Cancer is a leading cause of death worldwide, causing nearly one in six deaths. Resistance to therapy continues to be a primary cause of cancer treatment failures, resulting in a high number of cancer-related deaths. Recent technological advancements and the availability of a vast amount of omics data have ushered us into a new era of precision medicine, a paradigm shift away from one-size-fits-all cancer treatment toward patient-specific medicine. The main aim of this project was the in-depth statistical analysis of the NUC-7738 drug using multiple omics datasets and the development of machine learning (ML) models that can be used to predict in silico whether an individual would be sensitive or resistant. Through data analysis and visualisation, tumour origins that are likely to be sensitive and resistant to the drug were identified. Extensive correlation analysis identified genes that highly correlate with drug efficacy across different omics datasets (gene expressions, alternative polyadenylation, proteomics, gene effect, mutations). Furthermore, through a range of feature selection and ML methods, predictive models were created that can be used to predict drug response given a patient’s molecular characteristics. A multi-omics model that incorporates all the models’ predictions was also attempted, with modest success. Clustering cancer cell lines based on their alternative polyadenylation usage yielded two clusters with varied drug sensitivity. Ensemble clustering of the gene effect dataset revealed genes with similar dependency profiles, which is especially beneficial when it comes to identifying alternative druggable targets for genes of interest that are not conventionally druggable. Finally, correlation analysis when the drug was combined with two other promising drugs was performed. This showcased that when the drug was used in combination with Paclitaxel, the HINT1 gene, which is required to activate NUC- 7738, had a significantly stronger correlation with drug efficacy.

Main technologies used:

Python SciKit-learn Tensorflow SciPy NumPy Seaborn Matplotlib

Important note:

All of the code I have written is in a private repository, since the dataset is owned by NuCana and cannot be shared. Click below to explore one of the eight Jupyter notebooks I wrote, as a sample.

Cardiovascular Disease (CVD) is the number one cause of death worldwide, killing thousands of people every day. Recent advances in Machine Learning (ML) have brought new ways to diagnose CVD. The purpose of this project was to create a web platform that uses an ML model and a visualization library to enable cardiologists to visualize and predict their patient's condition in seconds. The project experimented with five ML classification algorithms (Random Forest, XGBoost, CatBoost, KNN, Logistic Regression), with Random Forest performing the best and therefore being deployed on a web platform developed. The web platform was developed using Flask.

Main technologies used:

Python SciKit-learn Flask Matplotlib Firebase

Following a discussion with a physicist friend, I created a simple website that allows you to use quantum random numbers to escape determinism!
The inability to predict the outcome of a process is defined as randomness. When rolling a die, for example, it is impossible to predict what number will come up. It is important to note, however, that randomness is not inherent in the process. A dice roll obeys the laws of mechanics and can be predicted if the initial conditions are known precisely and computing power is sufficient. Similarly, when a computer chooses an option at random, the numbers remain deterministic because they are generated using a seed value, which is required to initialize the algorithm. If the same seed value is used, the same sequence of "random" numbers will be generated. While this is adequate for some applications, the outcome can be predicted if the seed value and algorithm are known. Processes that follow precise laws are referred to as deterministic.
Laplace famously stated that if an intelligence had complete knowledge of the universe at a given instant and knew the laws of nature, it would be able to predict all future and past events. This means that everything we do in life, including you reading this text, is determined by the initial conditions of the universe.
Later, physicists discovered quantum phenomena, which are fundamentally random and involve elementary particles such as electrons and photons. No matter how much we know about the initial conditions, the outcome of these processes cannot be predicted. However, quantum phenomena exist only at the microscopic level and have no effect on processes at the human scale.
Here's where our website comes in. Quantum random number generators use the inherent randomness of quantum processes to generate a sequence of random numbers that, in theory, cannot be predicted. We use this to quantum randomly select one of the options you enter, allowing you to bring quantum randomness to the macroscopic scale and theoretically enjoy experiences and memories that are truly free of determinism.

Main technologies used:

HTML/CSS JavaScript Node.js Firebase

My prototype is a smart physical device that assists students in studying using the Pomodoro technique. I have been using this technique to divide my study sessions into small, highly focused chunks separated by short breaks since I was fifteen years old. I have always wished for a system that would allow me to track this habit while also visualising and rewarding my progress in a meaningful way. This prototype is based on this concept and provides a complete system to accomplish that.

Features:

• Its main component is powered by an Arduino Uno and includes a holographic timer and alarm system (such the name Holodoro).
• Semi-autonomous plant watering system that is watering the user’s favourite plant based on their studying habits, powered by an Arduino micro.
• Wireless component on this system located outside of the room that turns LED lights red whenever the user is studying, and green whenever they are having a break (do-not disturb feature).
• System is mainly controlled using voice commands (NLP).
• Uses Firebase for its authentication and database purposes, and provides a web interface using p5.js.
• Other smart features include: measuring plant growth over time and generating a visual comparison. Automatic pause of a study session whenever the user leaves the computer, powered by ML.

Main technologies used:

HTML/CSS JavaScript ml5.js Firebase p5.js Arduino

There are multiple agents moving in a pseudo-random fashion within an art canvas, that the user can completely control using hardware. The user can change the agents number (count), width/size, colours, and movement (noise scale and strength), as well as the canvas’ background and alpha channel. A range of inputs are used such as a joystick to navigate and select options, a potentiometer enabling variable amount control (e.g., agent's count or agent’s width/size), and an ultrasonic sensor that generates a reverse movement effect when the user's hand is within 20 centimetres. The ultrasonic sensor also captures screenshots when the user places their hand on it for approximately 2 seconds. An LED strip acts as an output, lighting up an appropriate colour when the user is choosing a colour, making the process more immersive. My objective was to create a generative art project that is fun, gives users creative freedom, and employs a variety of meaningful inputs and outputs.

Main technologies used:

HTML/CSS JavaScript p5.js Arduino

Own implementation of Spherical K-Means and expectation-maximisation (EM) algorithms. The EM algorithm can fit a finite mixture of von Mises-Fishers unsupervisedly. The vMF distribution was used as it allows for better modelling of datasets with very large dimensions - as directional vectors on a hypersphere rather than points in a Euclidean space.

Tested on two very different datasets:
dataset 1: a small-scale dataset with 4-dimensional features; the dataset has no labels provided
dataset 2: a large-scale dataset with 3000-dimensional features; correct labels (the last column) are given to measure the clustering performance.

Main technologies used:

Python NumPy Pandas SciPy