scholarly journals Morphology, Life Cycle, Environmental Factors and Fitness – a Machine Learning Analysis in Kissing Bugs (Hemiptera, Reduviidae, Triatominae)

2021 ◽  
Vol 9 ◽  
Author(s):  
Jorge E. Rabinovich
Keyword(s):  
Machine Learning ◽  
Life History ◽  
Life Cycle ◽  
Adaptive Dynamics ◽  
Evolutionary Game ◽  
Intrinsic Rate ◽  
Natural Increase ◽  
Learning Approach ◽  
Triatomine Species ◽  
Machine Learning Approach

Populations are permanently evolving and their evolution will influence their survival and reproduction, which will then alter demographic parameters. Several phenotypic, life history and environmental variables are known to be related to fitness measures. The goal of this article was to look into the possible types of those relationships in insects of the subfamily Triatominae, vectors of Trypanosoma cruzi, the causative agent of Chagas disease. After an exhaustive literature review of 7,207 records of publications referring exclusively to all possible features of the triatomines, using 15 keywords those records were reduced to 2,968 publications, that were analyzed individually; after deleting those publications that did not have the data in quantitative form as needed for the objective of this article, I found that 171 papers were adequate for the present analysis. From them I compiled a dataset of 11 variables and 90 cases from 36 triatomine species. Those variables included four environmental, two life cycle, and four morphological variables, and one demographic parameter: a fitness measure (the population intrinsic rate of natural increase, r0), used as dependent variable. However, the relationship between T. cruzi and its vector host was not included in this analysis despite triatomine-T. cruzi interactions constitute an important factor in the evolution of triatomine’s life history. I resorted to the Random Forest method as a machine learning approach for the analysis of this dataset, and found that –in addition to the triatomine species themselves– only the two life cycle variables (mean development time from egg to adult, and mean fecundity, expressed as the average number of female eggs laid per female per day) were statistically significant in determining fitness (r0). The machine learning approach used in the analysis provided a similar but deeper insight into these relationships than classical regression. Except for an analysis on senescence, this is the first study in triatomines addressing these questions. These results will be useful for other theoretical optimization approaches (frequency-dependence, density-dependence, evolutionary game theory, and adaptive dynamics), thus contributing to the theoretical framework for interpreting the succession of stages in insect adaptations, a framework yet to be constructed.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

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.

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