Predicting Large-Scale Internet Censorship - A 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.

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Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample

Biological Psychiatry Cognitive Neuroscience and Neuroimaging ◽

10.1016/j.bpsc.2019.05.018 ◽

2020 ◽

Vol 5 (8) ◽

pp. 799-807 ◽

Cited By ~ 7

Author(s):

Emily A. Boeke ◽

Avram J. Holmes ◽

Elizabeth A. Phelps

Keyword(s):

Machine Learning ◽

Large Scale ◽

Learning Approach ◽

Machine Learning Approach

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

PLoS ONE ◽

10.1371/journal.pone.0241239 ◽

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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Improving tropospheric corrections on large-scale Sentinel-1 interferograms using a machine learning approach for integration with GNSS-derived zenith total delay (ZTD)

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111608 ◽

2020 ◽

Vol 239 ◽

pp. 111608 ◽

Cited By ~ 1

Author(s):

Roghayeh Shamshiri ◽

Mahdi Motagh ◽

Hossein Nahavandchi ◽

Mahmud Haghshenas Haghighi ◽

Mostafa Hoseini

Keyword(s):

Machine Learning ◽

Large Scale ◽

Learning Approach ◽

Total Delay ◽

Zenith Total Delay ◽

Machine Learning Approach ◽

Tropospheric Corrections

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An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods

Nutrients ◽

10.3390/nu13093195 ◽

2021 ◽

Vol 13 (9) ◽

pp. 3195

Author(s):

Tazman Davies ◽

Jimmy Chun Yu Louie ◽

Tailane Scapin ◽

Simone Pettigrew ◽

Jason HY Wu ◽

...

Keyword(s):

Machine Learning ◽

Dietary Fiber ◽

Large Scale ◽

Learning Algorithm ◽

Fiber Content ◽

Learning Approach ◽

K Nearest Neighbors ◽

Machine Learning Approach ◽

Packaged Foods ◽

Prediction Approach

Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making it challenging for consumers and policy makers to monitor fiber consumption. Here, we developed a machine learning approach for automated and systematic prediction of fiber content using nutrient information commonly available on packaged products. An Australian packaged food dataset with known fiber content information was divided into training (n = 8986) and test datasets (n = 2455). Utilization of a k-nearest neighbors machine learning algorithm explained a greater proportion of variance in fiber content than an existing manual fiber prediction approach (R2 = 0.84 vs. R2 = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale.

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