8 skills found
hodcroftlab / CovariantsReal-time updates and information about key SARS-CoV-2 variants, plus the scripts that generate this information.
neherlab / FfpopsimFFPopSim is a collection of C++ classes and a Python interface for efficient simulation of large populations, in particular when the product of mutation rate and population size is larger than one. It consists of one library for individual-based simulations, and a complementary one for simulation of the entire genotype distribution. The latter is coded efficiently using Fast-Fourier Transforms to speed up recombination operations.
malikfahad / Particle FilterParticle filters or Sequential Monte Carlo (SMC) methods are a set of genetic, Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made, and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of some Markov process, given some noisy and partial observations. Particle filters implement the prediction-updating transitions of the filtering equation directly by using a genetic type mutation-selection particle algorithm. The samples from the distribution are represented by a set of particles; each particle has a likelihood weight assigned to it that represents the probability of that particle being sampled from the probability density function.
shishirdas / Breast Cancer PredictionContent Past Usage: Attributes 2 through 10 have been used to represent instances. Each instance has one of 2 possible classes: benign or malignant. Wolberg,~W.~H., \& Mangasarian,~O.~L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In {\it Proceedings of the National Academy of Sciences}, {\it 87}, 9193--9196. -- Size of data set: only 369 instances (at that point in time) -- Collected classification results: 1 trial only -- Two pairs of parallel hyperplanes were found to be consistent with 50% of the data -- Accuracy on remaining 50% of dataset: 93.5% -- Three pairs of parallel hyperplanes were found to be consistent with 67% of data -- Accuracy on remaining 33% of dataset: 95.9% Zhang,~J. (1992). Selecting typical instances in instance-based learning. In {\it Proceedings of the Ninth International Machine Learning Conference} (pp. 470--479). Aberdeen, Scotland: Morgan Kaufmann. -- Size of data set: only 369 instances (at that point in time) -- Applied 4 instance-based learning algorithms -- Collected classification results averaged over 10 trials -- Best accuracy result: -- 1-nearest neighbor: 93.7% -- trained on 200 instances, tested on the other 169 -- Also of interest: -- Using only typical instances: 92.2% (storing only 23.1 instances) -- trained on 200 instances, tested on the other 169 Relevant Information: Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself: Group 1: 367 instances (January 1989) Group 2: 70 instances (October 1989) Group 3: 31 instances (February 1990) Group 4: 17 instances (April 1990) Group 5: 48 instances (August 1990) Group 6: 49 instances (Updated January 1991) Group 7: 31 instances (June 1991) Group 8: 86 instances (November 1991) Total: 699 points (as of the donated datbase on 15 July 1992) Note that the results summarized above in Past Usage refer to a dataset of size 369, while Group 1 has only 367 instances. This is because it originally contained 369 instances; 2 were removed. The following statements summarizes changes to the original Group 1's set of data: Group 1 : 367 points: 200B 167M (January 1989) Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805 Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial : Changed 0 to 1 in field 6 of sample 1219406 : Changed 0 to 1 in field 8 of following sample: : 1182404,2,3,1,1,1,2,0,1,1,1 Number of Instances: 699 (as of 15 July 1992) Number of Attributes: 10 plus the class attribute Attribute Information: (class attribute has been moved to last column) Attribute Domain Sample code number id number Clump Thickness 1 - 10 Uniformity of Cell Size 1 - 10 Uniformity of Cell Shape 1 - 10 Marginal Adhesion 1 - 10 Single Epithelial Cell Size 1 - 10 Bare Nuclei 1 - 10 Bland Chromatin 1 - 10 Normal Nucleoli 1 - 10 Mitoses 1 - 10 Class: (2 for benign, 4 for malignant) Missing attribute values: 16 There are 16 instances in Groups 1 to 6 that contain a single missing (i.e., unavailable) attribute value, now denoted by "?". Class distribution: Benign: 458 (65.5%) Malignant: 241 (34.5%) Acknowledgements O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition via linear programming: Theory and application to medical diagnosis", in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers). Inspiration Rouse Tek Bio informatics Cytogenomics Project is an attempt to bring the human genome to the understanding of how cancers develop. All of our bodies are composed of cells. The human body has about 100 trillion cells within it. And usually those cells behave in a certain fashion. They observe certain rules, they divide when they’re told to divide, they’re quiescent when they’re told to remain dormant, they stay within a particular position within their tissue and they don’t move out of that. Occassionally however, a single cell, of those 100 trillion cells, behave in a different way. That cell keeps dividing when all its signals around it tell it to stop dividing. That cell ignores its counterparts around it and pushes them out of the way. That cell stops observing the rules of the tissue within which it is located and begins to move out of its normal position, invading into the tissues around it and sometimes entering the bloodstream and becoming a metastasis, depositing in another tissue of the body.. The reason the cell has gone rogue is because it has acquired within its genome, within its DNA, a number of abnormalities that cause it to behave as a cancer cell. All 100 trillion cells in the human body have got a copy of the human genome, they have 2 copies, 1 maternal, 1 paternal. Throughout Life all those copies of the genome in those 100 trillion cells, are acquiring abnormal changes or somatic mutations. These mutations are present in the cell and are not transmitted from parents to offspring. They are constrained to that individual cell. Those mutations occur in every cell of the body, normal and abnormal, for a number of different reasons. They occur because every time a cell divides possibly one letter of code out of 3 billion is replicated incorrectly. And that’s 1 source of somatic mutations. Another source is that our 100 trillion cells are being exposed to a number of different onslaughts like radiation, self generated chemicals from inhalation of things like tobacco smoke or even an unhealthy diet over time. Occasionally mechanisms in a particular cell make breakdown and the DNA of that cell begins to acquire somatic mutations rather more commonly than other cells. So in summary, every cell in the body acquires mutations throughout a lifetime, and as we get older we acquire more and more somatic mutations in which occasionally a particular type of gene is mutated where the protein that it makes is abnormal and drives the cell to behave in a rogue fashion that we call cancer.
akdemirlab / MutationalDistributionSet of scripts utilized in Akdemir et al., Nat Genetics Oct. 5 2020.
kousathanas / MultiDFEInference of the Distribution of Fitness effects of new mutations with Maximum Likelihood
amohseni / Frequency Dependent Moran ProcessComputational model of the Moran process for 2x2 symmetric normal form games. Derives the Nash equilibria, evolutionarily stable states, and stochastically stable state of each game, given the size of the population, mutation rate, and intensity of selection. Also, plots the stationary distribution, and allows for the simulation of individual evolving populations.
BianChang / ImmunoAIzerA computational framework for cell distribution and gene mutation prediction in tumor microenvironment
