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15,277 results for “GENETICS”
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 4. Structural Crossover
<p>Guided crossover operator is based on the two point separation from parents are selected<br> Left and right parts of them are related to each other by the condition to be meaningful With this<br> new child of his parents is that. But a new generation of the random choice to have reached this<br> stage. The crossover rate is fixed for our algorithm.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 5. Insertion and Deletion Hidden Layer in NN
<p>Change in NN structure is other method that we used to optimization of solution[18].<br> Insertion a hidden layer caused to mutation operator is much natural. As connection with father and<br> mother nodes is easily[20],[21]. Weights of node and errors automatically calculated.<br> For each stage of the implementation of the mutation operator in genetic algorithms, neural<br> networks, only one of the nodes in the hidden layer is selected and inserted. These layers are<br> inserted on condition that the definition does not harm the network structure and the action is<br> meaningful. As an added layer can adjust the weights and the connection to the parent node of a network<br> layer to be removed.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 3. The Structure of Neural Network
<p>A neural network (NN), in the case of artificial neurons called artificial neural<br> network (ANN) or simulated neural network (SNN), is an interconnected group of natural<br> or artificial neurons that uses a mathematical or computational model for information<br> processing based on a connectionist approach to computation. In most cases an ANN is an adaptive<br> system that changes its structure based on external or internal information that flows through the<br> network[9].<br> In more practical terms neural networks are nonlinear statistical data modelling or decision<br> making tools. They can be used to model complex relationships between inputs and outputs or<br> to find patterns in data.<br> Two neurons neural network active in memory (ON or 1) or disable (Off or 0), and each<br> edge (synapses or connections between nodes) is a weight. Edges with positive weight, stimulate or<br> activate next active node, and edges with negative weight, disable or inhibit the next connected<br> node (if it is active) ones.</p>
Database of Pines from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown Under Two Watering Regimes: Implications for Management of Genetic Resources"
<p>Raw data from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown under Two Watering Regimes: Implications for Management of Genetic Resources" Forests <strong>2018</strong> <em>9</em>(2), 71. doi:<a href="http://dx.doi.org/10.3390/f9020071">10.3390/f9020071. </a></p> <p>The database correspond to seedlings of four Mexican pines: <em>P. oocarpa, P. patula</em> and <em>P. pseudostrobus</em>, that were submitted to two watering treatments: Field Capacity (FC) and Drought-Stress (DS), during 90 days. Growth and biomass, survival and ontogenetic score were measured.</p>
Taking advantage from phenotype variability in a local animal genetic resource: identification of genomic regions associated with the hairless phenotype in Casertana pigs
<p>Ped and Map files for 96 Casertana breed pigs genotyped with Illumina BeadChip 60K Porcine.<br> The first field of the ped file contains the id of the farm (1az-6az).<br> The hairless phenotype, in the ped phenotype field, is codified as 1, the hairy phenotype is codified as 2.</p>
Data on Direct-to-Consumer Genetic Testing and DNA testing companies
<p>This table was compiled between 2011 and 2018. It is a list of companies offering direct-to-consumer DNA tests over the internet. It is primarily concerned with direct-to-consumer genetic testing companies operating currently and also includes those, which are no longer operating. The table briefly summarizes the services offered by each company and gives the company’s location.</p> <p>I am continuing to update this list, so it is a work in progress, it does include some companies that advertise their services to physicians and some companies may have altered their offerings. Further updates will be provided later in 2018. I have added a shorter table at the end of relevant other companies. I hope to make a searchable database of a revised version of the document available through my website in the future.</p> <p>This work is being released for informational and educational purposes and should not be used for commercial purposes.</p> <p>Please refer to my website for further updates <a href="http://www.andelkamphillips.com">http://www.andelkamphillips.com</a></p>
Microsat Data for 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'
<p>Microsattelite data from 94 samples (<em>Stunus vulgaris</em>) from 3 populations and including 29 loci. Used in the paper 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'. </p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 9. Step by step drawing of the selected route solution
<p>When the solution that is desired to be viewed is double clicked, the connections between bus stops are drawn in turn, and the route is shown as can be seen in Figure 9.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 7. Application main form which includes route, bus stops and GA parameters
<p>For this study, the school bus routes within the Ankara Province were used as case studies. The school bus routes were recorded by using the Android application and instantaneous GPS monitoring method. The home address of each student was taken as a stopping point. At the end of each route, the distances between the beginning and end points were recorded. After transferring the obtained route data into the database, the ill-adapted points were eliminated. By means of the developed application, the existing school bus routes are dynamically optimized using GA. It was developed both as a mobile and desktop application. Using Android- based mobile software, the information regarding GPS locations of bus stops and school buses is transferred to the server on a real-time basis. Using the desktop software, where GAs are run, these coordinates are shown on a Google map, and the most suitable route is produced and sent to the school bus via server. This method provides the opportunity to dynamically reflect certain factors such as a different initial point for the school bus, some students being absent from the school on a particular day, and eventual changes on the existing roads on the route production process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process
<p>The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding
<p>Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA
<p>The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 10. Obtained experimental results
<p>The GA procedure was carried out using the parameter values that can be seen in Table 1, for a total of 10 school bus services’ routes identified by means of mobile-based software for a school located in the Ankara Province. The obtained experimental results are shown in Table 2 and Figure 10. The algorithm working duration also includes the formation of a distances’ matrix.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 1. Example of a VRP solution
<p>VRP is widely described as the process where the distribution vehicles deliver goods in a depot to the clients found in geographically dispersed locations and then return to the depot on the optimum route. The VRP, first introduced by Dantzig and Ramser in 1959, aims to minimize the total distance to be covered during the routing of the vehicles in a centrally-located depot (Dantzig & Ramser, 1959). The routing process is operated by taking the vehicle capacities into consideration so as to ensure that each client located on the routing plan is visited only once. The depot, vehicle, and goods concepts mentioned in the general description of VRP can be re-adapted as school, student and bus stop, based on different sectors that the problem applies to. Figure 1 shows a scheme indicating the VRP solution applied to a distribution system, where the clientele network is dispersed over three different zones.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route
<p>During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 2. A dynamic school bus routing case
<p>VRP’s can also be classified into two categories as dynamic and static routing problems. In the static VRP’s, the stops/locations that the vehicle will visit are pre-specified and do not change during the distribution/collection process. In dynamic VRP’s, on the other hand, new stops can be added to the planned route during the process or certain stops can be omitted. In similar dynamic problems, some or all of the access points are not known in the beginning. These points are dynamically defined during the route design or planning stages. In the dynamic VRP, using a real- time communication network between the vehicle and decision-making system, the vehicle routes can be re-defined during the operation. This type of problems is defined as online or real-time problems by some scholars (Pillac, Gendreau, Guéret & Medaglia, 2013). Two examples of this can be certain orders getting cancelled or new orders being taken while a water distribution vehicle is on its route, or a school bus being informed on its route that certain students will be absent from school that day. In current conditions, dynamic VRP’s are more frequently needed, and are attributed with a more specific importance. The first study dealing with dynamic VRP was carried out by Wilson and Colvin (Pillac, Gendreau, Guéret & Medaglia, 2013). The enhancements in GPS, traffic sensors, and mobile communication systems caused a further acceleration in studies carried out in this field. Within the context of this study, DSBRP will be investigated. DSBRP is graphically explained in Figure 2.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 18. Drawing of the time series for males and females of primary stage students and its prediction)
<p>Note that the Tabulated value equals 3.841 while the Q value is less than Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 17. Drawing of the time series for females of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 16. Drawing of the time series for males of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.