Code & Data
Adeft (Acromine based Disambiguation of Entities From Text context) is a utility for building models to disambiguate acronyms and other abbreviations of biological terms in the scientific literature. It makes use of an implementation of the Acromine algorithm developed by the NaCTeM at the University of Manchester to identify possible longform expansions for shortforms in a text corpus. It allows users to build disambiguation models to disambiguate shortforms based on their text context. A growing number of pretrained disambiguation models are publicly available to download through adeft.
Adeft: Acromine-based Disambiguation of Entities from Text with applications to the biomedical literature.
Steppi A, Gyori BM, Bachman JA.
Journal of Open Source Software. 2020. 5(45): 1708. doi: 10.21105/joss.01708.
ProteinNet is a standardized data set for machine learning of protein structure. It provides protein sequences, structures (secondary and tertiary), multiple sequence alignments (MSAs), position-specific scoring matrices (PSSMs), and standardized training / validation / test splits. ProteinNet builds on the biennial CASP assessments, which carry out blind predictions of recently solved but publicly unavailable protein structures, to provide test sets that push the frontiers of computational methodology. It is organized as a series of data sets, spanning CASP 7 through 12 (covering a ten-year period), to provide a range of data set sizes that enable assessment of new methods in relatively data poor and data rich regimes.
ProteinNet: a standardized data set for machine learning of protein structure.
BMC Bioinformatics. 2019. 20(1):311. doi: 10.1186/s12859-019-2932-0. PMID: 31185886.
The Growth Rate inhibition (GR) Calculator is an open source set of Python, R and on-line tools for quantifying the responses of cancer cells to drugs in a manner that corrects for the confounding effects of variable cell proliferation rates. Response metrics computed from GR data include GR50 and GRmax and are direct analogues of familiar IC50 and Emax response measures.
Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs.
Hafner M, Niepel M, Chung M, Sorger PK.
Nat Methods. 2016. 13(6):521-7. doi: 10.1038/nmeth.3853.PMID: 27135972.
Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics.
Hafner M, Niepel M, Sorger PK.
Nat Biotechnol. 2017. 35(6):500-502. doi: 10.1038/nbt.3882. PMID: 28591115.
ASHLAR (Alignment by Simultaneous Harmonization of Layer/Adjacency Registration) is an open source Python package that stiches together successive microscopy image tiles to generate a single, seamless image. ASHLAR also registers images from different fluorescent channels at a high level of accuracy.
Reliable high-throughput imaging of cells grown in multi-plate wells is complicated by loss of cells during staining and wash steps. The dye drop method uses a set of incrementally more dense solutions to prevent cell loss. Dye Drop software consists of Python tools for determining the viability and cell cycle states of cells before and after drug treatment.
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