Predicting morbidity by local similarities in multi-scale patient trajectories

[EN] Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.This work was supported by the CrowdHealth project (COLLECTIVE WISDOM DRIVING PUBLIC HEALTH POLICIES (727560)) and the MTS4up project (DPI2016-80054-R).Carrasco-Ribelles, LA.; Pardo-Más, JR.; Tortajada, S.; Sáez Silvestre, C.; Valdivieso, B.; Garcia-Gomez, JM. (2021). Predicting morbidity by local similarities in multi-scale patient trajectories. Journal of Biomedical Informatics. 120:1-9. https://doi.org/10.1016/j.jbi.2021.103837S1912

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Background and objectives. The computational biology area is growing up over the years. The interest in researching and developing computational tools for the acquisition, storage, organization, analysis, and visualization of biological data generates the need to create new hardware architectures and new software tools that allow processing big data in acceptable times. In this sense, heterogeneous computing takes an important role in providing solutions but at the same time generates new challenges for developers in relation to the impossibility of porting source code between different architectures. Methods. Intel has recently introduced oneAPI, a new unified programming environment that allows code developed in the SYCL-based Data Parallel C++ (DPC++) language to be run on different devices such as CPUs, GPUs, and FPGAs, among others. Due to the large amount of CUDA software in the field of bioinformatics, this paper presents the migration process of the SW\# suite, a biological sequence alignment tool developed in CUDA, to DPC++ through the oneAPI compatibility tool dpc (recently renowned as SYCLomatic). Results. SW\# has been completely migrated with a small programmer intervention in terms of hand-coding. Moreover, it has been possible to port the migrated code between different architectures (considering different target platforms and vendors), with no noticeable performance degradation. Conclusions. The SYCLomatic tool presented a great performance-portability rate. SYCL and Intel oneAPI can offer attractive opportunities for the Bioinformatics community, especially considering the vast existence of CUDA-based legacy codes

Eye movements in code reading:relaxing the linear order

Abstract—Code reading is an important skill in programming. Inspired by the linearity that people exhibit while natural lan-guage text reading, we designed local and global gaze-based mea-sures to characterize linearity (left-to-right and top-to-bottom) in reading source code. Unlike natural language text, source code is executable and requires a specific reading approach. To validate these measures, we compared the eye movements of novice and expert programmers who were asked to read and comprehend short snippets of natural language text and Java programs. Our results show that novices read source code less linearly than natural language text. Moreover, experts read code less linearly than novices. These findings indicate that there are specific differences between reading natural language and source code, and suggest that non-linear reading skills increase with expertise. We discuss the implications for practitioners and educators. I

Bioinformatics for High-throughput Virus Detection and Discovery

Pathogen detection is a challenging problem given that any given specimen may contain one or more of many different microbes. Additionally, a specimen may contain microbes that have yet to be discovered. Traditional diagnostics are ill-equipped to address these challenges because they are focused on the detection of a single agent or panel of agents. I have developed three innovative computational approaches for analyzing high-throughput genomic assays capable of detecting many microbes in a parallel and unbiased fashion. The first is a metagenomic sequence analysis pipeline that was initially applied to 12 pediatric diarrhea specimens in order to give the first ever look at the diarrhea virome. Metagenomic sequencing and subsequent analysis revealed a spectrum of viruses in these specimens including known and highly divergent viruses. This metagenomic survey serves as a basis for future investigations about the possible role of these viruses in disease. The second tool I developed is a novel algorithm for diagnostic microarray analysis called VIPR: Viral Identification with a PRobabilistic algorithm). The main advantage of VIPR relative to other published methods for diagnostic microarray analysis is that it relies on a training set of empirical hybridizations of known viruses to guide future predictions. VIPR uses a Bayesian statistical framework in order to accomplish this. A set of hemorrhagic fever viruses and their relatives were hybridized to a total of 110 microarrays in order to test the performance of VIPR. VIPR achieved an accuracy of 94% and outperformed existing approaches for this dataset. The third tool I developed for pathogen detection is called VIPR HMM. VIPR HMM expands upon VIPR\u27s previous implementation by incorporating a hidden Markov model: HMM) in order to detect recombinant viruses. VIPR HMM correctly identified 95% of inter-species breakpoints for a set of recombinant alphaviruses and flaviviruses Mass sequencing and diagnostic microarrays require robust computational tools in order to make predictions regarding the presence of microbes in specimens of interest. High-throughput diagnostic assays coupled with powerful analysis tools have the potential to increase the efficacy with which we detect pathogens and treat disease as these technologies play more prominent roles in clinical laboratories

Visual Molecular Dynamics Investigations of the Impact of Hydrophobic Nanoparticles on Prognosis of Alzheimer’s Disease and Cancers

The possible impact of hydrophobic lectin nanoparticles on the prognosis and progression of Alzheimer's disease (AD) and cancers was investigated by Visual Molecular Dynamics (VMD) computer modeling programs available from the Beckmann Advanced Research Institute at the University of Illinois at Urbana. Our results indicate the possibility of impeding pathological aggregation of certain proteins such as modified tau- or beta-amyloid that are currently being considered as possible causes of Alzheimer's disease. VMD programs serve as useful tools for investigation hydrophobic protein aggregation that may play a role in aging of human populations

Trace alignment in process mining: Opportunities for process diagnostics

Abstract. Process mining techniques attempt to extract non-trivial knowledge and interesting insights from event logs. Process mining provides a welcome extension of the repertoire of business process analysis techniques and has been adopted in various commercial BPM systems (BPM|one, Futura Reflect, ARIS PPM, Fujitsu, etc.). Unfortunately, traditional process discovery algorithms have problems dealing with lessstructured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in a way that event logs can be explored easily. Trace alignment can be used in a preprocessing phase where the event log is investigated or filtered and in later phases where detailed questions need to be answered. Hence, it complements existing process mining techniques focusing on discovery and conformance checking

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs