CamDavidsonPilon/lifelines: v0.24.8

Davidson-Pilon, Cameron; Kalderstam, Jonas; Jacobson, Noah Tobias; Zivich, Paul N; Kuhn, Ben; Williamson, Mike; sean-reed,; AbdealiJK,; Fioré-Gartland, Andrew; Datta, Deepyaman; Moneda, Luis; Gabriel, Joseph M.; WIlson, Daniel; Parij, Alex; Moncada‐Torres, Arturo; Stark, Kyle; Anton, Steven R.; Peña, Miguel Sancho; Besson, Lilian; Singaravelan, Karthikeyan; Jona,; Gadgil, Harsh; Golland, Dave; Hussey, Sean; Kumar, Ravin; Begun, Matthew; Noorbakhsh, Javad; Klintberg, Andreas; Rendeiro, André F.; Flaxman, Abraham D.

doi:10.5281/zenodo.3833188

Cited by 3 publications

(1 citation statement)

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“…Survival analyses and volcano plots were generated using lifelines Python library [24] and ggplot2 R package, respectively. The p-values or FDR<0.05 were considered significant.…”

Section: Discussionmentioning

confidence: 99%

Discovery of biomarker candidates associated with the risk of short-term and mid/long-term relapse after infliximab withdrawal in Crohn’s patients: a proteomics-based study

Pierre

Baiwir

Huynh-Thu

et al. 2020

Gut

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ObjectiveA subset of Crohn’s disease (CD) patients experiences mid/long-term remission after infliximab withdrawal. Biomarkers are needed to identify those patients.DesignNew biomarkers of relapse were searched in the baseline serum of CD patients stopping infliximab when they were under combined therapy (antimetabolite and infliximab) and stable clinical remission (diSconTinuation in CrOhn’s disease patients in stable Remission on combined therapy with Immunosuppressors cohort, n=102). From shotgun proteomics experiment (discovery step), biomarker candidates were identified and further targeted by selected reaction monitoring (verification step). The dataset was stratified to search for markers of short-term (<6 months) or mid/long-term relapse (>6 months). The risk of relapse and the predicting capacity associated with biomarker candidates were evaluated using univariate Cox model and log-rank statistic, respectively. To test their complementary predicting capacity, biomarker candidates were systematically combined in pairs.ResultsDistinct biomarker candidates were associated with the risk (HR) of short-term (15 proteins, 2.9<HR<16.1, p<0.05) and mid/long-term (17 proteins, 2.1<HR<4.7, p<0.05) relapse, they reflect different pathophysiological processes. In stratified and non-stratified datasets, novel marker combinations exhibited a high predicting capacity as shown by their higher Z-scores (false discovery rate <0.001) than C reactive protein and faecal calprotectin (current references in predicting relapse).ConclusionWe identified for the first time circulating biomarker candidates associated with the risk of mid/long-term relapse in CD patients stopping infliximab. We also highlight a sequence of pathophysiological processes leading to relapse, this could help to better understand the disease progression. Our findings may pave the way for a better non-invasive evaluation of the risk of relapse when contemplating antitumour necrosis factor α withdrawal in CD patients.

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“…Survival analyses and volcano plots were generated using lifelines Python library [24] and ggplot2 R package, respectively. The p-values or FDR<0.05 were considered significant.…”

Section: Discussionmentioning

confidence: 99%

Discovery of biomarker candidates associated with the risk of short-term and mid/long-term relapse after infliximab withdrawal in Crohn’s patients: a proteomics-based study

Pierre

Baiwir

Huynh-Thu

et al. 2020

Gut

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A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

Peták

Kamal

Dirner³

et al. 2021

npj Precis. Onc.

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Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

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Collapsar R-process Yields Can Reproduce [Eu/Fe] Abundance Scatter in Metal-poor Stars

Brauer

Drout

et al. 2021

ApJ

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It is unclear if neutron star mergers can explain the observed r-process abundances of metal-poor stars. Collapsars, defined here as rotating massive stars whose collapse results in a rapidly accreting disk around a black hole that can launch jets, are a promising alternative. We find that we can produce a self-consistent model in which a population of collapsars with stochastic europium yields synthesizes all of the r-process material in metal-poor ([Fe/ H] < − 2.5) stars. Our model reproduces the observed scatter and evolution of scatter of [Eu/Fe] abundances. We find that if collapsars are the dominant r-process site for metal-poor stars, r-process synthesis may be linked to supernovae that produce long γ-ray bursts. Our results also allow for the possibility that core-collapse supernovae beyond those that launch γ-ray bursts also produce r-process material (e.g., potentially a subset of Type Ic-BL supernovae). Furthermore, we identify collapsar jet properties (isotropic energy, engine luminosity, or engine time) that may trace r-process yield and verify that the amount of r-process yield produced per collapsar in our model ( ∼ 0.07M e ) is consistent with other independent estimates. In the future, achieving 0.05 dex precision on distribution scatter or a reliable selection function would further constrain our probe of r-process production. Our model would also hold for another prompt r-process site with a power-law yield, and work is needed to determine if, for example, fast-merging neutron stars can also explain abundance scatter.Unified Astronomy Thesaurus concepts: Core-collapse supernovae (304); Stellar jets (1607); R-process (1324); Nucleosynthesis (1131); Stellar abundances (1577)

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CamDavidsonPilon/lifelines: v0.24.8

Cited by 3 publications

References 0 publications

Discovery of biomarker candidates associated with the risk of short-term and mid/long-term relapse after infliximab withdrawal in Crohn’s patients: a proteomics-based study

Discovery of biomarker candidates associated with the risk of short-term and mid/long-term relapse after infliximab withdrawal in Crohn’s patients: a proteomics-based study

A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

Collapsar R-process Yields Can Reproduce [Eu/Fe] Abundance Scatter in Metal-poor Stars

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