Data for DeepRank2 tutorials

This data contains raw PDB files and other metadata that can be used to run DeepRank2 tutorials for processing and later training on protein-protein interfaces and protein structures containing single residue variants. The PDB files in data_raw/ppi/ folder have been generated using PANDORA software. The PDB files in data_raw/srv/ folder have been retrieved from Ramakrishnan et al

Design, Fabrication and Testing of a Configurable Full-Field Stimulus Source for Electroretinography

The retina is the fundamental component of vision as it contains the photoreceptors, specialized cells which convert light stimuli into electrical signals, and which represent the first stage of the visual process. The electroretinogram (ERG) is a functional test widely used in clinical practice to help diagnose diseases of the retina and optic nerve. In fact, it measures the electrical response to a light stimulus of retinal cells and different ERG tests can be used to probe different retinal areas and cells. The tests most used are the full-field ERG, which consists of flashes of variable intensity and frequency, the multifocal electroretinogram (mfERG), which consists of illuminating in a pseudo-random way hexagonal elements on a monitor, and finally the pattern electroretinogram (pERG), which consists in high-contrast reversals of a grating pattern. While the first is performed by probing the entire retina, mfERG and pERG testing is usually limited to its central portion. The stimulators most commonly used in visual electrophysiology are Ganzfeld stimulators, spherical devices used to perform full-field ERG, and flat monitors, which are able to display patterns to perform standard mfERG and pERG. Devices capable of performing all the tests of interest are obtained by integrating a Ganzfeld stimulator with an internal or external monitor. The stimulators can use different technologies, but light emitting diodes (LEDs) are becoming very popular since they are almost ideal light sources. The conventional pERG testing is limited to the central part of the human visual field and this limits the sensitivity of the test. Since pERG probes retinal ganglion cells (RGCs), and since the first cells to be damaged by glaucoma are peripheral RGCs, it may be extremely helpful in early detection of this optic neuropathy. Thus, the Neural Engingeering Vision Laboratory at UIC developed a three dimensional stimulus source able to perform peripheral pattern electroretinogram (ppERG). The flat monitors used in standard tests are not really suited for the detection of peripheral early disfunctions, since they do not probe the entire human visual field. Thus, the developed stimulator consisted in an hemispherical dome tilted with white LEDs in its peripheral part and its validation gave satisfactory results, obtaining waveforms similar to those obtained with conventional pERG. The purpose of this thesis is to build a stimulus source for electroretinography that is capable of presenting an arbitrary pattern of pixels to the entire visual field, thus bringing together the functionalities of Ganzfeld stimulators, standard monitors and ppERG stimulus source. The pattern of pixels shall be configurable to present stimuli for flash ERG, pERG, and mfERG protocols to any arbitrary sector of the visual field, giving also the possibility to use different colours

Implementation of an IoT Node for Biomedical Applications

The Internet of Things is an inter-networking of physical devices that communicate with each other through the Internet. The technological progress of the last few decades and more efficient wireless protocols led to an exponential growth of devices connected with an increasing amount of data exchanged across the globe. The birth of the IoT caused significant changes in different sectors, among which e-Health, involving big innovations in medical care, prevention and remote diagnosis. The goal of our project is to plan and implement an IoT node to collect clinical data and to detect atrial fibrillation through the analysis of electrocardiogram. We chose to focus on this particular arrhythmia because it represents one of the most common heart diseases. Additionally, it is often asymptomatic and associated with more dangerous illnesses. The device validation has been realized on a sample of patients affected by atrial fibrillation and other heart diseases, in order to evaluate the reliability of the obtained data and the efficiency of the algorithm. Finally, we have analyzed the advantages and limitations of the device, introducing potential future adjustments that could improve its functionality

PANDORA v2.0: A MODELLER-based peptide-MHC integrative modeling pipeline

<p>PANDORA is an anchor-restrained modeling pipeline for generating peptide-MHC structures.</p><p>It contains multiple functions to pre-process data and it's able to exploit different crucial domain knowledge provided by the user to guide the modeling.</p><p>The advised installation method is through conda (https://anaconda.org/csb-nijmegen/csb-pandora):</p><p>1) Request a MODELLER license at: https://salilab.org/modeller/registration.html</p><p>Replace XXXX with your MODELLER License key and run the command:</p><blockquote><p>alias KEY_MODELLER='XXXX'</p></blockquote><p>2) Install csb-pandora with conda:</p><blockquote><p>conda install -c csb-nijmegen csb-pandora -c salilab -c bioconda</p></blockquote><p>Alternatively, PANDORA  v2.0.0-beta2.2 can be installed from the provided .zip file (follow instruction for github installation in https://github.com/X-lab-3D/PANDORA/blob/master/README.md ).</p><p>PANDORA documentation can be found at: <a href="https://csb-pandora.readthedocs.io/en/latest/">https://csb-pandora.readthedocs.io/en/latest/</a></p><p>BioRxiv manuscript: https://www.biorxiv.org/content/10.1101/2023.07.20.549892v1</p&gt

PANDORA v2.0: A MODELLER-based peptide-MHC integrative modeling pipeline

<p>PANDORA is an anchor-restrained modeling pipeline for generating peptide-MHC structures.</p><p>It contains multiple functions to pre-process data and it's able to exploit different crucial domain knowledge provided by the user to guide the modeling.</p><p>The advised installation method is through conda (https://anaconda.org/csb-nijmegen/csb-pandora):</p><p>1) Request a MODELLER license at: https://salilab.org/modeller/registration.html</p><p>Replace XXXX with your MODELLER License key and run the command:</p><blockquote><p>alias KEY_MODELLER='XXXX'</p></blockquote><p>2) Install csb-pandora with conda:</p><blockquote><p>conda install -c csb-nijmegen csb-pandora -c salilab -c bioconda</p></blockquote><p>Alternatively, PANDORA  v2.0.0-beta2.1 can be installed from the provided .zip file (follow instruction for github installation in https://github.com/X-lab-3D/PANDORA/blob/master/README.md ).</p><p>PANDORA documentation can be found at: <a href="https://csb-pandora.readthedocs.io/en/latest/">https://csb-pandora.readthedocs.io/en/latest/</a></p><p>BioRxiv manuscript: https://www.biorxiv.org/content/10.1101/2023.07.20.549892v1</p&gt

dianna

What's Changed 386 dashboard selection method for xai method by @laurasootes in https://github.com/dianna-ai/dianna/pull/416 Pin prospector version to avoid pyroma install error by @loostrum in https://github.com/dianna-ai/dianna/pull/424 update test instructions by @cwmeijer in https://github.com/dianna-ai/dianna/pull/425 378 update button for execution by @geek-yang in https://github.com/dianna-ai/dianna/pull/396 Fixed error message with method selection and removed unnecessary run… by @cpranav93 in https://github.com/dianna-ai/dianna/pull/431 make label a required argument (Fixes #131) by @cwmeijer in https://github.com/dianna-ai/dianna/pull/426 Unpin prospector by @egpbos in https://github.com/dianna-ai/dianna/pull/442 429 implement recent dashboard layout changes to text tab by @laurasootes in https://github.com/dianna-ai/dianna/pull/438 refactor rise and fix linter issues (refs 447) by @cwmeijer in https://github.com/dianna-ai/dianna/pull/450 New Contributors @cpranav93 made their first contribution in https://github.com/dianna-ai/dianna/pull/431 Full Changelog: https://github.com/dianna-ai/dianna/compare/v0.6.0...v0.7.0</p

dianna

What's Changed Make input text lowercase before tokenizing by @loostrum in https://github.com/dianna-ai/dianna/pull/463 Add visualization for timeseries by @stefsmeets in https://github.com/dianna-ai/dianna/pull/491 Cache python install in github actions by @stefsmeets in https://github.com/dianna-ai/dianna/pull/482 400 implement label file upload support by @cpranav93 in https://github.com/dianna-ai/dianna/pull/451 Combine special characters into single token by @loostrum in https://github.com/dianna-ai/dianna/pull/462 467 write a short readme on how to start the dashboard by @laurasootes in https://github.com/dianna-ai/dianna/pull/497 Added label file upload for text by @cpranav93 in https://github.com/dianna-ai/dianna/pull/495 Add notebook requirement by @stefsmeets in https://github.com/dianna-ai/dianna/pull/499 Refactor dashboard by @stefsmeets in https://github.com/dianna-ai/dianna/pull/502 Make paths independent in notebooks by @stefsmeets in https://github.com/dianna-ai/dianna/pull/509 Add button to show/hide parameters by @stefsmeets in https://github.com/dianna-ai/dianna/pull/504 create label file for ResNet model by @laurasootes in https://github.com/dianna-ai/dianna/pull/513 Add license by @WillemSpek in https://github.com/dianna-ai/dianna/pull/516 Downgrade dash by @WillemSpek in https://github.com/dianna-ai/dianna/pull/530 517 dashboard robust path by @WillemSpek in https://github.com/dianna-ai/dianna/pull/518 add time step masking (refs #477) by @cwmeijer in https://github.com/dianna-ai/dianna/pull/494 Add pre-commit for linting/auto-formatting by @stefsmeets in https://github.com/dianna-ai/dianna/pull/508 435 long text does not fit in dashboard explanation image by @laurasootes in https://github.com/dianna-ai/dianna/pull/526 529 update readmes and roadmap by @elboyran in https://github.com/dianna-ai/dianna/pull/532 Run CI only on PRs ready for review by @stefsmeets in https://github.com/dianna-ai/dianna/pull/533 478 rise for time series by @cwmeijer in https://github.com/dianna-ai/dianna/pull/506 539 polish notebook rise timeseries by @geek-yang in https://github.com/dianna-ai/dianna/pull/540 New Contributors @stefsmeets made their first contribution in https://github.com/dianna-ai/dianna/pull/491 @WillemSpek made their first contribution in https://github.com/dianna-ai/dianna/pull/516 Full Changelog: https://github.com/dianna-ai/dianna/compare/v0.7.0...v0.8.0</p

Deeprank2

What's Changed Fix fix: check only 1 pssm for variant queries by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/430 fix: pdb files with underscore in the filename gives unexpected query ids by @joyceljy in https://github.com/DeepRank/deeprank2/pull/447 fix: dataset_train inheritance warnings by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/461 fix: cast hse feature to float64 by @DanLep97 in https://github.com/DeepRank/deeprank2/pull/465 fix: readthedocs after deeprank2 renaming by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/472 fix: force scipy version for fixing deeprank2 installation by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/478 fix: warning messages for invalid data in test_dataset.py by @joyceljy in https://github.com/DeepRank/deeprank2/pull/442 fix: make scipy 1.11.2 work by @cbaakman in https://github.com/DeepRank/deeprank2/pull/482 Refactor refactor: inherit information from training set for valid/test sets by @joyceljy in https://github.com/DeepRank/deeprank2/pull/446 refactor: rename deeprankcore to deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/469 Build build: improve installation making use of pyproject.toml file only and setuptools by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/491 CI CI: decrease sensitivity of test_graph_augmented_write_as_grid_to_hdf5 by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/445 CI: fewer triggers by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/457 Docs docs: update README.md by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/443 docs: create tutorial README by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/455 docs: improve installation instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/452 docs: add tutorials for PPIs by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/434 docs: add tutorials for variants by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/459 docs: minor improvements to install instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/484 docs: type hinting and docstrings in molstruct by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/497 docs: joss paper by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/423 docs: clarify ppi scoring metrics and add doc strings and tests by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/498 docs: add performances table for deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/493 Style style: auto-scrape trailing whitespace upon save in VS code by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/483 Full Changelog: https://github.com/DeepRank/deeprank2/compare/v2.0.0...v2.1.

Deeprank2

What's Changed Fix fix: check only 1 pssm for variant queries by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/430 fix: pdb files with underscore in the filename gives unexpected query ids by @joyceljy in https://github.com/DeepRank/deeprank2/pull/447 fix: dataset_train inheritance warnings by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/461 fix: cast hse feature to float64 by @DanLep97 in https://github.com/DeepRank/deeprank2/pull/465 fix: readthedocs after deeprank2 renaming by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/472 fix: force scipy version for fixing deeprank2 installation by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/478 fix: warning messages for invalid data in test_dataset.py by @joyceljy in https://github.com/DeepRank/deeprank2/pull/442 fix: make scipy 1.11.2 work by @cbaakman in https://github.com/DeepRank/deeprank2/pull/482 Refactor refactor: inherit information from training set for valid/test sets by @joyceljy in https://github.com/DeepRank/deeprank2/pull/446 refactor: rename deeprankcore to deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/469 Build build: improve installation making use of pyproject.toml file only and setuptools by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/491 CI CI: decrease sensitivity of test_graph_augmented_write_as_grid_to_hdf5 by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/445 CI: fewer triggers by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/457 Docs docs: update README.md by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/443 docs: create tutorial README by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/455 docs: improve installation instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/452 docs: add tutorials for PPIs by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/434 docs: add tutorials for variants by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/459 docs: minor improvements to install instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/484 docs: type hinting and docstrings in molstruct by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/497 docs: joss paper by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/423 docs: clarify ppi scoring metrics and add doc strings and tests by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/498 docs: add performances table for deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/493 Style style: auto-scrape trailing whitespace upon save in VS code by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/483 Full Changelog: https://github.com/DeepRank/deeprank2/compare/v2.0.0...v2.1.