Advancing admixture graph estimation via maximum likelihood network orientation

MotivationAdmixture, the interbreeding between previously distinct populations, is a pervasive force in evolution. The evolutionary history of populations in the presence of admixture can be modeled by augmenting phylogenetic trees with additional nodes that represent admixture events. While enabling a more faithful representation of evolutionary history, admixture graphs present formidable inferential challenges. A key challenge is the need for admixture graph inference algorithms that are accurate while being completely automated and computationally efficient. Given the challenge of exhaustively evaluating all topologies, search heuristics have been developed to enable efficient inference. One heuristic, implemented in the popular method TreeMix, consists of adding admixture edges to an initial tree while optimizing a suitable objective function.ResultsHere, we present a demographic model (with one admixed population incident to a leaf) where TreeMix and any other starting-tree-based maximum likelihood heuristic using its likelihood function is guaranteed to get stuck in a local optimum and return the incorrect network topology. To address this issue, we propose a new search strategy based on reorientating the admixture graph that we term the maximum likelihood network orientation (MLNO) problem. We augment TreeMix with an exhaustive search for MLNO, referred to as OrientAGraph. In evaluations using previously published admixture graphs, OrientAGraph outperforms TreeMix on 4/8 models (there are no differences in the other cases). Overall, OrientAGraph finds graphs with higher likelihood scores and topological accuracy while remaining computationally efficient. Lastly, our study reveals important directions for improving maximum likelihood admixture graph estimation.AvailabilityOrientAGraph is available on Github (https://github.coin/ekinolloy/OrientAGraph) under the GNU General Public License v3.0.

Network Analysis of Physical and Psychiatric Symptoms of Hospital Discharged Patients Infected with COVID-19

In the current study, we aimed to investigate the network structure of COVID-19 symptoms and its related psychiatric symptoms, using a network approach. Specifically, we examined how COVID-19 symptoms relate to psychiatric symptoms and highlighted potential pathways between COVID-19 severity and psychiatric symptoms. With a sample of six hundred seventy-five recovered COVID-19 patients recruited 1 month after hospital discharge, we respectively integrated COVID-19 symptoms with PTSD, depression, and anxiety symptoms and analyzed the three network structures. In all three networks, COVID-19 severity and ICU admission are not linked directly to COVID-19 symptoms after hospitalization, while COVID-19 severity (but not ICU admission) is linked directly to one or more psychiatric symptoms. Specific pathways between COVID-19 symptoms and psychiatric symptoms were discussed. Finally, we used directed acyclic graph estimation to show potential causal effects between COVID-19 related variables and demographic characteristics. Keywords: COVID-19; symptom network; anxiety, depression, PTSD

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the further processes applied on the graph (e.g., LP, classification). As our knowledge of data is limited, a single approximation cannot easily find the appropriate graph, so in line with this, multiple graphs are constructed. Recently, multi-metric fusion techniques have been used to construct more accurate graphs which better represent the data manifold and, hence, improve the performance of LP. However, most of these algorithms disregard use of the information of label space in the LP process. In this article, we propose a new multi-metric graph-fusion method, based on the Flexible Manifold Embedding algorithm. Our proposed method represents a unified framework that merges two phases: graph fusion and LP. Based on one available view, different simple graphs were efficiently generated and used as input to our proposed fusion approach. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. Our experimental results on four face datasets in face recognition demonstrated the superiority of the proposed method compared to other state-of-the-art algorithms.