Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

Predicting Protein Molecular Function

Predicting Protein Molecular Function by Barbara Elizabeth Engelhardt Doctor of Philosophy in Computer Science and the Designated Emphases in Computational and Genomic Biology and Communication, Computation, and Statistics University of California, Berkeley Professor Michael I. Jordan, Chair The number of known nucleotide sequences encoding proteins is growing at an extraordinarily fast rate du...

Predicting protein function from structure: unique structural features of proteases.

We have noted consistent structural similarities among unrelated proteases. In comparison with other proteins of similar size, proteases have smaller than average surface areas, smaller radii of gyration, and higher C(alpha) densities. These findings imply that proteases are, as a group, more tightly packed than other proteins. There are also notable differences in secondary structure content b...

Enhancing Neural Disfluency Detection with Hand-Crafted Features

In this paper, we apply a bidirectional Long Short-Term Memory with a Conditional Random Field to the task of disfluency detection. Long-range dependencies is one of the core problems for disfluency detection. Our model handles long-range dependencies by both using the Long Short-Term Memory and hand-crafted discrete features. Experiments show that utilizing the hand-crafted discrete features s...

Boosting Hand-Crafted Features for Curvilinear Structure Segmentation by Learning Context Filters

Human Action Recognition by Random Features and Hand-Crafted Features: A Comparative Study

One popular approach for human action recognition is to extract features from videos as representations, subsequently followed by a classification procedure of the representations. In this paper, we investigate and compare hand-crafted and random feature representation for human action recognition on YouTube dataset. The former is built on 3D HoG/HoF and SIFT descriptors while the latter bases ...