Bio+Health A.I.

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Biological and medical fields generate vast data, from genetic sequences to clinical images. The theme “Bio+Health A.I.: From Species to Patients” explores how artificial intelligence can analyze, predict, and support decisions across scales of life—from monitoring ecosystems to improving patient care.

Applications range from biodiversity conservation and bioinformatics to diagnostics and personalized medicine. By working on projects in this theme, students see how A.I. can help protect species, accelerate discoveries, and improve health outcomes, while also grappling with the ethical responsibilities of working with sensitive data.

Biology:

FLORES, Argem Gerald R., BSCS 2012
MicroWebPh: An online repository, management and retrieval system for information on microorganisms
GONZALES, Anne Muriel V., BSCS 2015
Automatically identifying bird species based on vocalization: Probabilistic neural network vs. multilayer perceptron
MENDOZA, Arlene A., BSCS 2015
Probabilistic neural network and multilayer perceptron for dog breed identification based on bark vocalizations
VILLANUEVA, Marieann Jocel S., BSCS 2015
Identifying frog species based on vocalization: MLP vs. PNN

Health:

BONDAD, Rachelle G., BSCS 2013
A Practical comparison among neural networks, Bayesian networks, and collaborative filtering in classifying diabetis mellitus patients
CRUZABRA, Rommel Andrew C., BSCS 2009
A system for automated clinical diagnosis of breast cancer from mammograms
HERNANDEZ, Danielle M., BSCS (UP Cebu) 2019
The Cell Lab
MAGNO, Katrina Joy H., MIT 2013
An automated system for anthropometry
MANIPOL, Kristine O., BSCS 2003
Online dietary calculations for Filipinos
PAMA, Jose Arniel, BSCS (UP Cebu) 2019 cum laude
A sign language recognition system using LSTM
PELAEZ, Kristine Bernadette P., BSCS 2014 cum laude
Digital anthropometry for lower body garments
TALEGON, Mongilbert D.S., BSCS 2013
An interactive tool for gel electrophoresis analysis of DNA

Students are encouraged to contribute to this theme by developing applications that harness A.I. for biology, medicine, or the intersections between them. Whether one is drawn to analyzing DNA sequences, designing clinical decision-support systems, or creating models that predict disease outbreaks, the work can add to a growing body of knowledge that saves species and saves lives. By joining this theme, the student takes part in shaping an A.I. that works in service of both ecological balance and human health.