Search Results for "prediction" - Page 5
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Showing 260 open source projects for "prediction"
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GPRED-GC
a Gene PREDiction model accounting for 5'-3' GC gradient
A new hidden Markov model (HMM)-based ab initio gene prediction tool for finding genes with highly variable GC contents such as the genes with negative GC gradients in grass genomes. -
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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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austenite transformations simulator
A solver for austenite transformations simulation.
A console software which is a solver for austenite transformations simulation. It has a text file in/out interface. Also there is a windowed shell for the calculation result processind and import selected results to excel files. Thermodynamics and kinetic calculations. For MS Windows only. -
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knnAUC
k-nearest neighbors AUC test.
knnAUC : k-nearest neighbors AUC test. In the knnAUC framework, we first calculated the AUC estimator based on a k-nearest neighbors classifier, and then evaluate the significance of the AUC based statistic (Alternative hypothesis: AUC > 0.5, that is to say, X has prediction power for Y). -
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CFM-ID
Competitive Fragmentation Modeling for Metabolite Identification
Windows binaries and cross-platform source code for processing of EI-MS (GC-MS) and ESI-MS/MS (LC-MS) data for identification of metabolites. Includes tools for applying Competitive Fragmentation Modeling (CFM) to spectrum prediction and metabolite identification tasks, as well as a tool for fragment generation, and another for peak annotation. For further documenation see https://sourceforge.net/p/cfm-id/wiki/Home/ For related publications, and their associated supplementary data... -
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BNNPT
A powerful method for nonlinear dependence of two continuous variables
Testing dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a powerful method for nonlinear dependence of two continuous variables (X and Y). We addressed this research question by using BNNPT (Bagging Nearest-Neighbor Prediction Test, software available at https://sourceforge.net/projects/bnnpt/). In the BNNPT framework, we first used the value of X to construct a bagging neighborhood structure. And then, we got the out of bag... -
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The Deep Review
A collaboratively written review paper on deep learning, genomics, etc
This repository is home to the Deep Review, a review article on deep learning in precision medicine. The Deep Review is collaboratively written on GitHub using a tool called Manubot (see below). The project operates on an open contribution model, welcoming contributions from anyone. To see what's incoming, check the open pull requests. For project discussion and planning see the Issues. As of writing, we are aiming to publish an update of the deep review. We will continue to make project... -
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PTM-X
a solfware for predicting posttranslational modification cross-talk
The PTM-X (PTM cross-talk) project is a freely available bioinformatics software platform that allows to calculate the features of PTM pairs and the posterior probabilities of being cross-talk pairs. The prediction model is a naive Bayse classifier that integrats five features: protein sequence distance, tertiary structural distance, co-location in a disordered region, residue co-evolution, and modification co-evolution. An online tool is also available at http://bioinfo.bjmu.edu.cn/ptm-x/. -
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Neopepsee
Accurate genome-level prediction of neoantigens by harnessing sequence
we developed Neopepsee, a method that applies a machine learning to classify personal immunogenicity with next generation sequencing data. -
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FragGeneScan is an application for finding (fragmented) genes in short reads. It can also be applied to predict prokaryotic genes in incomplete assemblies or complete genomes. FragGeneScan was first released through omics website (http://omics.informatics.indiana.edu/FragGeneScan/) in March 2010, where you can find its old releases. FragGeneScan migrated to SourceForge in October, 2013.
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NanoGLADIATOR
Real-time detection of copy number alterations from nanopore data.
Nano-GLADIATOR is a novel software tool that allows to analyze WGS data generated during a MinION or GridION X5 run for the detection of copy number alterations (CNAs). Nano-GLADIATOR (Nanopore Genomic aLterations AnD allelIc frAcTiOn pRedictor) is capable to perform CNVs/CNAs detection and allelic fraction prediction during the sequencing run ("On-line" mode for real time molecular karyotyping) and once the experiment is completed ("Off-line" mode, for parallel analysis of multiple samples... -
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MIGET graphical user interface
The multiple inert gas elimination technique (MIGET)
The multiple inert gas elimination technique (MIGET) was developed in the 1970s to measure the pulmonary exchange of a set of six different inert gases dissolved together in saline (or dextrose) and infused intravenously. It then uses those measurements to compute the distribution of ventilation/perfusion ratios that best explains the exchange of the six gases simultaneously. MIGET is based on the very same mass-conservation principles underlying the classic work of Rahn and Fenn and of... -
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EEG Seizure Prediction
Seizure prediction from EEG data using machine learning
The Kaggle-EEG project is a machine learning solution developed for seizure prediction from EEG data, achieving 3rd place in the Kaggle/University of Melbourne Seizure Prediction competition. The repository processes EEG data to predict seizures by training machine learning models, specifically using SVM (Support Vector Machine) and RUS Boosted Tree ensemble models. The framework processes EEG data into features, trains models, and outputs predictions, handling temporal data to ensure accuracy. -
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This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system. Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
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LibRec
Leading Java Library for Recommender Systems
LibRec is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking. -
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MetExpert is an expert system to assist users with limited expertise in informatics to interpret GCMS data for metabolite identification without querying spectral databases
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wgssat_nbfgr
WGSSAT: SSR Annotation Pipeline
WGSSAT provides a Graphical User Interface pipeline to mine and characterize SSR from Whole genome data. This pipeline integrates prediction of genes, ncRNA, repeats and SSR from whole genome assembly and mapping of these predicted SSR on to the genome (classified according to genes, ncRNA, repeats, exonic and intronic region) along with primer designing and mining of cross-species amplification markers. The mining of SSRs from whole genome provides valuable information on the abundance of SSRs... -
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MagNes
A modular C++ API for chemical structure handling and representation
MagNes is a C++ programming interface for implementation of different algorithms and graphical rendering through run-time plugins. MagNes currently provides: -A complete C++ API for managing of chemical information -A rich set of parsers (PDB, XYZ, Gaussian and many others) -An OpenGL based visor for molecular rendering. -A C++ API for implementation of Karplus-like equations for prediction of NMR scalar couplings. -
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OncoIMPACT
Cancer driver prediction via integrative omics
OncoIMPACT is a model-driven approach to integrate omics profiles (genomics, transcriptomics etc.) and provides patient-specific cancer driver gene predictions. -
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This R package combines biological information (expression data) with theoretical information (miRNA target prediction databases) in order to obtain a curated list of potential miRNA-mRNA interaction pairs.
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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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Crosshub
Multi-way analysis of The Cancer Genome Atlas (TCGA) project datasets
Crosshub enables multi-way analysis of RNA-Seq, miRNA-Seq and methylome data of The Cancer Genome Atlas (TCGA) project: 1. differential expression analysis (genes, alternative transcripts and miRNA) 2. regulatory miRNA prediction (TargetScan, DIANA microT, mirSVR, PicTar, miRTarBase + co-expression) 3. regulatory TF prediction (ENCODE ChIP-Seq + co-expression) 4. methylation profiling analysis 5. RNA-Seq vs. clinical (TNM, stage, follow-up) correlation analysis Generates Excel summaries... -
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ConsPred
ConsPred is a pipeline combining several gene/CDS annotation tools
... and adapted to specific needs. The genome annotations are produced in formats ready for submission to public sequence archives. Important notice: Conspred development has been ended with version 1.33 (except minor bug fixes). We are currently developing a completely rewritten Conspred2, which focuses on consensus gene prediction and high-quality gene start prediction. Conspred2 will not do any functional annotation (consider e.g. Prokka and GAMOLA2 for this purpose). -
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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...