sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
30d9a39f5fc4f9f66cad29f4e535c9266aaa2d5bd76a3dc965af398b5032cac6 | Shell | 7,929 | 196 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
. ./cmd.sh || exit 1;
. ./path.sh || exit 1;
# basic settings
stage=-1 # stage to start
stop_stage=100 # stage to stop
verbose=1 # verbosity level (lower is less info)
n_gpus=0 # number of gpus in traini... |
dac081ba542b7122f1aa34babaef556c67f2063baef78d59533e4376ce9104ce | Shell | 7,999 | 199 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
. ./cmd.sh || exit 1;
. ./path.sh || exit 1;
# basic settings
stage=-1 # stage to start
stop_stage=100 # stage to stop
verbose=1 # verbosity level (lower is less info)
n_gpus=8 # number of gpus in traini... |
e06707f89506942ab082f29ca0535493cfaa8731de9c16c6c2c035fad0eeec2f | Shell | 8,003 | 199 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
. ./cmd.sh || exit 1;
. ./path.sh || exit 1;
# basic settings
stage=-1 # stage to start
stop_stage=100 # stage to stop
verbose=1 # verbosity level (lower is less info)
n_gpus=1 # number of gpus in traini... |
034a1abd61ddacf9eef24eeed304ca73be657b3ffd11f2635a7dcdd536afb804 | Shell | 8,179 | 205 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
. ./cmd.sh || exit 1;
. ./path.sh || exit 1;
# basic settings
stage=1 # stage to start
stop_stage=100 # stage to stop
verbose=1 # verbosity level (lower is less info)
n_gpus=1 # number of gpus in traini... |
936e6273a3f06afe037657bca093db712099b42a8c3785c7f70a5ad3748e29d9 | Shell | 8,718 | 256 | #!/bin/sh
set -e
# sh nrndiagnose.sh [python]
# Diagnostic tests to verify launch functionality of a NEURON
# installation. Problems that occur are generally traceable to
# environment issues that can be fixed with proper
# PATH, PYTHONPATH, PYTHONHOME, and/or NRN_PYLIB
# filled in by configure
prefix="/raid/zyc/cod... |
da78d9737cc9eda5760f5607f3cb61506a115e60ea214301f7661b6bb5a3b1a2 | Shell | 8,871 | 390 | #!/bin/bash
#
# This file is a part of LEMON, a generic C++ optimization library.
#
# Copyright (C) 2003-2009
# Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
# (Egervary Research Group on Combinatorial Optimization, EGRES).
#
# Permission to use, modify and distribute this software is granted
# provided tha... |
fd82161db17c3a16d17949e529505d470099f341771a478099ce6d07e04eeeac | Shell | 8,888 | 212 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
. ./cmd.sh || exit 1;
. ./path.sh || exit 1;
# basic settings
stage=0 # stage to start
stop_stage=100 # stage to stop
verbose=1 # verbosity level (lower is less info)
n_gpus=1 # number of gpus in traini... |
a35ebd07cfa49c888626c6dcacc31a34bab596059cde94c23bae3fb85baf9340 | Shell | 9,358 | 269 | #!/usr/bin/env bash
# svaba_explore — one-shot launcher for the bps explorer (+ optional r2c.db).
#
# Spins up a tiny localhost HTTP server in a temp directory containing
# symlinks to:
# * docs/bps_explorer.html (the viewer)
# * the bps.txt[.gz] you passed
# * the r2c.db you passed (OPTIONAL)
# …then opens the ... |
40f45b7a32291357f24317aeef53dc076e066abcbd46e4e4ec4ae10a4e4a8163 | Shell | 9,471 | 73 | #!/bin/sh
#i=80
#while [ "$i" -le 200 ]; do
# python save_syn.py --dataroot /research/cbim/medical/medical-share/public/MMWHS/FSL --name fsl_4ch_exp1_modality_nature --input_nc 3 --output_nc 2 --model pix2pix --netG resnet_9blocks --direction AtoB --dataset_mode fsl1db --modality ct --epoch $i --gpu_ids 1 --result... |
bacde4a74d36e66dd63fddc4adc7edf8910ffe0511cc01225b2bb0e9321406bf | Shell | 9,493 | 137 | #!/bin/bash
# The purpose of this script is to generate (static and temporal) functional connectivity matrices on a pre-defined Human Connectome Project (HCP) subject, using the HCP wb_command
# i.e., https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ
## PREREQUISITES:
# Before running the script you ... |
6e205301f025c4de0372065b4b427eb25f3b9886719fbb1efaf053fac3de1975 | Shell | 9,948 | 244 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
. ./cmd.sh || exit 1;
. ./path.sh || exit 1;
# basic settings
stage=2 # stage to start
stop_stage=100 # stage to stop
verbose=1 # verbosity level (lower is less info)
n_gpus=1 # number of gpus in traini... |
ebb56be08115515bae9c16babbb35e4d900fa60d5feb7875cf51a279848be42f | Shell | 9,961 | 256 | #!/usr/bin/env bash
#
# mosdepth_lowmapq_blacklist.sh — flag regions dominated by multi-mappers.
#
# Given paired mosdepth regions.bed.gz outputs, one unfiltered by MAPQ and
# one MAPQ-filtered, emit a BED of genomic windows where the fraction of
# reads passing the MAPQ filter is low enough that the locus is effective... |
b9b0a666dd1f7812bcc090eaca5a311ebac0dc38e61fdfbcabea223fab0f137d | Shell | 11,042 | 316 | #!/bin/bash
#--------------------------------------------------------
usage()
{
cat << EOF
This script uses vagrant to start a virtual machine and
builds percolator with converters packages by executing
a builder file.
usage: $0
[[-h]] [[-a]]
[[-b branch]]|[[-s source_direc... |
ec3217d75d41ec1b5192065e05a36897642361b0df8f13ff846103093071b5e2 | Shell | 11,096 | 288 | #!/bin/tcsh -xef
#The user may specify the following individual details, to perform preprocessing of multiple run per subject
#All details change depending on the folder path to follow
set monkey = (m16) #ID of the monkey
set name = (After) #Name of monkey
set session = (s30) #ID of the session
set run = (r1 r2 r3) #... |
8297d6fb55157f81e15b35e0e9e40063165b559406a2a070f067fa6452d34cc6 | Shell | 11,544 | 298 | #!/bin/bash
############################################################################################################################
# This is just my standard QC pipeline for genetic data. This pipeline has to be readapted depending on the aim of the study.
#####################################################... |
f90e48ed5765ad30953113bc130ba8e659d990b9b8d53f585fe858223cceef35 | Shell | 12,059 | 323 | #!/bin/tcsh -xef
#the user may specify a single subject to run with
set monkey = (m17)
set session = (s26)
set run = (r2)
set condition = (cnd2)
set tr_counts = (113)
set bp_l = (.1)
set bp_h = (.01)
set blur = (1) #spatial blurring (or smoothing factor)
set n_cpu = (8) #number of processors used for parallel process... |
4eef3345c7c2e48740b5a842bf8ab31e2d30a6bec74c1a8f8443e011736976de | Shell | 12,174 | 237 | #!/usr/bin/env bash
#
# run_svaba_on_panel.sh -- run svaba (tumor/normal) on every simulated tumor BAM
# in a panel folder built by build_sim_panel.sh, then postprocess (+ optional
# benchmark vs the panel's ground truth). Mirrors the user's preferred invocation:
#
# time svaba run -t <sim_tumor> -n <matched_normal> ... |
904231c006db6e2ed10bd30317b6333ea05645a04e545a947dcb2be1a37ed7af | Shell | 12,385 | 188 | #!/bin/bash
# The purpose of this script is to generate the structural connectivity matrix of a given Human Connectome Project (HCP) subject, using two brain parcellations:
# the Schaefer 400 parcellation (400 cortical regions) and the HCP multi-modal (aka Glasser) parcellation (360 cortical regions).
# Ths script wil... |
f81e211867c65e36b46c31caa5d4faaa98f2245084b50de575fce9cffd5c2a9f | Shell | 12,564 | 309 | #!/usr/bin/env bash
#
# combine_blacklists.sh — combine multiple BED blacklists into one file.
#
# "Combining" blacklists means a UNION over intervals: a region ends up in the
# output if ANY of the input files covered it. This is what you almost always
# want when you're assembling a single --blacklist argument for sv... |
add801dca13673b54666f881e50598834a79170a4ab42beef356232eb327ade1 | Shell | 12,580 | 97 | set -ex
#python train.py --dataroot ./datasets/edges2handbags --name edges2handbags --model pix2pix --netG resnet_9blocks --direction AtoB --lambda_L1 100 --dataset_mode aligned --norm batch --pool_size 0
#python train.py --dataroot ./datasets/stroke_ct --name stroke_ct --model pix2pix --netG resnet_9blocks --direction... |
9afd42ca5c5a760e86a84174dbb7352489a5820cfb79c47abd6f5e47e5f0f7c4 | Shell | 12,589 | 295 | #!/bin/bash
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
. ./cmd.sh || exit 1;
. ./path.sh || exit 1;
# basic settings
stage=-1 # stage to start
stop_stage=100 # stage to stop
verbose=1 # verbosity level (lower is less info)
n_gpus=1 # number of gpus in traini... |
3d52540bec9cf0c1e3b1a7c8651749d970467b9f7f382d418c63601cd4b37ac6 | Shell | 12,948 | 238 | #!/usr/bin/env bash
#
# build_sim_panel.sh -- build a reproducible, self-contained simulated tumor panel:
# * many SVs on chr22 (default) -> diploid donor genome + ground truth
# * ART -> BWA-MEM simulated tumor reads
# * in-silico tumor IMPURITY by admixing a REAL whole-genome normal
# (downsampled), at seve... |
b3c53bc86e7dc9fef93b1c12c4c3eb28808ce6953f7f36fb22fed094382767c7 | Shell | 13,485 | 399 | #!/usr/bin/env bash
cmd="$@"
no_feats=false
no_wav=false
no_text=false
no_spk_sort=false
non_print=false
function show_help
{
echo "Usage: $0 [--no-feats] [--no-text] [--non-print] [--no-wav] [--no-spk-sort] <data-dir>"
echo "The --no-xxx options mean that the script does not require "
echo "xxx.s... |
8b5ae3fbae3aca1c7563e96be35059de55f47d510e7e8a6edbf49f32a18f2bc5 | Shell | 14,528 | 372 | #!/bin/bash
set -e -E -u -o pipefail
# defaults
CONDA_ENV="test-env"
IN_UBUNTU_BASE_CONTAINER=${IN_UBUNTU_BASE_CONTAINER:-"false"}
METHOD=${METHOD:-""}
PRODUCES_ARTIFACTS=${PRODUCES_ARTIFACTS:-"false"}
SANITIZERS=${SANITIZERS:-""}
ARCH=$(uname -m)
LGB_VER=$(head -n 1 "${BUILD_DIRECTORY}/VERSION.txt")
# create the ... |
971eddf54ac5065c465fa65b7bb840e912cf151b225dd02b981404a00f3bcfd4 | Shell | 15,818 | 122 | set -ex
#python test.py --dataroot ./datasets/stroke_ct --name stroke_ct --model pix2pix --netG resnet_9blocks --direction AtoB --dataset_mode aligned --norm batch
#python train.py --dataroot ./datasets/stroke_ct --name stroke_ct --model pix2pix --netG resnet_9blocks --direction AtoB --lambda_L1 100 --dataset_mode ali... |
f21e7088662b7f25769bd78cbe00145d8e671cc66a72f5207bfb62dfd7b90a83 | Shell | 16,203 | 62 | # ICON-LM
bs=200
seed=1 && stamp="20231005-094726" && echo "seed=$seed, stamp=$stamp" &&
CUDA_VISIBLE_DEVICES=1 python3 analysis.py --backend jax --model 'icon_lm' --test_config_filename 'test_lm_precise_config.json' --model_config_filename 'model_lm_config.json' --analysis_dir /home/shared/icon/analysis/icon_lm_learn_... |
d50c018cb21d353b90fb3cdb9b3c680f8812f2f37b50b73fa4325cd467e8b23b | Shell | 17,243 | 447 | # svaba_local_function.sh — sourceable bash helpers for common svaba tasks.
#
# Designed to be dot-sourced into an interactive shell (bash/zsh), NOT
# executed as a script. Usage:
#
# # in your ~/.bashrc or ~/.zshrc (or run once per shell session):
# source ~/git/svaba/scripts/svaba_local_function.sh
#
# # then u... |
fd10644acbcc55534067136926f5b1e23be6fab26ae8dbe926e216d678f9df0b | Shell | 18,538 | 476 | #!/usr/bin/env bash
#
# svaba_cloud.sh — scatter svaba across GCP VMs, one partition per VM.
#
# Takes the svaba run command you'd normally run locally and distributes
# it across N worker VMs, each handling a chromosome partition. The
# script manages -k (regions) and -a (analysis ID) per worker — you
# provide everyt... |
15f73cc72ce326b0ab1b1cec4186707d78d446cc63f6f9eb5f9ae5cbecbab98e | Shell | 21,287 | 486 | #Starting from the maps obtained after the regression (one map per condition, per run, per monkey)
#Create a single average map, to be subsequently used in the ANOVA
###############
#Average M9 per condition
#Vocal
#3dcalc \
-a m9_s51_r1_cnd1_to_MBM_SparseContinuousVocal.nii.gz \
-b m9_s51_r2_cnd2_to_MBM_SparseContinu... |
b2a106e779d4c643c892631cd41584d1133bbe95bbbb9c86d3b2059376016521 | Shell | 25,917 | 616 | #!/usr/bin/env bash
#
# svaba_postprocess.sh — one-stop post-processing of a svaba run.
#
# Replaces (and subsumes) the older sort_output.sh and
# sort_and_deduplicate_bps.sh. Given a svaba analysis ID, this script:
#
# 1. Merges per-thread output BAMs into one BAM per suffix.
# ${ID}.thread*.${suffix}.bam -> ... |
a294036bcfbc122257c401411f30c3b3d80017f7e4abbf3307c7b1d1a7c2d06d | Stan | 499 | 28 | data {
int<lower=0> N;
int<lower=0> P;
matrix[N,P] x;
int<lower=0> ys[N];
int<lower=0> ns[N];
real<lower=0> conc;
real<lower=0> concShape;
real<lower=0> concRate;
}
parameters {
vector[P] beta;
}
model {
vector[N] xb;
real a[N];
real b[N];
real p[N];
xb = x * beta;
for (n in 1:N) {
... |
7dc040b677268c0aaad83da6da7009c8c32c5bdd9ca3c579cf9d0037359b07da | Stan | 501 | 28 | data {
int<lower=0> N;
int<lower=0> P;
matrix[N,P] x;
int<lower=0> ys[N];
int<lower=0> ns[N];
real<lower=0> concShape;
real<lower=0> concRate;
}
parameters {
real<lower=0> conc;
vector[P] beta;
}
model {
vector[N] xb;
real a[N];
real b[N];
real p[N];
xb = x * beta;
for (n in 1:N) {... |
88859d12696c7239a6027b88c0678ef00f45642ab3d44fc89273fd89738698a9 | Stan | 652 | 14 | /*
leafcutter is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
leafcutter is distributed in the hope that it will be usef... |
8209397987837bef297db95a4286c32561767fdb44ffd4631c699304739467c8 | Stan | 1,155 | 41 | data {
int<lower=0> N; // sample size
int<lower=0> P; // number of covariates
int<lower=0> K; // number of classes
vector[P] x[N]; // covariates
vector[K] y[N]; // counts
real<lower=0> concShape; // concentration shape
real<lower=0> concRate; // concentration rate
}
parameters {
simplex[K] beta_raw[P];... |
c3985ea7686a1537896ac0f2f87f5c52e82f0da7dbeb4313dbb3a1caf7f6fe0d | Stan | 1,221 | 44 | data {
int<lower=0> N; // sample size
int<lower=0> P; // number of covariates
int<lower=0> K; // number of classes
vector[P] x[N]; // covariates
vector[K] y[N]; // counts
real<lower=0> concShape; // concentration shape
real<lower=0> concRate; // concentration rate
}
parameters {
simplex[K] beta_raw[P];... |
569b2018e89f23e69b49d537c58bedcf5bdae952dc77b6da16183fd1f3f5d036 | Stan | 1,497 | 50 | data {
// data dimensions
int<lower=0> n_voxel;
int<lower=0> n_frame;
int<lower=0> n_nus;
// data index
array[n_voxel] int<lower=1, upper=n_voxel> idx_voxel;
array[n_frame] int<lower=1> tr_idx;
// observations
array[n_voxel] vector[n_frame] Y;
// design matrix
matrix[n_frame, n_nus] X_n... |
49a6ef58891f117d668a2a6ac51af1952b140dff5163005d4df5e088366b5fc3 | Stan | 1,556 | 52 | data {
int<lower=0> N; // sample size
int<lower=0> P; // number of covariates
int<lower=0> K; // number of classes
vector[P] x[N]; // covariates
vector[K] y[N]; // counts
real<lower=0> concShape; // concentration shape
real<lower=0> concRate; // concentration rate
real<lower=0> outlier_prior_a;
real... |
084bed41cf4032acf22820e08f14ee71f23532177b3b5a21bccb5ad720fc0f1b | Stan | 1,882 | 62 | data {
// data dimensions
int<lower=0> n_roi;
int<lower=0> n_voxel;
int<lower=0> n_frame;
int<lower=0> n_nus;
// data index
array[n_voxel] int<lower=1, upper=n_roi> idx_roi;
array[n_voxel] int<lower=1, upper=n_voxel> idx_voxel;
array[n_frame] int<lower=1> tr_idx;
// observations
array[n... |
4a4c7dcedba946a6bce7c02c902de6512dd58e0189755c78661a9f0af8df2ee2 | Stan | 4,152 | 109 | data {
// data dimensions
int<lower=0> n_voxel;
int<lower=0> n_frame;
int<lower=0> n_nus;
int<lower=0> n_hemo;
// data index
array[n_frame] int<lower=1> tr_idx;
array[n_hemo] int<lower=1> hemo_idx;
// observations
array[n_voxel] vector[n_frame] Y;
// design matrix
vector[n_frame] X;
// nuisanc... |
eb6669b816c1bcea6e0d64238ae20d76b12eb567a64377df6a19171d482f0395 | Text | 10 | 1 | # decoding |
7535ebcee9937110f685502ab6b27964d024d47e3883d803120ea8ce53c037cf | Text | 13 | 1 | # AIDMAN_CODE |
2e2f63f77eaa3bb6bb99b8dcffdaaac68c236aa30fcd769d4dfaffb2582fe02e | Text | 19 | 1 | # EEGNet Fusion V2
|
e57b16dad8e83a4ae772f80272c4999edbbb27bb847f52d31a64fe90486e1e9a | Text | 21 | 1 | # industry_prediction |
30bde7f0d5b17379c186fe28b5232d0c1460db4dace7af507c8518f230f58cc3 | Text | 22 | 2 | # Scherer_et_al_2023
|
cc772665e217debdb182a87c320d31f9349361467a66c4f498f8e35526ef6d56 | Text | 28 | 4 | pbmpi
=====
phylobayes mpi
|
f1a433d23f0c9f8a31dfbb4549eae77855341a9b622fe29c9148f209f4fe5b85 | Text | 32 | 1 | # 2023_MotorRep_duringAdaptation |
7c95a26f200a1aae1e74b579ceeaa48a624b7df13363375125190fe04d98cffc | Text | 35 | 2 | # Cl3ft
Pilot Cleft Analysis Tools
|
80b2eeb4c5d9408e4bc7d638a25ebc7cbc8d17358ff7b70bbd9c4ff7f1900e84 | Text | 50 | 2 | # muscle_texture
More information following soon.
|
bd283d04260b5f43f9f0a89ca740c59b825724c9935b284f534a476320b95a49 | Text | 52 | 3 | # EMPD
This is the code used for the EM-PD project
|
3e9bc8cc690fc0c38a3b8647092dab7be7db1c75827d1fd0f40654dc35a07523 | Text | 72 | 1 | Pycroscopy3D is a collection of tools for miscroscopy, written in python |
4d4f9e1ddc02a09a2764e326ddfa44c1df733e3df424c71c8307b478578e4fa1 | Text | 82 | 2 | # HA_time
Hyper-acuity algorithm for estimating spike timing from Calcium imaging
|
6f793f6b67ae1b94a931f2eced248e9840d276995a7de9cc1fec0bbf23637606 | Text | 87 | 2 | # DynamicBinauralProcessing
Code associated with studying Dynamic Binaural Processing.
|
33334655c3daba9d794b5c39f6a1367ea01d0c7a9bce229d4c9b4aa73189288c | Text | 106 | 3 | # `bioinformatics-snakemake-pipelines`
Collection of Snakemake pipeline for common bioinformatics tasks.
|
d16897018800394265f94b5e378fc1030541a20ad6a6d9804733b9fb34ba59bd | Text | 106 | 2 | # FHS-MrOS-16S
FHS and MrOS 16S microbiome analysis and figure scripts used for the Okoro et al 16S paper
|
5a97d54cb63c5b372cf38b469afbb396c37fdc16826ca554a129a1606f794110 | Text | 109 | 2 | # eeg-spheres
Alternative formulation for the EEG lead field in a volume conductor with N concentric spheres
|
be47fee8728d9258e017f72f0c217b5fce288b84ee00dc76ed6a1019e0bc27a5 | Text | 152 | 2 | # UBNIN
Unique Brain Network Identification Number (UBNIN) for encoding brain networks of individual subject from Structural Magnetic Resonance Imaging
|
5192ce89d44696e78f13c3a9cf388d3e6e130bd5199c8308b3c78c8514e7c5db | Text | 193 | 4 | # SleepBeta
Code and source data for SleepBeta paper.
All codes have been tested using Python 3.8 in a Windows 10 laptop computer.
Source data have been uploaded together with the source code.
|
47aeb58b9fb87e656953db32c1d0485191422006303201bfc001f50e73a20446 | Text | 215 | 2 | # Ashar_2023_CBP_reattribution
Accompanies Ashar et al. 2023, using natural language methods to investigate shifts in chronic back pain patients' pain attributions following treatment with pain reprocessing therapy
|
2da01a5bb3438cf5a4997ec3eaf0411966aefee30bb3d00736a8f7c29005b066 | Text | 232 | 14 | # rat-simulator
A rat in an environment simulator.
### Installation:
`$ pip install rat-simulator`
### Links to Project
Github link:
https://github.com/Vemundss/rat-simulator
PyPi link:
https://pypi.org/project/rat-simulator/
|
66d752a845adf40aee126d78e0327bd558b33655d548877d7f094024b61b5b93 | Text | 232 | 2 | # Sip TRiggered Optogenetic Behavior Enclosure (STROBE) FPGA Project & Source
This repository contains the VHDL source (built atop the FlyPAD by Itskov et al) for the STROBE device. The VHDL project was built in Quartus II 13.0sp1.
|
8c080fe4c39cb71762f744a2cef3d72935d5bbd4e0a3d9779e3dfc533271bcae | Text | 261 | 4 | # TextRelevancy
Text relevancy deep neural network trained for automatic identification of neuronal reconstructions from the literature
Further information and documentation on how to install and use the tool API at [Joindbre Docs](https://docs.joindbre.com)
|
506a7075c8962082dd056b5d608795713976d332050fcc7e12611bcf980d0047 | Text | 269 | 4 | # p4-phylogenetics
A Python phyloinformatic toolkit, and an implementation of tree-heterogeneous models of evolution.
Documentation is at [https://pgfoster.gitlab.io/p4doc/](https://pgfoster.gitlab.io/p4doc/),
mirrored at [https://p4.nhm.ac.uk/](https://p4.nhm.ac.uk/)
|
5c5e07774a43d799da255e68c72e2e1dbd73a02657243f3b3dac45246308b5ce | Text | 276 | 7 | The three sheets in the excels represents the following
assay - measurements of metabolites / lipids / proteins
rowData - information about metabolites / lipids / proteins including IDs, pathways etc.
colData - information about samples including disease groups, age etc.
|
6879f321569d846dcf7e3be96bd72dc70cdc91498a47253dd4158f943f104bb4 | Text | 279 | 14 | # Hegemon
Exploring bioinformatics datasets
```
cd Hegemon
chmod 777 tmpdir
javac tools/*.java
vim explore.conf
fix the absolute pathname for the dataset
```
You can put this directory inside your system www or public\_html
where php scripts can be requested from a browser.
|
92528a3c489f9c1eec3cc6754a58db8b8f58a859a35c960af7bd117a3e30f708 | Text | 287 | 2 | # NeuralRegenMagFieldReview
Our goal was to evaluate how magnetic fields affect neural repair in vitro. We started by performing a systematic review of publications that studied neural repair responses to magnetic field and then models magnetic field affects on neural repair processes.
|
f3c524be826e1bebffb6372407dc311e862ebc234d3af4b0910df45875d526b1 | Text | 291 | 2 | # RoQ-DCE-DL
Implementation for paper "Retrospective Quantification of Clinical Abdominal DCE-MRI Using Pharmacokinetics-Informed Deep Learning: A Proof-of-Concept Study" in Frontiers in Radiology, Sec. Artificial Intelligence in Radiology Volume 3 - 2023 | doi: 10.3389/fradi.2023.1168901
|
d929c5be457e1095edc134df4edb98b423f3d6f57572974cae8fed2c33c0838f | Text | 303 | 5 | # KCC2 reverse mode helps to clear postsynaptically released potassium at glutamatergic synapses
This repository contains the code for generating Figure 3 and S4 of the article: KCC2 reverse mode helps to clear postsynaptically released potassium at glutamatergic synapses
DOI: 10.5281/zenodo.8146820
|
e1dc68740d3ffeefb3387583011ddfbee2133cdd029b9f2710c55704b1b42b90 | Text | 321 | 2 | # References
M. H. Olívia, K. Ádám, K. András, T. Kálmán, E. Gabriella, and N. Attila, “Automated eye-blink artefact removal from 64-channel human electroencephalogram (EEG) recordings,” in Joint Meeting of the Hungarian Neuroscience Society (MITT) - Austrian Neuroscience Association (ANA), 2023, pp. 86–86.
|
1b19ec70acc11dcd302dd25820ac8bb19a2e5f3ad06ab18f510fb01617c8b7e4 | Text | 328 | 16 | [](https://zenodo.org/badge/latestdoi/375333265)
# rat-simulator
A rat in an environment simulator.
### Installation:
`$ pip install rat-simulator`
### Links to Project
Github link:
https://github.com/Vemundss/rat-simulator
PyPi link:
https://pypi.org/project/rat-sim... |
95f4231a9abb1a0e83a98673e83ea1648f5f9f0f5dd5783ed63aec4c2645fc68 | Text | 388 | 2 | # Single nuclei transcriptome and epigenome analysis of motor cortex and frontal cortex postmortem samples from C9-ALS and C9-FTD donors
Code repository for analysis in "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation" (submitted to Nature Communications) [bio... |
4e6dfa879e387f211a073faebad2ca3cad76546542b6a8a2ddacbe955122d338 | Text | 402 | 8 | ## Towards Interpretable Seizure Detection Using Wearables
Irfan Al-Hussaini, Cassie S. Mitchell, ICASSP 2023
Link to paper: https://ieeexplore.ieee.org/document/10097091
This work represents the winning submission to the [Seizure Detection Challenge: ICASSP 2023](https://signalprocessingsociety.org/publications-reso... |
b98ce321f057ca91f55c394ac17be7f9912ee3bc43bd305ec24b99c3c54bcfa9 | Text | 412 | 8 | # A multi-layer feature fusion method for few-shot image classification
This repo is part of the paper "[A multi-layer feature fusion method for few-shot image classification](https://www.mdpi.com/2419014)". It provides the respective code and a dataset covering 28 categories of pests and beneficial insects from maize ... |
ce05cb46dab73520f5dae57ef0c209c69e66c5c8e2b39ffa3ab9d829e6980d7f | Text | 421 | 4 | # GeneralAnalysis
Multi-functional data analysis programs
This repository contains a range of different MATLAB functions that are all provided as-is. There are no guarantees that anything will work, but most of the supplied functions should work fine. Some functions are well documented, others not at all. If you're in... |
ea336986a9e9967b371614b6cedeab4b867a9340ff9f0d99e9a501d731abac90 | Text | 422 | 5 | # Tempotron
This [tempotron](https://www.nature.com/articles/nn1643) implementation is a fork of [this original repo](https://github.com/dieuwkehupkes/Tempotron) by
[Dieuwke Hupkes](https://dieuwkehupkes.nl/). I am using the tempotron in a research project. I made it work in Python3, fixed some numpy deprecation w... |
39f8123f961d06b5afbe64ba45b95ea43e801a6d23b0844ebab962c42797556a | Text | 432 | 6 | # neuro-metabolic-model
Code accompanying the manuscript titled "Mechanisms underlying pathological cortical bursts during metabolic depletion".
1) Example simulation and plotting scripts for AI, Iso, BS, and SZ regimes: simulations.m & plotting_simulations.m
2) Code for the DP algorithm to find the optimal projection... |
e534d81e6a2559f7552fe0351a96b0b2930326eac11455f146c531f169de775a | Text | 471 | 11 | # StrokeAnalyzer
Code base and case examples for statistical analysis of "Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists" https://arxiv.org/abs/2211.15341
Accepted in Scientific Reports.
Please cite as
```
Ostmeier, Sophie, et al. "Non-inf... |
3a0c4fb80acbdbd66eacd11b6201ab052a24db72f00fab14ee098a963c442fde | Text | 488 | 18 | # Description
TriNet is a tri-fusion netural network for anticancer and antimicrobial peptides recognition. By inputting the peptide sequences and PSSM profiles, you can retrain the model.
# Requirements
Python 3.9.7
Python packages: Tensorfollow(vr.2.8.0), Matplotlib, scikit-learn, numpy, pandas and scipy
if you ... |
6f48bd228df9099311fa93507f0f17834a53d881c8831e716ea1a532c8c891c7 | Text | 504 | 11 | # Machine-Learning-Assisted-Vector-Atomic-Magnetometry
NN code for paper:
> Xin Meng, Youwei Zhang, Xichang Zhang, Shenchao Jin, Tingran Wang, Liang Jiang, Liantuan Xiao, Suotang Jia and Yanhong Xiao,
> ***Machine Learning Assisted Vector Atomic Magnetometry*** (arXiv: [2301.05707](https://arxiv.org/abs/2301.05707)).
... |
12228563b9f2622ea9f8d030cd8ac152733ea22e4864835b5e14d99ffa66638e | Text | 532 | 9 | # BitterBuster
Originally Developed by Team HAI Fall 2022
Angela Zhang, Constanza Tong, Ruizi Wang, Yuan Tan
All scripts and related assets fall under the CC BY-NC-SA 4.0 License
All future derivations of this code should contain the above attribution
## Overview
BitterBuster is a Fall 2022 Project led by Team HAI as... |
5a90ae02444ad719f02a834c62761abee511db0a96bfbd26e959cd8c74d1276c | Text | 608 | 23 | # emergent-grid-cells
### Create and install Conda env from .yml file:
```console
$ conda env create -f environment.yml
```
### Activate conda environment:
```console
$ conda activate ml
```
### Add anaconda environment to ipykernel:
```console
$ python -m ipykernel install --user --name=ml
```
Source:
https://mediu... |
c00d9ddaf530b497d1f64619648cba6eb0f3bee3b9431ec539b686c93c19c5a9 | Text | 613 | 6 | fMRI data has been reconstructed from raw multiband EPI data. The image orientation has been corrected using FSL. If opened in FSLeyes, S - I, superior - inferior; A - P, anterior - posterior;R - L, right - left.
Each animal had 1-3 repetitions of 10-min resting-state imaging in one scan session (post day 1 or post ... |
e40ba3f209e1ffe1a8d19283f5b3d023d0722231fed3045b12b965636aed5431 | Text | 618 | 24 | # pywinEA2
Refactored version of pyWinEA, evolutionary algorithms' library.
Documentation still in progress... For more information on basic usage of the library see the __examples__ directory.
## Installation
To install this library you can do the following:
```bash
git clone git@github.com:FernandoGaGu/... |
631048483eaf1f239a929b12a04b99ac9819ee085cf4cbe99de92b747055e38a | Text | 628 | 11 | # P1_P2_detection_ratio
Since it is not possible to publicly share intracranial pressure signals from real patients, a synthetic toy dataset is provided as an example to run both the analysis pipelines. Therefore, the results obtained with it have no scientific significance.
Training pipelines used to select NN archi... |
f9e093cbd60ebb56d701d2746cb7c83726504bc726f95375948d7ff66ed3200e | Text | 635 | 20 | # iHerd
**iHerd**: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease
## Setup
Clone the repository.
```
git clone https://github.com/aicb-ZhangLabs/iHerd.git
```
## Requirement
To create a new conda environment for iHerd, run this com... |
6776c3f62742417bf006813902b850eaf76750fbef792bfb01a806c0c065ff04 | Text | 651 | 7 | # SIR-RBD
Regional brain measurements of iRBD patients used for running the agent-based Susceptible-Infected-Removed (SIR) model.
_RBDDBM.mat_, _RBDthickness.mat_, and _RBDarea.mat_ contain the average age-, sex-, and site-corrected W-scores (based on age- and sex-matched healthy controls recruited in each centre) fo... |
2c983665ca98775a2cd630f83599f03db3bf662cbe75a4e73e1ab3f2af388bc2 | Text | 660 | 10 | # HierarchicalBayesianEEGfMRI
Stan code for the manuscript - Estimation of fMRI Responses Related to Epileptic Discharges Using Bayesian Hierarchical Modeling
Zhengchen Cai1, Nicolás von Ellenrieder1, Andreas Koupparis2, Hui Ming Khoo3, Satoru Ikemoto1, Masataka Tanaka4, Chifaou Abdallah1, Saba Rammal1, Francois Dubea... |
c98150580d520b015f661739a5e5d866fbd646483f3803b22f9ec4ced20dfa2f | Text | 661 | 4 | # Sip TRiggered Optogenetic Behavior Enclosure (STROBE) Software and Source
This repository contains the UI source and executable, and Python post-processing scripts for the STROBE device.
The STROBE UI was built in Visual Studios 2012 in C++ on Windows 10. It will require installation of Qt 5.11 in order to compile. ... |
e5406b3e4866f766365e01413a3ac579c1ed713e2a54e81130912308b4106e17 | Text | 704 | 25 | [](https://zenodo.org/badge/latestdoi/362146259)
# emergent-grid-cells
### Create and install Conda env from .yml file:
```console
$ conda env create -f environment.yml
```
### Activate conda environment:
```console
$ conda activate ml
```
### Add anaconda environment t... |
9b89bef0b243a5e593754fcc3939e2040e01911dfe917cf6c730f5abb06a0292 | Text | 718 | 25 | ## SMART 3
### Checking Model Metadata
We pass through tensorflow serving at this url:
```/model/metadata```
If the model input names change, then we need to change it in the code
### APIs
Classify programmatically
```/api/smart3/search```
You can put in your peaks as a json list of dicts, with 1H,13C as heade... |
7bd3c1b01aa9be3cd49c8767100101066a9115b6c6c57e179c803f71b9b42850 | Text | 754 | 7 | # Single nuclei transcriptome and epigenome analysis of motor cortex and frontal cortex postmortem samples from C9-ALS and C9-FTD donors [](https://zenodo.org/badge/latestdoi/564545578)
Code repository for analysis in "Divergent single cell transcriptome and epigenome alte... |
8ca8dee068f25e81a28b9f4e910aa92fcfe8487567c1cfd9f8bee7f033aecfd6 | Text | 764 | 19 | ### INTRODUCTION
This example code demonstrates methods for evaluating resolution and noise in reconstructions of MR data by non-linear and non-stationary methods.
The methods together with results on in-vivo MR data are published in the paper
Assessment of resolution and noise in Magnetic Resonance images reconstruc... |
a4d3e68adeef21465da7cab96e4191a93899c0c96cf453e65805afc99e2bf67a | Text | 764 | 7 | # Technical Development and In Silico Implementation of 3D-QALAS on a 1.5T MRI-Linear Accelerator Hybrid System: A Prospective R-IDEAL Stage 0/1 Technology Development Report
Code for the paper: "Technical Development and In Silico Implementation of 3D-QALAS on a 1.5T MRI-Linear Accelerator Hybrid System: A Prospective... |
050890ca0a960b5e3413cd21bb2f14da9d52d519af6f476861bed67dd0967037 | Text | 774 | 5 | References
----------
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilit... |
e150827ff0442a9a66017ebaad70bde4e75acf711f10488f6c67e8710ee65d48 | Text | 819 | 22 | # Feature Selection - Framework Code
[](https://www.python.org/downloads/release/python-390/)
Repo contains code used to generate the results and figures for the manuscript to JBO.
1. pca_fs_framework
2. lda_fs_framework
3. bipls_fs_framework
4. ensemble_fs_... |
c8362f0297937b25e36f1211ba672308104b893a00c63e3ac3d47796a0ccbd45 | Text | 827 | 14 | [](https://github.com/percolator/percolator/actions?query=workflow%3A%22Build+and+release%22)
This repository holds the source code for percola... |
a04d9ae4fab551aee8b6eac72960687e149321e099aae991372c275fcecec88d | Text | 829 | 24 | # Stan-X Tools
Various tools associated with mapping transposons (originated as part of the StanEx project)
As of December 2020, a transposable element mapper, variant caller, synthetic reference genome generator, and download helper are provided.
These tools are subcommands of the main "sx" command. For example:
``... |
2b91f1bfd9bd5206ed47f1e7be746c33c74597f5d4cf0a7c57ddf2f1e4569f63 | Text | 831 | 12 | # README
It is a pytorch implemention of paper "BRITS: Bidirectional Recurrent Imputation for Time Series, Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li Yitan Li. (NerIPS 2018)". The paper can be found here. http://papers.nips.cc/paper/7911-brits-bidirectional-recurrent-imputation-for-time-series
To train the BRIST m... |
889cb6a5b577c686e072e80829d3eda7a91cbaf4c67720dc1f1361931fef4b24 | Text | 850 | 19 | README.txt for MacPherson, R.A. et al., "Genetic and Genomic Analyses of Drosophila melanogaster Models of Chromatin Modification Disorders"
Relevant GEO Accession number: GSE213763
Files
Coregulated_Genes
-- raw data and statistical analysis code for experiments involving knockdown of genes coregulated with th... |
ece7a0b137e2e82e99df9b56f5cd6ffa6b5703ce81e617846eeb4f94f5d6f84a | Text | 864 | 15 | ## Percolator
[](https://github.com/percolator/percolator/actions/workflows/build.yml) [](code_of_conduct.md)
This reposit... |
ef96905f0559a79508f249ea4bfd19b53828873719b71f056f0fb35e16e79364 | Text | 900 | 20 | Weighted projective spaces
==========================
This is a dataset of 150000 randomly generated weighted projective spaces with at worst terminal singularities, in dimensions 1 to 10.
The data consists of the plain text files "rank_1_dim_N.txt" where N, which is the dimension of the weighted projective space, is... |
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