Re-Fraction: A Machine Learning Approach for Deterministic Identification of Protein Homologues and Splice Variants in Large-scale MS-based Proteomics

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.

Download Full-text

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

Download Full-text

Predicting Large-Scale Internet Censorship - A Machine Learning Approach

10.18130/v35399 ◽

2015 ◽

Author(s):

Jin Li

Keyword(s):

Machine Learning ◽

Large Scale ◽

Learning Approach ◽

Machine Learning Approach ◽

Internet Censorship

Download Full-text

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.

Download Full-text

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

Download Full-text

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

10.1101/2020.11.12.380485 ◽

2020 ◽

Author(s):

Jean-Philippe Villemin ◽

Claudio Lorenzi ◽

Andrew Oldfield ◽

Marie-Sarah Cabrillac ◽

William Ritchie ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cell Lines ◽

Cell Invasion ◽

Triple Negative ◽

Splice Variants ◽

Breast Cancer Cell Lines ◽

Learning Approach ◽

Machine Learning Approach ◽

Alternative Splice Variants

ABSTRACTBackgroundBreast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers.ResultsTranscriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype.ConclusionsUsing a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

Download Full-text

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.

Download Full-text