attiqrafiq Goto Github PK

acp-dl

A deep learning model to predict anticancer peptides.

acpred-webserver

ACPred webserver

aip_2d_dataset

aizkolari

Aizkolari is a set of tools in Python to measure mathematical distances from different groups of Nifti images (brain MRI in my case) to do a supervised feature selection for supervised classification with SVMPerf, without circularity.

ampfeatureselection

Wrapper-method to select sub-set of features for classifying antimicrobial peptides

antivpp

atlasreader

Python interface for generating coordinate tables and region labels from statistical MRI images

attiqrafiq.github.io

awesome-aidd

Awesome AI-aided Drug Discovery

bioinformatics-with-python-cookbook-second-edition

Bioinformatics with Python Cookbook Second Edition, published by Packt

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

covid19

covid19-1

Using Kalman Filter to Predict Corona Virus Spread

datasciencetools

Useful Data Science and Machine Learning Tools,Libraries and Packages

deep-learning

deepdta

ensemble-classifier-chain-model

featureselect

FeatureSelect

genim

ian_sarna_notebook

ifeature

iFeature is a comprehensive Python-based toolkit for generating various numerical feature representation schemes from protein or peptide sequences. iFeature is capable of calculating and extracting a wide spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. Furthermore, iFeature also integrates five kinds of frequently used feature clustering algorithms, four feature selection algorithms and three dimensionality reduction algorithms.

javastream

lexparsimon.github.io

:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.

lime

machine-learning-engineer-nanodegree-capstone-project-analysis

*****PROJECT SPECIFICATION: Machine Learning Capstone Analysis Project***** This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). In published studies, Machine Learning has been applied to Alzheimer’s/Dementia identification from MRI scans and related data in the academic papers/theses in References 10 and 11 listed in the References Section below. Recently, a close relative of mine had to undergo a sequence of MRI tests for cognition difficulties.The motivation for choosing this topic for the Capstone project arose from the desire to understand and analyze potential for Dementia and AD from MRI related data. Cognitive testing, clinical assessments and demographic data related to these MRI tests are used in this project. This Capstone project does not use the MRI "imaging" data and does not focus on AD, focusses only on Dementia. *****Conclusions, Justification, and Reflections***** [Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.] The formulation of OASIS data (Ref 1 and 2) in terms of a dementia classification problem based on demographic and clinical data only (and without directly using the MRI image data), is a simplification that has major advantages and appeal. This means the trained model can classify whether an individual has dementia or not with about 87% accuracy, without having to wait for radiological interpretation of MRI scans. This can provide an early alert for intervention and initiation of treatment for those with onset of dementia. The assumption that the combined cross-sectional and longitudinal datasets would lead to dementia label classification of acceptable accuracy came out to be true. The method required careful data cleaning and data preparation work, converting it to a binary classification problem, as outlined in this notebook. At the outset it was not clear which algorithm(s) would be more appropriate for the binary and multi-label classification problem. The approach of spot checking the algorithms early for accuracy led to the determination of a smaller set of algorithms with higher accuracy (e.g. Gadient Boosting and Random Forest) for a deeper dive examination, e.g. use of a k-fold cross-validation approach in classifying the CDR label. The neural network benchmark model accuracy of 78% for binary classification was exceeded by the classification accuracy of the main output of this study, the trained Gradient Boosting and Random Forest classification models. This builds confidence in the latter model for further training with new data and further classification use for new patients.

machine-learning-engineer-nanodegree-capstone-project-proposal

This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). Cross-Sectional and longitudinal OASIS MRI structural and demographic data (clinical, demographic, and brain related derived anatomic measures) from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/) will be used to train a set of linear and non-linear machine learning classification models. Pandas will be used for data loading and Python scikit-learn library for modeling. The goal is to train machine learning models to predict whether the individuals in the cross-validation set (test set) have dementia (CDR>0), and if they do, the severity level of dementia (CDR values 0.5, 1, and 2). The problem will be formulated both as a binary classification problem (CDR=0, and CDR>0) and a mult-iclass classification problem (CDR classes 0, 0.5, 1, and 2). Will Train a supervised machine learning classification model to properly classify the OASIS data according to clinical dementia ratings(CDR values). Candidate models will be chosen from the Python scikit-learn library. These models will include Logistic Regression, Linear Discriminant Analysis, KNN, Naive Bayes, CART, and SVM. The best model will be selected for detailed analysis of data based on the "Accuracy" metric.

multiple-roc-curves-in-a-single-plot

The code loops through the list of models and plots respective roc curves in a single plot

multirm

Code for paper 'Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications'

nd00333_azmlnd_c2

neural-network-architecture-diagrams

Diagrams for visualizing neural network architecture (Created with diagrams.net)