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Found 8 papers, 2 papers with code
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations
We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset.
DeepDeath: Learning to Predict the Underlying Cause of Death with Big Data
Multiple cause-of-death data provides a valuable source of information that can be used to enhance health standards by predicting health related trajectories in societies with large populations.
MotifMark: Finding Regulatory Motifs in DNA Sequences
A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs.
Fuzzy Constraints Linear Discriminant Analysis
In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA).
A New Type-II Fuzzy Logic Based Controller for Non-linear Dynamical Systems with Application to a 3-PSP Parallel Robot
More specifically, it is intended to incorporate the Type-II Fuzzy Logic paradigm into a model based controller, the so-called computed torque control method, and apply the result to a 3 degrees of freedom parallel manipulator.
DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins
To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.
A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival
Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles.
A Semi-Supervised Method for Predicting Cancer Survival Using Incomplete Clinical Data
The results of applying our method to three cancer datasets show the promise of semi-supervised learning for prediction of cancer survival.
