Benchmarking network propagation methods for disease gene identification

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version

Adult-Onset Alexander Disease Uncovered in A Previously Healthy Patient Presenting with Acute Stroke-like Symptoms

Alexander Disease is a rare, often fatal, leukoencephalopathy of early childhood associated with a heterozygous mutation of the glial fibrillary acid protein (GFAP) gene. Adult-Onset Alexander Disease (AOAD) is an exceptionally rare leukoencephalopathy that often presents with slowly progressive brainstem and cervical cord dysfunction features. Acute onset of AOAD has only ever been reported three times in the literature. We report a case of acute onset AOAD in a patient that presented with bulbar symptoms and left hemiplegia initially concerning for acute stroke

Generalized No-Broadcasting Theorem

We prove a generalized version of the no-broadcasting theorem, applicable to essentially any nonclassical finite-dimensional probabilistic model satisfying a no-signaling criterion, including ones with ‘‘superquantum’’ correlations. A strengthened version of the quantum no-broadcasting theorem follows, and its proof is significantly simpler than existing proofs of the no-broadcasting theorem

Improved bounds for embedding certain configurations in subsets of vector spaces over finite fields

The fourth listed author and Hans Parshall (\cite{IosevichParshall}) proved that if $E \subset {\mathbb F}_q^d$, $d \ge 2$, and $G$ is a connected graph on $k+1$ vertices such that the largest degree of any vertex is $m$, then if $|E| \ge C q^{m+\frac{d-1}{2}}$, for any $t>0$, there exist $k+1$ points $x^1, \dots, x^{k+1}$ in $E$ such that $||x^i-x^j||=t$ if the $i$'th vertex is connected to the $j$'th vertex by an edge in $G$. In this paper, we give several indications that the maximum degree is not always the right notion of complexity and prove several concrete results to obtain better exponents than the Iosevich-Parshall result affords. This can be viewed as a step towards understanding the right notion of complexity for graph embeddings in subsets of vector spaces over finite fields

Reusability Studies for Ares I and Ares V Propulsion

With a mission to continue to support the goals of the International Space Station (ISS) and explore beyond Earth orbit, the United States National Aeronautics and Space Administration (NASA) is in the process of launching an entirely new space exploration initiative, the Constellation Program. Even as the Space Shuttle moves toward its final voyage, Constellation is building from nearly half a century of NASA spaceflight experience, and technological advances, including the legacy of Shuttle and earlier programs such as Apollo and the Saturn V rocket. Out of Constellation will come two new launch vehicles: the Ares I crew launch vehicle and the Ares V cargo launch vehicle. With the initial goal to seamlessly continue where the Space Shuttle leaves off, Ares will firstly service the Space Station. Ultimately, however, the intent is to push further: to establish an outpost on the Moon, and then to explore other destinations. With significant experience and a strong foundation in aerospace, NASA is now progressing toward the final design of the First Stage propulsion system for the Ares I. The new launch vehicle design will considerably increase safety and reliability, reduce the cost of accessing space, and provide a viable growth path for human space exploration. To achieve these goals, NASA is taking advantage of Space Shuttle hardware, safety, reliability, and experience. With efforts to minimize technical risk and life-cycle costs, the First Stage office is again pulling from NASA s strong legacy in aerospace exploration and development, most specifically the Space Shuttle Program. Trade studies have been conducted to evaluate life-cycle costs, expendability, and risk reduction. While many first stage features have already been determined, these trade studies are helping to resolve the operational requisites and configuration of the first stage element. This paper first presents an overview of the Ares missions and the genesis of the Ares vehicle design. It then looks at one of the most important trade studies to date, the "Ares I First Stage Expendability Trade Study." The purpose of this study was to determine the utility of flying the first stage as an expendable booster rather than making it reusable. To lower the study complexity, four operational scenarios (or cases) were defined. This assessment then included an evaluation of the development, reliability, performance, and transition impacts associated with an expendable solution. This paper looks at these scenarios from the perspectives of cost, reliability, and performance

A comparison of Pfam and MEROPS: Two databases, one comprehensive, and one specialised.

BACKGROUND: We wished to compare two databases based on sequence similarity: one that aims to be comprehensive in its coverage of known sequences, and one that specialises in a relatively small subset of known sequences. One of the motivations behind this study was quality control. Pfam is a comprehensive collection of alignments and hidden Markov models representing families of proteins and domains. MEROPS is a catalogue and classification of enzymes with proteolytic activity (peptidases or proteases). These secondary databases are used by researchers worldwide, yet their contents are not peer reviewed. Therefore, we hoped that a systematic comparison of the contents of Pfam and MEROPS would highlight missing members and false-positives leading to improvements in quality of both databases. An additional reason for carrying out this study was to explore the extent of consensus in the definition of a protein family. RESULTS: About half (89 out of 174) of the peptidase families in MEROPS overlapped single Pfam families. A further 32 MEROPS families overlapped multiple Pfam families. Where possible, new Pfam families were built to represent most of the MEROPS families that did not overlap Pfam. When comparing the numbers of sequences found in the overlap between a MEROPS family and its corresponding Pfam family, in most cases the overlap was substantial (52 pairs of MEROPS and Pfam families had an intersection size of greater than 75% of the union) but there were some differences in the sets of sequences included in the MEROPS families versus the overlapping Pfam families. CONCLUSIONS: A number of the discrepancies between MEROPS families and their corresponding Pfam families arose from differences in the aims and philosophies of the two databases. Examination of some of the discrepancies highlighted additional members of families, which have subsequently been added in both Pfam and MEROPS. This has led to improvements in the quality of both databases. Overall there was a great deal of consensus between the databases in definitions of a protein family

Phase transition energetics in mesoscale photosynthetic condensates

The pyrenoid is a model two-component biomolecular condensate, vital for efficient photosynthesis in algae. Despite simulations predicting qualitative features of liquid-liquid phase separation driving their formation, the underlying energetics remain unclear. By modelling interactions between Rubisco protein carbon-capturing machinery inside pyrenoids as linker chemical and stretch potentials we explain spectroscopic and single-molecule data over physiological concentrations. This new parametrisation can be used for quantitative predictions in generalized emergent self-assembly of two-component condensates.Comment: v2: correction in the calculations v3: added experimental wor

Pre-hospital notification is associated with improved stroke thrombolysis timing

Peer reviewedPublisher PD

Predicting Suicidal and Self-Injurious Events in a Correctional Setting Using AI Algorithms on Unstructured Medical Notes and Structured Data

Suicidal and self-injurious incidents in correctional settings deplete the institutional and healthcare resources, create disorder and stress for staff and other inmates. Traditional statistical analyses provide some guidance, but they can only be applied to structured data that are often difficult to collect and their recommendations are often expensive to act upon. This study aims to extract information from medical and mental health progress notes using AI algorithms to make actionable predictions of suicidal and self-injurious events to improve the efficiency of triage for health care services and prevent suicidal and injurious events from happening at California\u27s Orange County Jails. The results showed that the notes data contain more information with respect to suicidal or injurious behaviors than the structured data available in the EHR database at the Orange County Jails. Using the notes data alone (under-sampled to 50%) in a Transformer Encoder model produced an AUC-ROC of 0.862, a Sensitivity of 0.816, and a Specificity of 0.738. Incorporating the information extracted from the notes data into traditional Machine Learning models as a feature alongside structured data (under-sampled to 50%) yielded better performance in terms of Sensitivity (AUC-ROC: 0.77, Sensitivity: 0.89, Specificity: 0.65). In addition, under-sampling is an effective approach to mitigating the impact of the extremely imbalanced classes