libegl1-nvidia-tesla-440
NVIDIA binary EGL library (non-GLVND variant) (Tesla 440 version)
EGL provides a platform-agnostic mechanism for creating rendering surfaces
for use with other graphics libraries, such as OpenGL|ES.
python3-django-ical
iCalendar feeds for Django (Python 3)
This module provides iCalendar feeds support in Django
applications.
libgcc-s1-mips64r6el-cross
GCC support library (mips64r6el)
Shared version of the support library, a library of internal subroutines
that GCC uses to overcome shortcomings of particular machines, or
special needs for some languages.
r-bioc-degreport
BioConductor report of DEG analysis
Creation of a HTML report of differential expression
analyses of count data. It integrates some of the code
mentioned in DESeq2 and edgeR vignettes, and report a ranked
list of genes according to the fold changes mean and
variability for each selected gene.
libxnnpack0
High-efficiency floating-point neural network inference operators (libs)
XNNPACK is a highly optimized library of floating-point neural network
inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not
intended for direct use by deep learning practitioners and researchers; instead
it provides low-level performance primitives for accelerating high-level
machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch,
and MediaPipe.
r-bioc-fmcsr
GNU R mismatch tolerant maximum common substructure searching
The fmcsR package introduces an efficient maximum common
substructure (MCS) algorithms combined with a novel matching
strategy that allows for atom and/or bond mismatches in the
substructures shared among two small molecules. The resulting
flexible MCSs (FMCSs) are often larger than strict MCSs,
resulting in the identification of more common features in
their source structures, as well as a higher sensitivity in
finding compounds with weak structural similarities. The fmcsR
package provides several utilities to use the FMCS algorithm
for pairwise compound comparisons, structure similarity
searching and clustering.