Neural Models for Generating Natural Language Summaries from Temporal Personal Health Data.

With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient  intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language.

Toward Subgraph-Guided Knowledge Graph Question Generation With Graph Neural Networks.

Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting that is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.

Modern Hopfield Networks for graph embedding.

The network embedding task is to represent a node in a network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding method from a new perspective, which leverages Modern Hopfield Networks (MHN) for associative learning.

Unmasking Fracture Risk in Type 2 Diabetes: The Association of Longitudinal Glycemic Hemoglobin Level and Medications.

Context: Fracture risk is underestimated in people with type 2 diabetes (T2D).

Objective: To investigate the longitudinal relationship of glycated hemoglobin (HbA1c) and common medications on fracture risk in people with T2D.

Methods: This retrospective population-based cohort study was conducted using de-identified claims and electronic health record data obtained from the OptumLabs Data Warehouse for the period January 1, 2007, to September 30, 2015.

NSIT: novel sequence identification tool.

Novel sequences are DNA sequences present in an individual's genome but absent in the human reference assembly. They are predicted to be biologically important, both individual and population specific, and consistent with the known human migration paths. Recent works have shown that an average person harbors 2-5 Mb of such sequences and estimated that the human pan-genome contains as high as 19-40 Mb of novel sequences.

FlexSnap: flexible non-sequential protein structure alignment.

Background: Proteins have evolved subject to energetic selection pressure for stability and flexibility. Structural similarity between proteins that have gone through conformational changes can be captured effectively if flexibility is considered. Topologically unrelated proteins that preserve secondary structure packing interactions can be detected if both flexibility and Sequential permutations are considered.

Proteomic and phospho-proteomic profile of human platelets in basal, resting state: insights into integrin signaling.

During atherogenesis and vascular inflammation quiescent platelets are activated to increase the surface expression and ligand affinity of the integrin alphaIIbbeta3 via inside-out signaling. Diverse signals such as thrombin, ADP and epinephrine transduce signals through their respective GPCRs to activate protein kinases that ultimately lead to the phosphorylation of the cytoplasmic tail of the integrin alphaIIbbeta3 and augment its function. The signaling pathways that transmit signals from the GPCR to the cytosolic domain of the integrin are not well defined.

Iterative non-sequential protein structural alignment.

Structural similarity between proteins gives us insights into their evolutionary relationships when there is low sequence similarity. In this paper, we present a novel approach called SNAP for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process consisting of a superposition step and an alignment step, until convergence.

TRELLIS+: an effective approach for indexing genome-scale sequences using suffix trees.

Unlabelled: With advances in high-throughput sequencing methods, and the corresponding exponential growth in sequence data, it has become critical to develop scalable data management techniques for sequence storage, retrieval and analysis. In this paper we present a novel disk-based suffix tree approach, called TRELLIS+, that effectively scales to massive amount of sequence data using only a limited amount of main-memory, based on a novel string buffering strategy. We show experimentally that TRELLIS+ outperforms existing suffix tree approaches; it is able to index genome-scale sequences (e.

Iterative non-sequential protein structural alignment.

Structural similarity between proteins gives us insights on the evolutionary relationship between proteins which have low sequence similarity. In this paper, we present a novel approach called STSA for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process, a superposition step and an alignment step, until convergence.

Indexing protein structures using suffix trees.

Approaches for indexing proteins and fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this chapter, we describe a new method for extracting the local feature vectors of protein structures. Each residue is represented by a triangle, and the correlation between a set of residues is described by the distances between Calpha atoms and the angles between the normals of planes in which the triangles lie.

Context shapes: Efficient complementary shape matching for protein-protein docking.

We describe an efficient method for partial complementary shape matching for use in rigid protein-protein docking. The local shape features of a protein are represented using boolean data structures called Context Shapes. The relative orientations of the receptor and ligand surfaces are searched using precalculated lookup tables.

SMOTIF: efficient structured pattern and profile motif search.

Background: A structured motif allows variable length gaps between several components, where each component is a simple motif, which allows either no gaps or only fixed length gaps. The motif can either be represented as a pattern or a profile (also called positional weight matrix). We propose an efficient algorithm, called SMOTIF, to solve the structured motif search problem, i.

EXMOTIF: efficient structured motif extraction.

Background: Extracting motifs from sequences is a mainstay of bioinformatics. We look at the problem of mining structured motifs, which allow variable length gaps between simple motif components. We propose an efficient algorithm, called EXMOTIF, that given some sequence(s), and a structured motif template, extracts all frequent structured motifs that have quorum q.

PSIST: indexing protein structures using suffix trees.

Approaches for indexing proteins, and for fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this paper, we developed a new method for extracting the local feature vectors of protein structures. Each residue is represented by a triangle, and the correlation between a set of residues is described by the distances between Calpha atoms and the angles between the normals of planes in which the triangles lie.

Predicting protein folding pathways.

A structured folding pathway, which is a time ordered sequence of folding events, plays an important role in the protein folding process and hence, in the conformational search. Pathway prediction, thus gives more insight into the folding process and is a valuable guiding tool to search the conformation space. In this paper, we propose a novel 'unfolding' approach to predict the folding pathway.