The use of computational tools for data preprocessing and analysis is daily activity for any bioinformatician. Having a interdisciplinary field, a plethora of tools exist in the domain, written by developers from various fields and languages. Many times there can be multiple candidate tools exists to accomplish any given task. Often it is the decision
Writing Python Wrapper for Bioinformatics Tools Read More »
This tutorial shows how to do molecular docking in colab. Molecular docking is an essential process in scientific drug discovery to predict the binding mode and affinity of a small molecule (ligand) to a target protein. This method can help to identify potential drug candidates by virtually screening large chemical libraries and selecting compounds that
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It is often required for aspiring data scientists to do portfolio projects either to show their skills or just simply for the pure joy of doing it. In either cases, the complexity of the project can be often a small one to medium one. To truly convince the recruiters, it is often required to do
Free Compute and Storage for Machine Learning Read More »
This post is a case study on primary or metastasis classifier using deep learning models. Overview Genetics is behind every physiological aspect of a living being such as height, eye color, hair color, etc. Mutations are unwanted changes happening to genetic code for various reasons. The main reasons account for such changes are hereditary, exposure
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This tutorial walks you through building a breast cancer survivability prediction classifier which use clinical and gene expression data by employing machine learning techniques. Overview Cancer is a disease in which cells in the human body grow uncontrollably. This phenomenon often spreads to other parts of the body. Human bodies contain trillions of cells. Human
Breast Cancer Survival Prediction with Clinical and Gene Expression Data Read More »
Introduction Probability distributions are cornerstones in the theory of machine learning. The entire field of machine learning is often theoretically viewed or explained from a probabilistic perspective. Compared to the linear algebraic point of view, the probabilistic perspective gives formulations of machine learning algorithms that are more impressive. Binomial distribution is one of the many
Binomial Distribution Read More »
The Jupyter Notebook is best suited for doing as well as documenting empirical research. It can contain text, code images, videos, interactive content, and also nicely typesetting equations. This makes anyone doing simulations or empirical research prototype their work and share it with their colleagues. The same Jupyter notebook can be used to visualize protein
Visualizing Protein Structures in Jupyter Notebook Read More »
Frequentist and Bayesian are two schools of thought in probability theory. Let us look at the difference between them. Bayesian and frequentist approaches are two major frameworks in statistics for analyzing data and making statistical inference. The frequentist approach is based on the idea of probability as the long-term frequency of an event in repeated
Frequentist and Bayesian: A Quick Comparison Note Read More »
Graphical User Interface (GUI) has been useful and easy to use for all types of computer users. We all use it every day to get things done. However, the computing history has the provision to do command-line instructions also to do the same things. Linux terminal is one such command line interface. Let us take
Linux Terminal Basics Read More »
This article is a reflection and articulation about the paper titled “Fair Active Learning” authored by Hadis Anahideh and Abolfazi Asudeh. The research paper is available on Arxiv and I am reviewing version 2 of it. The paper deal with bias in data and unfairness in the context of Active Learning. Learning algorithms do their
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