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deepvariant

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Python_Decision_Tree_and_Random_Forest

I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.

Blooddonorprediction

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

CASTOR_KRFE

Alignment-free method to identify and analyse discriminant genomic subsequences within pathogen sequences

My-labs

In these assignments, I worked with pandas, numpy, matplotlib, biopython libraries. There is also a local alignment algorithm for gene sequences. In each subfolder there is a task, how I solved it and a report, if it is required.

Bioinformatics-projects

First project is "Detecting gene from given DNA Sequence and Showing or visualize the output". Second Project is "Local Alignment Sequence Implementation". Third Project is "Implement the Motif Finding Problem in Python".

Dot-Matrix-Implementation-for-Local-Gene-Sequence-Allignment

This Notebook contains the implementation of the Dot Matrix algorithm for local gene sequence alignment.

Decision-Tree

Decision Tree algorithms: ID3, C4.5 and CART; Prunning algorithms: CCP, REP, PEP, MEP and IMEP.

notes