Pinned Repositories
AntarticAlgebra
Equation balancing game for SugarOS using pygame
Brain-Cog
Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for Brain-inspired Artificial Intelligence and simulating brains at multiple scales. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its ev
Breast_cancer_Prediction
Content 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.
content
The content behind MDN Web Docs
Discrete-Math-Fundamentals-Comp.-Sci.
Gauss
Gaussian field equations
gNamespace
IJava
A Jupyter kernel for executing Java code.
Lessons
Lorenz
sincak's Repositories
sincak/AntarticAlgebra
Equation balancing game for SugarOS using pygame
sincak/Brain-Cog
Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for Brain-inspired Artificial Intelligence and simulating brains at multiple scales. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its ev
sincak/Breast_cancer_Prediction
Content 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.
sincak/content
The content behind MDN Web Docs
sincak/Discrete-Math-Fundamentals-Comp.-Sci.
sincak/Gauss
Gaussian field equations
sincak/gNamespace
sincak/IJava
A Jupyter kernel for executing Java code.
sincak/Lessons
sincak/Lorenz
sincak/machacks
sincak/Math-Education
sincak/music_recommender
Music recommender using deep learning with Keras and TensorFlow
sincak/patronus
The Patronus Project (R) is a voluntary member driven block-chain based surveillance bot
sincak/princetonAOS
Repo to keep track of the lectures
sincak/pgmpy_notebook
Short Tutorial to Probabilistic Graphical Models(PGM) and pgmpy
sincak/RadLens2.0
RadLens web app upgraded and migrated to the Latest Tensorflow 2.0 and Tensorflow.js ver 1.0
sincak/Relativity
sincak/spikingjelly
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
sincak/spytorch
Tutorial for surrogate gradient learning in spiking neural networks