AIMC Topic: Gene Expression

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The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood.

Neuroscience letters Dec 24, 2020
BACKGROUND: Schizophrenia (SCZ) is a highly heritable mental disorder with a substantial disease burden. Machine learning (ML) method can be used to identify individuals with SCZ on the basis of blood gene expression data with high accuracy.
Algorithms Female Adult Male Humans Machine Learning RNA, Messenger Gene Expression Asian People Schizophrenia
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On tower and checkerboard neural network architectures for gene expression inference.

BMC genomics Dec 16, 2020
BACKGROUND: One possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ∼1,000 landmark genes and uses a computational method to infer the expression of another ∼10,000 ...
Neural Networks, Computer Algorithms Gene Expression Profiling Gene Regulatory Networks Gene Expression
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Identifying longevity associated genes by integrating gene expression and curated annotations.

PLoS computational biology Nov 30, 2020
Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are...
Genes, Fungal Gene Ontology Caenorhabditis elegans Longevity Reproducibility of Results Machine Learning Animals Algorithms Gene Expression Saccharomyces cerevisiae
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A pitfall for machine learning methods aiming to predict across cell types.

Genome biology Nov 19, 2020
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to p...
Humans Machine Learning Gene Expression Genomics Chromatin Epigenomics
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A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models.

Journal of advanced research Nov 11, 2020
INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients a...
Models, Theoretical Ovarian Neoplasms Proto-Oncogene Proteins c-akt Humans Female TOR Serine-Threonine Kinases Gene Expression Machine Learning Survival Rate Phosphatidylinositol 3-Kinases Carcinoma, Ovarian Epithelial Algorithms Risk Assessment Prognosis Gene Expression Regulation, Neoplastic
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MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

EBioMedicine Oct 8, 2020
BACKGROUND: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC).
Gene Expression Receptor, ErbB-2 Adult Breast Neoplasms Middle Aged ROC Curve Imaging, Three-Dimensional Image Processing, Computer-Assisted Neoadjuvant Therapy Magnetic Resonance Imaging Biomarkers Female Aged Young Adult Humans Machine Learning
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Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement.

Annals of the rheumatic diseases Oct 7, 2020
OBJECTIVE: We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).
Female Adult Male Middle Aged Humans Machine Learning Antigens, CD34 Collagen Actins Clinical Trials as Topic Severity of Illness Index Fibroblasts Gene Expression Skin Forearm Scleroderma, Diffuse
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Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

American journal of human genetics Sep 9, 2020
Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes ris...
Humans Phenotype Female Exome Sequencing Young Adult Mutation, Missense Unsupervised Machine Learning Principal Component Analysis Gene Expression Alleles Adult Registries Diabetes Mellitus, Type 2 Male Datasets as Topic Genetic Predisposition to Disease Adolescent United Kingdom Cluster Analysis Child Norway Hepatocyte Nuclear Factor 1-alpha
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Using autoencoders as a weight initialization method on deep neural networks for disease detection.

BMC medical informatics and decision making Aug 20, 2020
BACKGROUND: As of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detecti...
Deep Learning Early Detection of Cancer Diagnostic Imaging Gene Expression Humans Neural Networks, Computer Machine Learning Neoplasms
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A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Scientific reports Jul 16, 2020
Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have i...
Preoperative Care Chromosomes, Human, Pair 10 Chromosomes, Human, Pair 7 X-linked Nuclear Protein Biomarkers, Tumor Brain Neoplasms Male Mutation Artificial Intelligence Magnetic Resonance Imaging Humans Cyclin-Dependent Kinase Inhibitor p16 Aneuploidy Retrospective Studies Decision Trees Image Interpretation, Computer-Assisted Female Glioblastoma Isocitrate Dehydrogenase Sensitivity and Specificity Gene Expression
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