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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:
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This is a literature review and demonstration of what I did for futures works based on this study.
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This is a literature review and demonstration of what I did for futures works based on this study.
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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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This is a literature review and demonstration of what I did for futures works based on this study.
Published:
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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Plan to buy an RO system with Waterdrop. Check out available models here
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This is a literature review and demonstration of what I did for futures works based on this study.
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I compeleted Completed Quantitate Analysis of immunostaining tumor cells with Metamorph and EXCEL and presented in University Undergraduate Research Forum.
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This is a cool project from UCI MCSB designed by Dr. Jun Allard.
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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:
Published:
This is a literature review and demonstration of what I did for futures works based on this study.
Published:
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:
Published:
I compeleted Completed Quantitate Analysis of immunostaining tumor cells with Metamorph and EXCEL and presented in University Undergraduate Research Forum.
Published:
I compeleted Completed Quantitate Analysis of immunostaining tumor cells with Metamorph and EXCEL and presented in University Undergraduate Research Forum.