AIMC Topic: Genetic Markers
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Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples.
Computational and mathematical methods in medicine
Feb 23, 2021
Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of PE screening has attracted much attention. The purpose of this study is to screen placental mRNA to o...
A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications.
Scientific reports
Feb 8, 2021
Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene s...
A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles.
Journal of translational medicine
Nov 30, 2020
BACKGROUND: Myopathies are a heterogenous collection of disorders characterized by dysfunction of skeletal muscle. In practice, myopathies are frequently encountered by physicians and precise diagnosis remains a challenge in primary care. Molecular e...
Identification of early liver toxicity gene biomarkers using comparative supervised machine learning.
Scientific reports
Nov 5, 2020
Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive mo...
How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data.
International journal of molecular sciences
Oct 12, 2020
The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F population were genotype...
Impact of Gene Biomarker Discovery Tools Based on Protein-Protein Interaction and Machine Learning on Performance of Artificial Intelligence Models in Predicting Clinical Stages of Breast Cancer.
Interdisciplinary sciences, computational life sciences
Sep 10, 2020
Breast cancer, as one of the most common diseases threatening the women's life, has attracted serious attention of the clinical and biomedical researchers worldwide. The genome-based studies along with their registered GEO datasets are frequent in th...
Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome.
Frontiers in immunology
Mar 31, 2020
Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are mul...
Identification of Methylated Gene Biomarkers in Patients with Alzheimer's Disease Based on Machine Learning.
BioMed research international
Mar 26, 2020
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder and characterized by the cognitive impairments. It is essential to identify potential gene biomarkers for AD pathology.
Cyclin-Dependent Kinase Inhibitor p57
Protein Tyrosine Phosphatases
Peptide Hydrolases
Mitochondrial Proteins
Microfilament Proteins
Genetic Markers
Alzheimer Disease
Patients
Transcription Factors
DNA Methylation
Carrier Proteins
DNA-Binding Proteins
Humans
Machine Learning
Gene Expression Regulation, Neoplastic
RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.
Scientific reports
Feb 5, 2020
Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine le...
Global transcriptome analysis reveals relevant effects at environmental concentrations of cypermethrin in honey bees (Apis mellifera).
Environmental pollution (Barking, Essex : 1987)
Dec 4, 2019
Cypermethrin is a frequently used insecticide in agriculture and households but its chronic and molecular effects are poorly known are . Here we describe effects of sublethal cypermethrin exposure on the global transcriptome in the brain of honey bee...
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