pandas-dev/pandas: Pandas 1.0.3

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

doi:10.5281/zenodo.3715232

Cited by 97 publications

(37 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Standard UniProt IDs are used throughout to ensure maximum compatibilities. Most of the manipulations were done in Python [48] with Jupyter Notebooks [49] using the Pandas library [50], [51] and Numpy [52]. The plots were drawn using Matplotlib [53], Seaborn [54] and the MetBrewer colour palettes [55].…”

Section: Methodsmentioning

confidence: 99%

Pitfalls of machine learning models for protein-protein interactions

Lannelongue

Inouye

2022

Preprint

View full text Add to dashboard Cite

Protein-protein interactions (PPIs) are essential to understanding biological pathways as well as their roles in development and disease. Computational tools have been successful at predicting PPIs in silico, but the lack of consistent and reliable frameworks for this task has led to network models that are difficult to compare and, overall, a low level of trust in the PPI predictions. To better understand the underlying mechanisms that underpin these models, we designed B4PPI, an open-source framework for benchmarking that accounts for a range of biological and statistical pitfalls while facilitating reproducibility. We use B4PPI to shed light on the impact of network topology and how different algorithms deal with highly connected proteins. By studying functional genomics-based and sequence-based models (the two most popular approaches) on human PPIs, we show their complementarity as the former performs best on lone proteins while the latter specialises in interactions involving hubs. We also show that algorithm design has little impact on performance with functional genomic data. We replicate our results between both human and S. Cerevisiae data and demonstrate that models using functional genomics are better suited to PPI prediction across species. With rapidly increasing amounts of sequence and functional genomics data, our study provides a systematic foundation for future construction, comparison and application of PPI networks.

show abstract

Section: Methodsmentioning

confidence: 99%

Pitfalls of machine learning models for protein-protein interactions

Lannelongue

Inouye

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, in order to minimize this effect, the synthetic genome modeling approach was repeated for individual tissue types which were represented by at least 16 high-quality cell lines (as defined by F-measure of greater than or equal to 0.8) using the same parameters described above. All of the analyses were performed using Python version 3.6 using multiple packages including pandas (Reback et al, 2020), NumPy (C. R. Harris et al, 2020), the sklearn.metrics, sklearn.utils, resample modules in SciKits (Buitinck et al, 2013), Matplotlib (Hunter, 2007), seaborn (Waskom et al, 2020), and scipy (SciPy 1.0 Contributors et al, 2020).…”

Section: Synthetic Genome Modeling Approachmentioning

confidence: 99%

Recovering false negatives in CRISPR fitness screens with JLOE

Dede

Hart

2022

Preprint

View full text Add to dashboard Cite

It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a ~20% false negative rate, in addition to library-specific false negatives. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues improves our understanding of core essential genes and suggest only a small number of lineage-specific essential genes, enriched for transcription factors that define pathways of tissue differentiation. To recover false negatives, we introduce a method, Joint Log Odds of Essentiality (JLOE), which builds on our prior work with BAGEL to selectively rescue the false negatives without an increased false discovery rate.

show abstract

“…This work depended principally on the libraries pytorch [44], geotorch [45], numpy [46], scipy [47], pandas [48], ipython [49], matplotlib [50] and plotnine [51].…”

Section: Softwarementioning

confidence: 99%

Scaling Scaling Laws with Board Games

Jones

2021

Preprint

View full text Add to dashboard Cite

The largest experiments in machine learning now require resources far beyond the budget of all but a few institutions. Fortunately, it has recently been shown that the results of these huge experiments can often be extrapolated from the results of a sequence of far smaller, cheaper experiments. In this work, we show that not only can the extrapolation be done based on the size of the model, but on the size of the problem as well. By conducting a sequence of experiments using AlphaZero and Hex, we show that the performance achievable with a fixed amount of compute degrades predictably as the game gets larger and harder. Along with our main result, we further show that the test-time and train-time compute available to an agent can be traded off while maintaining performance.

show abstract

pandas-dev/pandas: Pandas 1.0.3

Cited by 97 publications

References 0 publications

Pitfalls of machine learning models for protein-protein interactions

Pitfalls of machine learning models for protein-protein interactions

Recovering false negatives in CRISPR fitness screens with JLOE

Scaling Scaling Laws with Board Games

Contact Info

Product

Resources

About