scholarly journals The Relevance of Cataract as a Risk Factor for Age-Related Macular Degeneration: A Machine Learning Approach

2019 ◽  
Vol 9 (24) ◽  
pp. 5550
Author(s):  
Antonieta Martínez-Velasco ◽  
Lourdes Martínez-Villaseñor ◽  
Luis Miralles-Pechuán ◽  
Andric C. Perez-Ortiz ◽  
Juan C. Zenteno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Macular Degeneration ◽  
Significant Risk ◽  
Age Related Macular Degeneration ◽  
Supervised Machine Learning ◽  
Current Debate ◽  
Learning Approach ◽  
Age Related ◽  
Machine Learning Approach

Age-related macular degeneration (AMD) is the leading cause of visual dysfunction and irreversible blindness in developed countries and a rising cause in underdeveloped countries. There is a current debate on whether or not cataracts are significant risk factors for AMD development. In particular, research regarding this association is so far inconclusive. For this reason, we aimed to employ here a machine-learning approach to analyze the relevance and importance of cataracts as a risk factor for AMD in a large cohort of Hispanics from Mexico. We conducted a nested case control study of 119 cataract cases and 137 healthy unmatched controls focusing on clinical data from electronic medical records. Additionally, we studied two single nucleotide polymorphisms in the CFH gene previously associated with the disease in various populations as positive control for our method. We next determined the most relevant variables and found the bivariate association between cataracts and AMD. Later, we used supervised machine-learning methods to replicate these findings without bias. To improve the interpretability, we detected the five most relevant features and displayed them using a bar graph and a rule-based tree. Our findings suggest that bilateral cataracts are not a significant risk factor for AMD development among Hispanics from Mexico.

scholarly journals Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

2021 ◽  
Author(s):  
Manik Kuchroo ◽  
Marcello DiStasio ◽  
Eda Calapkulu ◽  
Maryam Ige ◽  
Le Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Alzheimer’S Disease ◽  
Macular Degeneration ◽  
Single Cell ◽  
Topological Analysis ◽  
Age Related Macular Degeneration ◽  
Learning Approach ◽  
Age Related ◽  
Machine Learning Approach

1One Sentence SummaryA novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimer’s disease and multiple sclerosis.2AbstractNeurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimer’s disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called ‘diffusion condensation.’ By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

scholarly journals Interactions between Apolipoprotein E Metabolism and Retinal Inflammation in Age-Related Macular Degeneration

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 635
Author(s):  
Monica L. Hu ◽  
Joel Quinn ◽  
Kanmin Xue
Keyword(s):  
Risk Factor ◽  
Apolipoprotein E ◽  
Macular Degeneration ◽  
Amyloid Beta ◽  
Complement System ◽  
Genetic Risk ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Retinal Inflammation ◽  
The Complement System

Age-related macular degeneration (AMD) is a multifactorial retinal disorder that is a major global cause of severe visual impairment. The development of an effective therapy to treat geographic atrophy, the predominant form of AMD, remains elusive due to the incomplete understanding of its pathogenesis. Central to AMD diagnosis and pathology are the hallmark lipid and proteinaceous deposits, drusen and reticular pseudodrusen, that accumulate in the subretinal pigment epithelium and subretinal spaces, respectively. Age-related changes and environmental stressors, such as smoking and a high-fat diet, are believed to interact with the many genetic risk variants that have been identified in several major biochemical pathways, including lipoprotein metabolism and the complement system. The APOE gene, encoding apolipoprotein E (APOE), is a major genetic risk factor for AMD, with the APOE2 allele conferring increased risk and APOE4 conferring reduced risk, in comparison to the wildtype APOE3. Paradoxically, APOE4 is the main genetic risk factor in Alzheimer's disease, a disease with features of neuroinflammation and amyloid-beta deposition in common with AMD. The potential interactions of APOE with the complement system and amyloid-beta are discussed here to shed light on their roles in AMD pathogenesis, including in drusen biogenesis, immune cell activation and recruitment, and retinal inflammation.

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