The Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss blog is the place to keep up to date with our latest news / info & tutorials.

Our blog not only provides easy access to articles published via our website, but also provides links to our publications off site.

A complete guide to linear regression using gene expression data: an introduction 2021-04-03

A complete guide to linear regression using gene expression data: an introduction

Discusses how to use linear regression with transcriptomic data, introducing linear regression and the math behind it.

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DBSCAN and Gaussian Mixtures with gene expression data 2021-03-19

DBSCAN and Gaussian Mixtures with gene expression data

This tutorial is the continuation of the previous tutorial where we discussed algorithm belonging to partitional clustering (k-means) and hierarchical clustering.

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xDNN for SARS-CoV-2 identification in patient CT scans 2021-02-22

xDNN for SARS-CoV-2 identification in patient CT scans

This article is based on the work of Nitin Mane and his GitHub release: HIAS COVID-19 xDNN Classifier.

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Complexity reduction techniques with gene expression data 2021-02-08

Complexity reduction techniques with gene expression data

This tutorial will focus on different reduction complex techniques using gene expression data. In this tutorial and in the following I will use data from acute myeloid leukemia (AML), which is one of the most fatal malignancies.

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Artificial intelligence in leukemia 2020-10-30

Artificial intelligence in leukemia

In this article our immunology and bioinformatics expert Salvatore Raieli, focuses on other tasks and other type of data exploited in deep learning (DL) in hematology. As aforementioned, deep learning can be useful in many tasks related to leukemia. In the precedent review we focused on image data analysis. Here we will consider different other data sources and application such as therapy selection, differential diagnosis, risk predictions.

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Medical image diagnosis in leukemia 2020-10-20

Medical image diagnosis in leukemia

In this article our immunology and bioinformatics expert Salvatore Raieli, focuses on machine learning and deep learning in medical images diagnosis. The increase in available data, hardware capabilities and cloud computing are allowing a great development in the field, and medicine is benefiting from this revolution. Nowadays, many algorithms can be run in a personal computer or in cloud service, increasing the potential number of users and researchers.

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Acute Myeloid Leukemia: A general introduction 2020-10-09

Acute Myeloid Leukemia: A general introduction

In this article our immunology and bioinformatics expert Salvatore Raieli describes briefly what AML is, the current classification, current therapies available, and open questions.

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COVID 2020 — A data scientist perspective 2020-03-18

COVID 2020 — A data scientist perspective

Team member Dr Amita Kapoor published her findings on the COVID-19 pandemic. This research was referenced in the peer-reviewed paper: Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network by Kathakali Biswas, Abdul Khaleque, and Parongama Sen.

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98% Accuracy Acute Lymphoblastic Leukemia Detection 2020-03-06

98% Accuracy Acute Lymphoblastic Leukemia Detection

The final classifier achieves 98 (97.979)% using Tensorflow 2 & Ubuntu/GTX 1050 ti . You can run the classifier independently and classify local images, serve an API endpoint for HTTP requests, or you can use it as part of the VR experience which will be uploaded soon.

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Acute Lymphoblastic Leukemia Papers Evaluation Part 1 2020-01-19

Acute Lymphoblastic Leukemia Papers Evaluation Part 1

Here we will replicate the network architecture and data split proposed in the Acute Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System paper and compare our results.

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Acute Lymphoblastic Leukemia Papers Evaluation Part 2 Tensorflow 2.0 2020-01-19

Acute Lymphoblastic Leukemia Papers Evaluation Part 2 Tensorflow 2.0

Here we will train the network we created in part 1, using the augmented dataset proposed in the Leukemia Blood Cell Image Classification Using Convolutional Neural Network paper by T. T. P. Thanh, Caleb Vununu, Sukhrob Atoev, Suk-Hwan Lee, and Ki-Ryong Kwon.

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Acute Myeloid/Lymphoblastic Leukemia Data Augmentation 2019-03-12

Acute Myeloid/Lymphoblastic Leukemia Data Augmentation

The AML/ALL Classifier Data Augmentation program applies filters to datasets and increases the amount of training / test data available to use. The program is part of the computer vision research and development for the Peter Moss Acute Myeloid/Lymphoblastic (AML/ALL) Leukemia AI Research Project.

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