scholarly journals A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions

2019 ◽  
Vol 5 (7) ◽  
pp. 1199-1210 ◽  
Author(s):  
Adrian Jinich ◽  
Benjamin Sanchez-Lengeling ◽  
Haniu Ren ◽  
Rebecca Harman ◽  
Alán Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Quantum Chemistry ◽  
Redox Reactions ◽  
Large Scale ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Redox Potentials ◽  
Machine Learning Approach

scholarly journals A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315,000 redox reactions

2018 ◽  
Author(s):  
Adrian Jinich ◽  
Benjamin Sanchez-Lengeling ◽  
Haniu Ren ◽  
Rebecca Harman ◽  
Alán Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Quantum Chemistry ◽  
Redox Reactions ◽  
Large Scale ◽  
Computational Cost ◽  
Accurate Method ◽  
Redox Potentials ◽  
Quantum Chemistry Calculations ◽  
Quantitative Understanding ◽  
Semi Empirical

AbstractA quantitative understanding of the thermodynamics of biochemical reactions is essential for accurately modeling metabolism. The group contribution method (GCM) is one of the most widely used approaches to estimating standard Gibbs energies and redox potentials of reactions for which no experimental measurements exist. Previous work has shown that quantum chemical predictions of biochemical thermodynamics are a promising approach to overcome the limitations of GCM. However, the quantum chemistry approach is significantly more expensive. Here we use a combination of quantum chemistry and machine learning to obtain a fast and accurate method for predicting the thermodynamics of biochemical redox reactions. We focus on predicting the redox potentials of carbonyl functional group reductions to alcohols and amines, two of the most ubiquitous carbon redox transformations in biology. Our method relies on semi-empirical quantum chemistry calculations calibrated with Gaussian Process (GP) regression against available experimental data. Our approach results in higher predictive power than the GCM at a low computational cost. We design and implement a network expansion algorithm that iteratively reduces and oxidizes a set of natural seed metabolites, and demonstrate the high-throughput applicability of our method by predicting the standard potentials of more than 315,000 redox reactions involving approximately 70,000 compounds. Additionally, we developed a novel fingerprint-based framework for detecting molecular environment motifs that are enriched or depleted across different regions of the redox potential landscape. We provide open access to all source code and data generated.

scholarly journals Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

2019 ◽  
Author(s):  
Anton Levitan ◽  
Andrew N. Gale ◽  
Emma K. Dallon ◽  
Darby W. Kozan ◽  
Kyle W. Cunningham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Yeast Species ◽  
Transposon Mutagenesis ◽  
Learning Approach ◽  
Long Distance ◽  
Gene Essentiality ◽  
Genome Wide ◽  
Machine Learning Approach

ABSTRACTIn vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

2018 ◽  
Vol 45 (5) ◽  
pp. 2243-2251 ◽  
Author(s):  
Baozhou Sun ◽  
Dao Lam ◽  
Deshan Yang ◽  
Kevin Grantham ◽  
Tiezhi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

DFT-Machine Learning Approach for Accurate Prediction of pKa

2021 ◽  
Author(s):  
Robin Lawler ◽  
Yao-Hao Liu ◽  
Nessa Majaya ◽  
Omar Allam ◽  
Hyunchul Ju ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

2015 ◽  
Vol 11 (5) ◽  
pp. 2087-2096 ◽  
Author(s):  
Raghunathan Ramakrishnan ◽  
Pavlo O. Dral ◽  
Matthias Rupp ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Quantum Chemistry ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A machine learning approach to predict ethnicity using personal name and census location in Canada

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241239
Author(s):  
Kai On Wong ◽  
Osmar R. Zaïane ◽  
Faith G. Davis ◽  
Yutaka Yasui
Keyword(s):  
Machine Learning ◽  
First Nations ◽  
Predictive Value ◽  
Large Scale ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach

Background Canada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and census location features. Methods Using census 1901, the multiclass and binary class classification machine learning pipelines were developed. The 13 ethnic categories examined were Aboriginal (First Nations, Métis, Inuit, and all-combined)), Chinese, English, French, Irish, Italian, Japanese, Russian, Scottish, and others. Machine learning algorithms included regularized logistic regression, C-support vector, and naïve Bayes classifiers. Name features consisted of the entire name string, substrings, double-metaphones, and various name-entity patterns, while location features consisted of the entire location string and substrings of province, district, and subdistrict. Predictive performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1, Area Under the Curve for Receiver Operating Characteristic curve, and accuracy. Results The census had 4,812,958 unique individuals. For multiclass classification, the highest performance achieved was 76% F1 and 91% accuracy. For binary classifications for Chinese, French, Italian, Japanese, Russian, and others, the F1 ranged 68–95% (median 87%). The lower performance for English, Irish, and Scottish (F1 ranged 63–67%) was likely due to their shared cultural and linguistic heritage. Adding census location features to the name-based models strongly improved the prediction in Aboriginal classification (F1 increased from 50% to 84%). Conclusions The automated machine learning approach using only name and census location features can predict the ethnicity of Canadians with varying performance by specific ethnic categories.

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