
complex datasets. One of the promising applications of machine learning is in precision …
Psychiatric genetics and the structure of psychopathology
For over a century, psychiatric disorders have been defined by expert opinion and clinical
observation. The modern DSM has relied on a consensus of experts to define categorical …
observation. The modern DSM has relied on a consensus of experts to define categorical …
Analysis of polygenic risk score usage and performance in diverse human populations
A historical tendency to use European ancestry samples hinders medical genetics research,
including the use of polygenic scores, which are individual-level metrics of genetic risk. We …
including the use of polygenic scores, which are individual-level metrics of genetic risk. We …
An atlas of genetic scores to predict multi-omic traits
The use of omic modalities to dissect the molecular underpinnings of common diseases and
traits is becoming increasingly common. But multi-omic traits can be genetically predicted …
traits is becoming increasingly common. But multi-omic traits can be genetically predicted …
Machine learning SNP based prediction for precision medicine
In the past decade, precision genomics based medicine has emerged to provide tailored
and effective healthcare for patients depending upon their genetic features. Genome Wide …
and effective healthcare for patients depending upon their genetic features. Genome Wide …
Machine learning for genetic prediction of psychiatric disorders: a systematic review
Abstract Machine learning methods have been employed to make predictions in psychiatry
from genotypes, with the potential to bring improved prediction of outcomes in psychiatric …
from genotypes, with the potential to bring improved prediction of outcomes in psychiatric …
Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods
The analysis of large genomic data is hampered by issues such as a small number of
observations and a large number of predictive variables (commonly known as “large P small …
observations and a large number of predictive variables (commonly known as “large P small …
Computational models for clinical applications in personalized medicine—guidelines and recommendations for data integration and model validation
The future development of personalized medicine depends on a vast exchange of data from
different sources, as well as harmonized integrative analysis of large-scale clinical health …
different sources, as well as harmonized integrative analysis of large-scale clinical health …
Association map** in plants in the post-GWAS genomics era
PK Gupta, PL Kulwal, V Jaiswal - Advances in genetics, 2019 - Elsevier
With the availability of DNA-based molecular markers during early 1980s and that of
sophisticated statistical tools in late 1980s and later, it became possible to identify genomic …
sophisticated statistical tools in late 1980s and later, it became possible to identify genomic …
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge …
Background Improvements to prognostic models in metastatic castration-resistant prostate
cancer have the potential to augment clinical trial design and guide treatment strategies. In …
cancer have the potential to augment clinical trial design and guide treatment strategies. In …