pandas-dev/pandas: Pandas 1.1.2

Reback, Jeff; McKinney, Wes; jbrockmendel,; Bossche, Joris Van den; Augspurger, Tom; Cloud, Phillip; gfyoung,; Sinhrks,; Hawkins, Simon; Klein, Adam; Roeschke, Matthew; Tratner, Jeff; She, Chang; Petersen, Terji; Ayd, William; MomIsBestFriend,; Garcia, Marc; Schendel, Jeremy; Hayden, Andy; Jancauskas, Vytautas; Saxton, Daniel; McMaster, Ali; Battiston, Pietro; Seabold, Skipper; chris-b,; h-vetinari,; Hoyer, Stephan; Dong, Kaiqi; Overmeire, Wouter; Winkel, Martin

doi:10.5281/zenodo.4019559

Cited by 11 publications

(5 citation statements)

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“…R [68] and R packages tidyverse, ggpubr, ggdendro, seqinr, Biostrings, msa, reshape2, UpSetR, cluster, ade4, RColorBrewer, readxl, dplyr, and ggdendro were used for analysis or visualization [69][70][71][72][73][74][75][76][77][78][79][80]. Python 3.6.4, pandas [81], and CobraPy 0.14.1 [55], as well as code to implement Diamond-based annotation scoring from CarveMe [52], were used for the reconstruction and modeling. Memote [62] was used to evaluate all reconstructions.…”

Section: Author Summary Additional Informationmentioning

confidence: 99%

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

et al. 2022

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Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

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Section: Author Summary Additional Informationmentioning

confidence: 99%

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

et al. 2022

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“…All analyses were performed using the Python programming language [version 3.9.12; ( 22 )]. The open-source Python libraries Pandas [version 1.4.2; ( 23 )], NumPy [version 1.22.3; ( 24 , 25 )], Scikitlearn [version 1.0.2; ( 26 )], Matplotlib [version 3.5.1; ( 27 )], and SciPy [version 1.8.0; ( 28 )] were also used. Student’s t -tests were performed with SciPy’s ttest_ind function.…”

Section: Methodsmentioning

confidence: 99%

Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview

Wright-Berryman

Cohen²,

Haq³

et al. 2023

Front. Psychiatry

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BackgroundCurrent depression, anxiety, and suicide screening techniques rely on retrospective patient reported symptoms to standardized scales. A qualitative approach to screening combined with the innovation of natural language processing (NLP) and machine learning (ML) methods have shown promise to enhance person-centeredness while detecting depression, anxiety, and suicide risk from in-the-moment patient language derived from an open-ended brief interview.ObjectiveTo evaluate the performance of NLP/ML models to identify depression, anxiety, and suicide risk from a single 5–10-min semi-structured interview with a large, national sample.MethodTwo thousand four hundred sixteen interviews were conducted with 1,433 participants over a teleconference platform, with 861 (35.6%), 863 (35.7%), and 838 (34.7%) sessions screening positive for depression, anxiety, and suicide risk, respectively. Participants completed an interview over a teleconference platform to collect language about the participants’ feelings and emotional state. Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB) models were trained for each condition using term frequency-inverse document frequency features from the participants’ language. Models were primarily evaluated with the area under the receiver operating characteristic curve (AUC).ResultsThe best discriminative ability was found when identifying depression with an SVM model (AUC = 0.77; 95% CI = 0.75–0.79), followed by anxiety with an LR model (AUC = 0.74; 95% CI = 0.72–0.76), and an SVM for suicide risk (AUC = 0.70; 95% CI = 0.68–0.72). Model performance was generally best with more severe depression, anxiety, or suicide risk. Performance improved when individuals with lifetime but no suicide risk in the past 3 months were considered controls.ConclusionIt is feasible to use a virtual platform to simultaneously screen for depression, anxiety, and suicide risk using a 5-to-10-min interview. The NLP/ML models performed with good discrimination in the identification of depression, anxiety, and suicide risk. Although the utility of suicide risk classification in clinical settings is still undetermined and suicide risk classification had the lowest performance, the result taken together with the qualitative responses from the interview can better inform clinical decision-making by providing additional drivers associated with suicide risk.

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“…KO annotation results were aggregated and analyzed using KEGG-Decoder v1.2.2 which assesses percent completeness of metabolic pathways defined by KEGG ( Graham et al, 2018 ). A heatmap of the results was produced using a custom python v3.7.8 ( McKinney, 2010 ; Reback et al, 2020 ; Waskom, 2021 ) script available at https://github.com/zscooper/marinobacter_pangenomics .…”

Section: Methodsmentioning

confidence: 99%

“…HGTector2 output was combined with NCBI COG ( Galperin et al, 2019 ) annotations for each gene call assigned during pangenome assembly. Genes that were not annotated with COGs but scored as putative horizontally transferred genes were counted as “None.” The output data were analyzed and visualized using custom python v3.7.8 ( McKinney, 2010 ; Reback et al, 2020 ; Waskom, 2021 ) scripts available at https://github.com/zscooper/marinobacter_pangenomics .…”

Section: Methodsmentioning

confidence: 99%

Evolutionary Divergence of Marinobacter Strains in Cryopeg Brines as Revealed by Pangenomics

et al. 2022

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Marinobacter spp. are cosmopolitan in saline environments, displaying a diverse set of metabolisms that allow them to competitively occupy these environments, some of which can be extreme in both salinity and temperature. Here, we introduce a distinct cluster of Marinobacter genomes, composed of novel isolates and in silico assembled genomes obtained from subzero, hypersaline cryopeg brines, relic seawater-derived liquid habitats within permafrost sampled near Utqiaġvik, Alaska. Using these new genomes and 45 representative publicly available genomes of Marinobacter spp. from other settings, we assembled a pangenome to examine how the new extremophile members fit evolutionarily and ecologically, based on genetic potential and environmental source. This first genus-wide genomic analysis revealed that Marinobacter spp. in general encode metabolic pathways that are thermodynamically favored at low temperature, cover a broad range of organic compounds, and optimize protein usage, e.g., the Entner–Doudoroff pathway, the glyoxylate shunt, and amino acid metabolism. The new isolates contributed to a distinct clade of subzero brine-dwelling Marinobacter spp. that diverged genotypically and phylogenetically from all other Marinobacter members. The subzero brine clade displays genomic characteristics that may explain competitive adaptations to the extreme environments they inhabit, including more abundant membrane transport systems (e.g., for organic substrates, compatible solutes, and ions) and stress-induced transcriptional regulatory mechanisms (e.g., for cold and salt stress) than in the other Marinobacter clades. We also identified more abundant signatures of potential horizontal transfer of genes involved in transcription, the mobilome, and a variety of metabolite exchange systems, which led to considering the importance of this evolutionary mechanism in an extreme environment where adaptation via vertical evolution is physiologically rate limited. Assessing these new extremophile genomes in a pangenomic context has provided a unique view into the ecological and evolutionary history of the genus Marinobacter, particularly with regard to its remarkable diversity and its opportunism in extremely cold and saline environments.

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pandas-dev/pandas: Pandas 1.1.2

Cited by 11 publications

References 0 publications

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview

Evolutionary Divergence of Marinobacter Strains in Cryopeg Brines as Revealed by Pangenomics

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