Prediction of AL Amyloidosis Using Deep Learning
| dc.contributor.author | Murali, Anupa | |
| dc.date.accessioned | 2023-04-27T17:34:05Z | |
| dc.date.available | 2023-04-27T17:34:05Z | |
| dc.date.issued | 2023-04-27 | |
| dc.date.submitted | 2023-04-24 | |
| dc.description.abstract | AL amyloidosis (amyloid light chain or primary amyloidosis) is a rare protein disorder that can be potentially fatal or can cause permanent damage to the organs in the body, especially in cases where the diagnosis does not arrive early enough or where the treatment does not begin on time. It is a type of amyloidosis, which occurs when abnormal immunoglobulin light chain (LC) proteins in the body misfold and accumulate on the heart, the kidneys, and the other organs. In order to facilitate timely diagnosis of the disease before the symptoms start fully exhibiting themselves and before the damage to the organs becomes significant, we present a computational solution in this thesis, called "DALAD", which is based on (convolutional) deep learning networks and takes in an LC sequence from a patient as the input, and determines with high confidence whether the patient has the disease or not. We develop and test multiple versions of DALAD, which are characterized by the type of sequences they have been trained on and by the types of features they incorporate to make the predictions, in order to have high performance in each of these scenarios. We establish the following for DALAD. 1. DALAD is the first computational learning model to be able to accurately predict the onset of AL amyloidosis on both lambda and kappa LC sequences. 2. DALAD comfortably beats the state-of-the-art for lambda sequences in terms of accuracy measures, such as AUC score, sensitivity, and specificity. Our numbers for these three metrics are 0.89, 0.81, and 0.83, respectively, while for LICTOR, they are 0.87, 0.76, and 0.82, respectively. 3. DALAD is able to utilize the features from both V and J gene segments of the LC sequences to make more accurate predictions. We additionally show via the pairwise t-test that the J gene segments do improve our performance against both lambda and kappa sequences. 4. We provide aggregate statistics over multiple runs for each version of DALAD, along with the accuracy results for the best trained model corresponding to each version. All our findings indicate high prediction accuracy for both lambda and kappa sequences. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/19348 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.subject | bioinformatics | en |
| dc.subject | machine learning | en |
| dc.subject | convolutional neural network | en |
| dc.subject | deep neural network | en |
| dc.subject | al amyloidosis | en |
| dc.subject | amyloidosis | en |
| dc.subject | disease prediction | en |
| dc.subject | dalad | en |
| dc.title | Prediction of AL Amyloidosis Using Deep Learning | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Mathematics | en |
| uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
| uws-etd.degree.discipline | Computer Science | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws-etd.embargo.terms | 0 | en |
| uws.contributor.advisor | Ma, Bin | |
| uws.contributor.affiliation1 | Faculty of Mathematics | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |