17 skills found
pyt-team / TopoNetXComputing on Topological Domains
jeffheaton / MergelifeEvolve complex cellular automata with a genetic algorithm.
DicSo92 / SandboxScienceSandbox Science is an interactive platform designed to make learning and exploring scientific concepts fun and accessible. The platform offers a variety of simulations, from cellular automata like Game of Life to complex particle interactions in Particle Life.
theislab / Graph AbstractionGenerate cellular maps of differentiation manifolds with complex topologies.
huBioinfo / CytoCommunityA spatial omics data analysis tool that enables both unsupervised and supervised discovery of complex tissue cellular neighborhoods from cell phenotypes.
aVariengien / Self Organized ControlTo explore how complex and stable motor control can emerge from neurons, we designed neural cellular automata to robustly control a cart-pole agent.
Aadityaza / 3d Growing Neural Cellular AutomataThis project is inspired by the paper "Growing Neural Cellular Automata" found at the link: https://distill.pub/2020/growing-ca/. The project's objective is to develop an extension of the methodology described in this paper to enable the generation of complex structures in three dimensions.
wildart / ComputationalHomology.jlThis package provides various computational homology tools for cellular complexes.
Manibarathi / FluoroCellTrackHigh-throughput droplet microfluidic devices with fluorescence detection systems provide several advantages over conventional end-point cytometric techniques due to their ability to isolate single cells and investigate complex intracellular dynamics. While there have been significant advances in the field of experimental droplet microfluidics, the development of complementary software tools has lagged. Existing quantification tools have limitations including interdependent hardware platforms or challenges analyzing a wide range of high-throughput droplet microfluidic data using a single algorithm. To address these issues, an all-in-one Python algorithm called FluoroCellTrack was developed and its wide-range utility was tested on three different applications including quantification of cellular response to drugs, droplet tracking, and intracellular fluorescence. The algorithm imports all images collected using bright field and fluorescence microscopy and analyzes them to extract useful information. Two parallel steps are performed where droplets are detected using a mathematical Circular Hough Transform (CHT) while single cells (or other contours) are detected by a series of steps defining respective color boundaries involving edge detection, dilation, and erosion. These feature detection steps are strengthened by segmentation and radius/area thresholding for precise detection and removal of false positives. Individually detected droplet and contour center maps are overlaid to obtain encapsulation information for further analyses. FluoroCellTrack demonstrates an average of a ~92-99% similarity with manual analysis and exhibits a significant reduction in analysis time of 30 min to analyze an entire cohort compared to 20 h required for manual quantification.
lowlighter / Cellular Automaton🧫 A complex cellular automaton which simulates an entire ecosystem : universe, flora and fauna.
govizlora / Geometry Celluar AutomatonUsing elementary cellular automaton rulesets to generate complex geometries
kallus / Lattice Gas AutomataSimulation project for the course Simulation of complex systems at Chalmers University of Technology. A model for gas physics using a cellular automata. Using Python, C, OpenMP, PyGame etc.
yoelmatveyev / FireworldCellular automata collection that started from a cellular automation suitable for building complex circuitry. Also multistate cyclical, Life-like, hexagonal, Larger-than-Life CA etc.
MagnusPetersen / MNNCAAn implementation of the Multi-Neighborhood Neural Cellular Automata (MNNCA), enhancing traditional NCAs with increased expressiveness for complex pattern generation
DominikSabat / GrainGrowthThis program simulates grain growth using cellular automata, a computational model widely employed in studying complex systems. The simulation is designed to replicate the process of grain growth, a phenomenon observed in materials science and metallurgy.
eric-weiss / Algorithmic ArtA collection of code snippets that generate cool pictures. They are mostly based on simple generalizations of 1D cellular automata, using complex arithmetic instead of discrete rules to implement the dynamics. I may or may not clean up the code at some point but feel free to ask me questions if you feel inclined to learn more.
Biswajit458 / Liver Cancer Prediction Using Machine Learning And PythonOne of the most complex internal biological structures in the human body is liver .Upper right hand part of the abdomen is located by the liver which is reddish brown in colour and measures eight and half inches . Liver is wedge shaped gland normally weighs 1440grams to 1660 grams. Liver is divided into Left lobe and right lobe and filters 1.5L of blood per minute approximately.Liver cancer is the most dangerous cancer among variety of cancer. Due to this every third living is cause of death and which is nearly a sixth most common cancer in the world. Liver cancer is also known by the name hepatic cancer and most of the liver cancer is common to Hepatic cellular carcinoma (HCC). Liver cancer is the uncontrolled growing of tissue within the liver. Tumours are of two types such as non-cancerous cells (benign) and cancerous cells (malignant). There are 12000 deaths per year in world due to liver cancer. To avoid this, problem need to be analysed in earlier stages because earlier detection can help doctors to save lives and does not make very much complication on the human health. There are various techniques to acquire the image of liver from the patients those are Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Ultra Sounds but CT image is represented as accurate liver cancer diagnosis imaging modularity.With this in mind, ML algorithm has been used in many studies to predict the therapeutic outcome of HCC patients. Thus, in this review, the advantages and disadvantages of each ML algorithm are clarified, and relevant literature on the prediction of therapeutic outcomes after various treatment modalities for HCC is described.
