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GATK pipline to processing raw read data

4 minute read

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I’m sharing with you a sketch of comprehensive pipline for processing raw sequencing data. The pipeline mainly employs Bash and Python. The bioinformatic tools I’m using including samtools, Picard, GATK and bwa. I’m outlining the steps below:

Explorit relationships among biomechanical features from data of orthopaedic patients

less than 1 minute read

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Biomedical data set built by Dr. Henrique da Mota during a medical residence period in the Group of Applied Research in Orthopaedics (GARO) of the Centre Médico-Chirurgical de Réadaptation des Massues, Lyon, France. Each patient in the data set is represented in the data set by six biomechanical attributes derived from the shape and orientation of the pelvis and lumbar spine (in this order): pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius, and grade of spondylolisthesis. The following convention is used for the class labels: DH (Disk Hernia), Spondylolisthesis (SL), Normal (NO), and Abnormal (AB). In this exercise, we only focus on a binary classification task NO=0 and AB=1 In this Blog, I will walk through how do we predict a patient as Normal or Abnormal by training the model with 6 biomechanic features. The main method here is the K-nearest neighbor prediction. Two questions are addressed:

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.