Genetic modeling software

GP Learners for classification:. A cloud based platform for generating transparent, non-linear, large scale regression problems. GenProg, Evolutionary Program Repair? Software maintenance accounts for over two-thirds of that life cycle cost, and a key aspect of maintenance is fixing bugs in existing programs.

Unfortunately, the number of reported bugs far outstrips available development resources. It is common for a popular project to have hundreds of new bug reports filed every day. GenProg reduces software maintenance costs by automatically producing patches repairs for program defects.

Human repairs often involve inserting new code and deleting or moving existing code. GenProg uses those same building blocks to search for repairs automatically.

HeuristicLab is a framework for heuristic and evolutionary algorithms that is developed by members of the Heuristic and Evolutionary Algorithms Laboratory HEAL since A Python based genetic programming application suite with support for symbolic regression and classification.

Karoo GP is a scalable platform with multicore support, designed to work with realworld data. As a teaching tool, it enables instructors to share step-by-step how an evolutionary algorithm arrives to its solution. As a hands-on learning tool, Karoo GP supports rapid, repeatable experimentation with a simple interface.

Brameier and W. Banzhaf LGP is a paradigm of genetic programming that employs a representation of linearly sequenced instructions in automatically generated programs. A linear approach lends itself to programs which have two unique attributes: a graph-based functional structure and the existence of structurally non-effective instructions.

These software measures could be then alluded to as the most discriminative features in the classification problem. Secondly, the hyperboxes can overlap which is not surprising as they are formed in a highly dimensional feature space with respect to some software measures and become quite distinct as far as some other features are concerned. This occurs for the number of lines of code and the number of comments.

Experiment 3. Here we are interested in the description of software modules requiring low maintenance effort by defining a group of modules with the number of changes less than 7 so we are concerned with the description of low maintenance software modules.

The results are reported in a similar format as shown in the first experiment. Here we consider 1 and 2 hyperboxes as the two design alternatives of interest. Because of the anticipated character of the class involving low maintenance modules , we can envision that the hyperbox will be eventually spreading from the low bounds assumed by the software measures towards their higher values.

As illustrated in Figure 7, one hyperbox leads to better results than those coming with the use of the two or more hyperboxes. Classification rate for one and two hyperboxes for the training dark bars and testing data gray bars ; shown are average values of the rates The coverage of the hyperbox expressed in terms of the individual software measures helps us assess a discriminative property of the features.

As visualized in Figure 8, we note that the most discriminative software measures are the number of lines of code, program length, and the number of code characters. For instance, the rules could read as follows - if number of lines of code is A and program length is B, and the number of code characters is C…and ….

Conclusions We have developed a comprehensive design process of hyperbox classifiers. The two-phase development environment appeared to be a viable optimization structure. By initiating the development of the hyperboxes through fuzzy clustering we were able to concentrate a search for the geometry of the data and focus the ensuing genetic optimization on the most promising regions of the feature space.

The most visible advantage of the hyperbox classifier lies in its interpretability and this feature is fully exploited in the design of the classifier for the software data. The equivalent representation of the hyperbox classifier comes as a collection of rules where each hyperbox corresponds to a single rule.

We experimented with the software MIS dataset and showed how the classifier leads to a collection of rules describing software modules of some assumed quantification of software modules. While in this study we have confined ourselves to a two-class problem that is usually treated as a generic classification environment , the approach readily extends to a multiclass problem. We note however that the development of two-class classifiers is a fundamental and central task of classification.

Once those have been designed their aggregation is carried out through some straightforward techniques such as a max selection. Interestingly, the use of the genetic algorithm itself could be helpful in an overall optimization of the resulting ensemble of the classifiers. Likewise we could extend the hyperbox classifiers to an object-based suite of software measures.

References [1] J. Baker, Adaptive selection methods for genetic algorithms, Proc. De Falco, A. Della Cioppa, and E. Tarantino, Discovering interesting classification rules with genetic programming, Applied Soft Computing 1, , Duda and P. Eshelman and J. Gabrys, A. Neural Networks, Vol. Garmus, D. Addison- Wesley, [9] D.

Goldberg, Real-coded genetic algorithms, virtual alphabets, and blocking, Complex Systems, 5, , Haupt, S. Haupt, Practical Genetic Algorithms, J. York, Herrera, M. Lozano, and J. Verdegay, Tackling real-coded genetic algorithms: Operators and tools for behavioral analysis, Artificial Intelligence Review, vol. Springer- Verlag, Heidelberg, 3rd edition, [14] K. Muller, D.

Munson, T. FlowJo provides an intuitive interface, specialized analysis platforms, and open-ended plugin architecture. FlowJo supports your statistical work e. FCS Express supports you with plots, gates, cell cycle analysis and proliferation analysis. The data can be easily exported to microsoft office. Unlike the most of commercial and non-commercial related software tools, GelClust is very user-friendly and guides the user from image toward dendrogram through seven simple steps.

The program works for DNA or protein analysis as well as western blotting techniques. Due to its workflow-based concept, this application has become a prime example of software usability Link Melanie Gel Electrophoresis yes Melanie provides a flexible interface to visualize, explore and analyze 2D electrophoresis gel images, in order to identify protein markers of interest through differential expression analysis.

WebTool is a user-friendly platform for Monte Carlo-based significance evaluation of pairwise distances. Enter your sequence, choose a pattern for your oligo nucleotides, and Whitehead will present you with a list of oligos matching your criteria.

From this list, select those oligos you would like to consider further. It accepts a short DNA sequence, and returns a scrambled sequence. Link SpliceCenter Genomics yes The tools on SpliceCenter help evaluating the impact of gene splicing variation on specific molecular biology techniques.

It supports phylogenetic reconstruction of very large gene families and determination of orthologs on a large scale. Phred it analyzes the peaks of DNA sequence chromatogram files to call bases, assigning quality scores "Phred scores" to each base call.

Choose between a variety of spezies and search for a specifc section to get detailled information Link. Another feature is to index reference sequence in the FASTA format or extract subsequence from indexed reference sequence.

Link Blast2GO High-throughput Sequencing yes Blast2GO is spepcialized for annotation of sequences and data mining on the resulting annotations, primarily based on the gene ontology GO vocabulary. With the help of an algorithm that considers similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations Blast2GO optimizes function transfer from homologous sequences.

The tool includes numerous functions for the visualization, management, and statistical analysis of annotation results, including gene set enrichment analysis.

Predictions are made directly from transcript sequences which is possible through the high quality of fungal transcript assemblies. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. The cumulative Skellam distribution function is used to detect significant normalised count differences of opposed sign at each DNA strand peak-pairs. Irreproducible discovery rate for overlapping peak-pairs across biological replicates is estimated using the package 'idr'.

The program provides different visualizations and statistical summaries for the detected ROIs and includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis.

No further knowledge of scripting languages required. It utilizes SPAdes for transforming the de Bruijn graph into the assembly graph and finds a subgraph of the assembly graph that we refer to as the plasmid graph. It further uses ExSPAnder for repeat resolution in the plasmid graph using paired reads and generates plasmidic contigs. Link PlasmidFinder 1. PlasmidFinder can be used for replicon sequence analysis of raw, contig group, or completely assembled and closed plasmid sequencing data.

The program detects a broad variety of plasmids that are often associated with antimicrobial resistance in clinically relevant bacterial pathogens. While OrfM is sequencing platform-agnostic, it is best suited to large, high quality datasets such as those produced by Illumina sequencers.

It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations. Additionally design and select a combination of cell structure probes. Supports smoothing, sharpening, edge detection, median filtering and thresholding on both 8-bit grayscale and RGB color images.

Measure area, mean, standard deviation, min and max of selection or entire image. Measure lengths and angles. Use real world measurement units such as millimeters. Generate histograms and profile plots. It also automatically records the steps in a cloning project. Enter your own sequence, or import a record from GenBank. Design and annotate primers for PCR, sequencing, or mutagenesis. Identify open reading frames ORFs with a single mouse click. Link Pymol Mass Spectronomy yes Pymol is a molecular visualization system.

PedHunter is being used by other research groups to query other genealogical databases. Follow one of the two PedHunter hyperlinks to retrieve a paper and software.

The software is designed to analyze data generated by a technique called comparative genomic hybridization, but it has also been used to analyze cytogenetic breakpoint data.