DrugCell | A visible neural network model for drug response prediction | Genomics library
kandi X-RAY | DrugCell Summary
kandi X-RAY | DrugCell Summary
DrugCell is an interpretable neural network-based model that predicts cell response to a wide range of drugs. Unlike fully-connected neural networks, connectivity of neurons in the DrugCell mirrors a biological hierarchy (e.g. Gene Ontology), so that the information travels only between subsystems (or pathways) with known hierarchical relationship during the model training. This feature of the framework allows for identification of subsystems in the hierarchy that are important to the model's prediction, warranting further investigation on underlying biological mechanisms of cell response to treatments. The current version (v1.0) of the DrugCell model is trained using 509,294 (cell line, drug) pairs across 1,235 tumor cell lines and 684 drugs. The training data is retrieved from Genomics of Drug Sensitivity in Cancer database (GDSC) and the Cancer Therapeutics Response Portal (CTRP) v2. DrugCell characterizes each cell line using its genotype; the feature vector for each cell is a binary vector representing mutational status of the top 15% most frequently mutated genes (n = 3,008) in cancer. Drugs are encoded using Morgan Fingerprint (radius = 2), and the resulting feature vectors are binary vectors of length 2,048.
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Top functions reviewed by kandi - BETA
- Train the neural network
- Creates a word mask for each term
- Build a vector from cell features
- Calculate Pearson correlation coefficient
- Load ontology from file
- Performs prediction on a single cell
- Load a mapping from a file
- Load training data
- Load mapping from file
- Prepare data for prediction
DrugCell Key Features
DrugCell Examples and Code Snippets
Lines of Code : 15License : Permissive (MIT)
Copypython -u train_drugcell.py -onto drugcell_ont.txt
-gene2id gene2ind.txt
-cell2id cell2ind.txt
-drug2id drug2ind.txt
-genotype cell2muta Community Discussions
Trending Discussions on Genomics
QUESTION
I´m working with two text files that look like this: File 1
...ANSWER
Answered 2022-Apr-09 at 00:49Perhaps you are after this?
QUESTION
I'm using the software plink2 (https://www.cog-genomics.org/plink/2.0/) and I'm trying to iterate over 3 variables.
This software admits an input file with .ped extention file and an exclude file with .txt extention which contains a list of names to be excluded from the input file.
The idea is to iterate over the input files and then over exclude files to generate single outputfiles.
- Input files: Highland.ped - Midland.ped - Lowland.ped
- Exclude-map files: HighlandMidland.txt - HighlandLowland.txt - MidlandLowland.txt
- Output files: HighlandMidland - HighlandLowland - MidlandHighland - MidlandLowland - LowlandHighland - LowlandMidland
The general code is:
...ANSWER
Answered 2021-Dec-09 at 23:50Honestly, I think your current code is quite clear; but if you really want to write this as a loop, here's one possibility:
QUESTION
From this example string:
...ANSWER
Answered 2021-Dec-09 at 01:11use regexp_extract(col, r"&q;Stockcode&q;:([^/$]*?),&q;.*")
if applied to sample data in your question - output is
QUESTION
I am making a code which takes in jumble word and returns a unjumbled word , the data.json contains a list and here take a word one-by-one and check if it contains all the characters of the word and later checking if the length is same , but the problem is when i enter a word as helol then the l is checked twice and giving me some other outputs including the main one(hello). i know why does it happen but i cant get a fix to it
...ANSWER
Answered 2021-Nov-25 at 18:33As I understand it you are trying to identify all possible matches for the jumbled string in your list. You could sort the letters in the jumbled word and match the resulting list against sorted lists of the words in your data file.
QUESTION
I am trying to use plink1.9 to split multiallelic into biallelic. The input is that
...ANSWER
Answered 2021-Nov-17 at 09:45I used bcftools to complete the task.
QUESTION
I have a FASTA file that has about 300000 sequences but some of the sequences are like these
...ANSWER
Answered 2021-Oct-12 at 20:28You can match your non-X containing FASTA entries with the regex >.+\n[^X]+\n. This checks for a substring starting with > having a first line of anything (the FASTA header), which is followed by characters not containing an X until you reach a line break.
For example:
QUESTION
For example, I have two strings:
...ANSWER
Answered 2021-Oct-04 at 22:27For your example your pattern would be:
QUESTION
I am currently trying to run genomic analyses pipelines using Hail(library for genomics analyses written in python and Scala). Recently, Apache Spark 3 was released and it supported GPU usage.
I tried spark-rapids library start an on-premise slurm cluster with gpu nodes. I was able to initialise the cluster. However, when I tried running hail tasks, the executors keep getting killed.
On querying in Hail forum, I got the response that
That’s a GPU code generator for Spark-SQL, and Hail doesn’t use any Spark-SQL interfaces, only the RDD interfaces.
So, does Spark3 not support GPU usage for RDD interfaces?
...ANSWER
Answered 2021-Sep-23 at 05:53As of now, spark-rapids doesn't support GPU usage for RDD interfaces.
Source: Link
Apache Spark 3.0+ lets users provide a plugin that can replace the backend for SQL and DataFrame operations. This requires no API changes from the user. The plugin will replace SQL operations it supports with GPU accelerated versions. If an operation is not supported it will fall back to using the Spark CPU version. Note that the plugin cannot accelerate operations that manipulate RDDs directly.
Here, an answer from spark-rapids team
Source: Link
We do not support running the RDD API on GPUs at this time. We only support the SQL/Dataframe API, and even then only a subset of the operators. This is because we are translating individual Catalyst operators into GPU enabled equivalent operators. I would love to be able to support the RDD API, but that would require us to be able to take arbitrary java, scala, and python code and run it on the GPU. We are investigating ways to try to accomplish some of this, but right now it is very difficult to do. That is especially true for libraries like Hail, which use python as an API, but the data analysis is done in C/C++.
QUESTION
I have 1500 files with the same format (the .scount file format from PLINK2 https://www.cog-genomics.org/plink/2.0/formats#scount), an example is below:
...ANSWER
Answered 2021-Sep-07 at 11:10a tidyverse solution
QUESTION
I have been implementing a suite of RecordBatchReaders for a genomics toolset. The standard unit of work is a RecordBatch. I ended up implementing a lot of my own compression and IO tools instead of using the existing utilities in the arrow cpp platform because I was confused about them. Are there any clear examples of using the existing compression and file IO utilities to simply get a file stream that inflates standard zlib data? Also, an object diagram for the cpp platform would be helpful in ramping up.
...ANSWER
Answered 2021-Jun-02 at 18:58Here is an example program that inflates a compressed zlib file and reads it as CSV.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DrugCell
Hardware required for training a new model GPU server with CUDA>=10 installed
Software Python 2.7 or >=3.6 Anaconda Relevant information for installing Anaconda can be found in: https://docs.conda.io/projects/conda/en/latest/user-guide/install/. PyTorch >=0.4 Depending on the specification of your machine, run appropriate command to install PyTorch. The installation command line can be found in https://pytorch.org/. Specify Conda as your default package. Example 1: if you are working with a CPU machine running on MAC OS X, execute the following command line: conda install pytorch torchvision -c pytorch Example 2: for a LINUX machine without GPUs, run the following command line: conda install pytorch torchvision cpuonly -c pytorch Example 3: for a LINUX-based GPU server with CUDA version 10.1, run the following command line: conda install pytorch torchvision cudatoolkit=10.1 -c pytorch networkx numpy
Set up a virtual environment If you are testing the pre-trained model using a CPU machine, run the following command line to set up an appropriate virtual environment (pytorch3drugcellcpu) using the .yml files in environment_setup. MAC OS X conda env create -f environment_cpu_mac.yml LINUX conda env create -f environment_cpu_linux.yml If you are training a new model or test the pre-trained model using a GPU server, run the following command line to set up a virtual environment (pytorch3drugcell). conda env create -f environment.yml After setting up the conda virtual environment, make sure to activate environment before executing DrugCell scripts. When testing in sample directory, no need to run this as the example bash scripts already have the command line. source activate pytorch3drugcell (or pytorch3drugcellcpu)
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