Search Results for "lottery prediction algorithm"
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Showing 26 open source projects for "lottery prediction algorithm"
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tsfresh
Automatic extraction of relevant features from time series
tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python.... -
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NannyML
Detecting silent model failure. NannyML estimates performance
...By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance. -
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miRDeep*
MiRDeep*
Please cite: An, J., Lai, J., Lehman, M.L. and Nelson, C.C. (2013) miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res, 41, 727-737. We will create index for you if you tell us your interested species (j.an@qut.edu.au). download command line version "MDS_command_line_Vxx.zip" clicking "Browse All Files" please find miRPlant in sourceforge for plant miRNA prediction. -
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TextGen
textgen, Text Generation models
...This project implements the training and prediction of Seq2Seq, ConvSeq2Seq, and BART models based on PyTorch, which can be used for text generation tasks such as text translation. -
Accounting practice management softwareCanopy is a cloud-based practice management software for accounting and tax firms, offering tools for client engagement, document management, workflow automation, and time & billing. Its Client Engagement platform centralizes interactions with a secure portal, customizable branding, and email integration, while the Document Management system enables organized, paperless file storage. The Workflow module enhances visibility into tasks and projects through templates, task assignments, and automation, reducing human error. Additionally, the Time & Billing feature tracks billable hours, generates invoices, and processes payments, ensuring accurate financial management. With its comprehensive features, Canopy streamlines operations, reduces stress, and enhances client experiences.
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AmPEP and AxPEP
Sequence-based Antimicrobial Peptide Prediction by Random Forest
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens due to its broad range of activities and low toxicity. However, identification of AMPs through wet-lab experiment is still expensive and time consuming. AmPEP is an accurate computational method for AMP prediction using the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Our optimal model, AmPEP with 1:3 data ratio achieved a very high accuracy of 96%, MCC of 0.9, AUC-ROC of 0.99 and Kappa statistic of 0.9. AmPEP outperforms existing methods with respect to accuracy, MCC, and AUC-ROC when tested using the benchmark datasets. ... -
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AUDACITY
AUtozygosity iDentification And ClassIfication Tool
AUDACITY is novel computational approach for the identification of Runs of Homozygosity by using VCF files from whole-exome and whole-genome sequencing data generated by second generation sequencing technologies. AUDACITY is a tool integrating novel RoH detection algorithm and autozygosity prediction score for prioritization of mutation-surrounding regions. ###################################################################### The AUDACITY tool has been published on Computational and Structural Biotechnology Journal (CSBJ). . You can find it at https://www.sciencedirect.com/science/article/pii/S2001037020303354 ###################################################################### -
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GWOVina 1.0
The Hybrid Grey Wolf Optimization for Protein-Ligand Docking
Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. Based on the implementation of AutoDock Vina, GWOVina employs grey wolf optimization (GWO) algorithm to speed up the search for optimal ligand poses. Our rigid docking experiments show that GWOVina has enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, GWOVina is also competitive in pose ranking. ... -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
ML-From-Scratch is an open-source machine learning project that demonstrates how to implement common machine learning algorithms using only basic Python and NumPy rather than relying on high-level frameworks. The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and... -
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CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
The most trusted software in constructionHCSS provides easy-to-use software built for construction companies that want to win more work, work smarter, and boost profits. For nearly 40 years, we've helped heavy civil contractors, infrastructure builders, and utility companies improve operations, from estimating and project management to field tracking, equipment maintenance, and safety. Tools like HeavyBid, HeavyJob, and HCSS Safety are built for the field and designed to work together, giving your team real-time visibility, tighter cost control, and better job outcomes. With 45+ accounting integrations and customizable APIs, HCSS fits seamlessly into your tech stack. We regularly update our software based on feedback from real crews, ensuring it fits the way your team works. Backed by award-winning 24/7/365 support and a proven implementation process, HCSS helps reduce risk, cut inefficiencies, and deliver fast ROI. If you're ready to grow your business and gain a competitive edge, HCSS is the partner that gets you there.
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DACO-algorithm
A novel transcription factor complex prediction algorithm.
Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. Complexation of transcription factors and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enable to infer controlled genes as well as the exerted regulatory mechanism. We developed DACO (domain-aware cohesiveness optimization), a novel algorithm that combines... -
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PF_HP
Prediction of proteinfolding in 2D HP model
Even in the simplified two dimensional HP-model (hydrophob/polar) the prediction of proteinfolding is NP complete. We implement a brute force algorithm with serial and parallel execution to solve short inputs of HP sequences (0-1 bitstrings). Selbst im vereinfachten zweidimensionalen HP-Modell (hydrophob/polar) ist die Proteinfaltung bereits NP-vollständig. Hier implementieren wir einen brute-force Algorithmus zur Lösung kurzer Eingabesequenzen (0-1-Bitstrings) für die Proteinfaltung. ... -
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BioPPSy
A biochemical property prediction system
Computationally predicts the pharmacokinetic properties of drug candidates using Quantitative Structure Property Relationships (QSPR) modelling. Assembles a set of tools and databases for predicting the physical properties of small molecules. The program models a given property's dependence on a collection of molecular and structural descriptors using a training set of molecules. Neural networks and support vector regression are available, as well as linear models. The models generated by... -
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faif
C++ header only library with AI and bioinformatics algorithms
C++ header only library, small and fast; Naive Bayesian Classifier, Decision Tree Classifier (ID3), DNA/RNA nucleotide second structure predictor, timeseries management, timeseries prediction, generic Evolutionary Algorithm, generic Hill Climbing algorithm and others. -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. ... -
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data... -
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Community profiling (CP) is a guilt-by-association approach for predicting functional associations between proteins: two proteins are predicted to be associated if they share similar presence and absence profiles (called community profiles) across microbial communities. Community profiling is conceptually similar to the phylogenetic profiling approach to functional prediction, however with fundamental differences. This package provides different profile construction methods and correlation metrics, which we have shown are important for optimizing the performance of this new approach. Please refer to our paper for details of the algorithm: Dazhi Jiao, Wontack Han and Yuzhen Ye. Functional association prediction by community profiling. ...
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ingap-cdg
codon-based de Bruijn graph algorithm for gene construction
Currently, most gene prediction methods detect coding sequences (CDSs) from transcriptome assembly when lacking of closely related reference genomes. However, these methods are of limited application due to highly fragmented transcripts and extensive assembly errors, which may lead to redundant or false CDS predictions. Here we present a novel algorithm, inGAP-CDG, for effective construction of full-length and non-redundant CDSs from unassembled transcriptomes. inGAP-CDG achieves this by combining a newly developed codon-based de bruijn graph to simplify the assembly process and a machine learning based approach to filter false positives. ... -
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CNVision
CNV prediction from Illumina genotyping data
CNVision is a Perl script that runs Illumina genotyping data (all chips from 300k to latest Omni) through PennCNV, QuantiSNPv2.3 and GNOSIS (an in-built algorithm). It merges the results and assesses the quality of the raw data. CNVision can also identify de novo CNVs in family-based data using a highly accurate algorithm that considers the possibility of CNVs in either parent based on the raw genotyping data. The script is optimized to work in a UNIX-based environment; it should work in... -
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ExSTraCS
Extended Supervised Tracking and Classifying System
This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. -
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Unsupervised Random Forest
On-line Unsupervised Random Forest
This tool uses Random Forest and PAM to cluster observations and to calculate the dissimilarity between observations. It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. ... -
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DeNovoCheck
DeNovoCheck: Inheritance analysis for NGS trio data
DeNovoCheck is intended to be used for inheritance analysis in NGS tio data. For rare dominant Mendelian diseases, patient-parent trios are often used to reduce the number of candidate variants. The algorithm bases the inheritance prediction on the data available in the parental BAM files and allows for a fast and reliable selection of potential de novo variants. -
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GSP program are based on Bayesian framework with an EM algorithm to predict genome size iteratively, which is elegant in mathematics. The model first develop under the no sequencing error model, then extend to the sequencing errors containing model.
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BayesianCortex
simple algorithm for a realtime interactive visual cortex for painting
A paint program where the canvas is the visual cortex of a simple kind of artificial intelligence. You paint with the mouse into its dreams and it responds by changing what you painted gradually. There will also be an API for using it with other programs as a general high-dimensional space. Each pixel's brightness is its own dimension. Bayesian nodes have exactly 3 childs because that is all thats needed to do NAND in a fuzzy way as Bayes' Rule which is NAND at certain extremes. NAND can be... -
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Shape is a molecular conformation prediction program. It uses a genetic algorithm to efficiently search the conformational space of a biomolecule and then clusters the results. It is very simple to use.
