tags: phenoscape, meeting
# Phenoscape Journal Club
1st Friday of the month, 2:30-3:30 eastern
## Connection info
US phone toll free: 877.853.5257, 855.880.1246
US phone not toll free: 669 900 6833, 646.558.8656
International #'s": https://renci.zoom.us/zoomconference?m=pZZ0jdZAKrL_L2SZsyjJ1rn04r0jrDVv
Meeting ID: 929 124 809
Papers available on [Paperpile](https://paperpile.com/shared/30ynCr)
## Sign up
Suggestions for next time:
- Ontological models for the domain
* Vogt (2019) Levels and building blocks—toward a domain granularity framework for the life sciences https://doi.org/10.1186/s13326-019-0196-2
* Tang et al (2019) TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations. https://academic.oup.com/bioinformatics/article/35/3/518/5056037
- Reasoning, semantic similarity
* Smaili et al (2018) Formal axioms in biomedical ontologies improve analysis and interpretation of associated data. https://www.biorxiv.org/content/10.1101/536649v1
* Kulmanov & Höhndorf (2017) Evaluating the effect of annotation size on measures of semantic similarity. Journal of Biomedical Semanticsvolume 8:7 https://doi.org/10.1186/s13326-017-0119-z
- AI, Machine Learning, sub-symbolic reasoning
* Alshahrani et al (2017) Neuro-symbolic representation learning on biological knowledge graphs https://academic.oup.com/bioinformatics/article/33/17/2723/3760100
* Smaili et al (2019) OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction, Bioinformatics, Volume 35, Issue 12, June 2019, Pages 2133–2140, https://doi.org/10.1093/bioinformatics/bty933
* Smaili et al (2018) Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations, Bioinformatics, Volume 34, Issue 13, 01 July 2018, Pages i52–i60, https://doi.org/10.1093/bioinformatics/bty259
* May 1 - Predicting candidate genes from phenotypes, functions, and anatomical site of expression. Jun Chen, Azza Althagafi, Robert Hoehndorf https://doi.org/10.1101/2020.03.30.015594
* April 3 - A. Martin (NIH proposal) The loci of evolution: integrating knowledge about the genomic targets of phenotypic variation across Eukaryotes. Posted to Slack, with reviews.
* March 6 - Diego - Lewis et al (2016) Estimating Bayesian Phylogenetic Information Content. Systematic Biology, 65, 1009. https://doi.org/10.1093/sysbio/syw042
* Diego interested in using the measure of Dissonance (D) proposed here
* Hypothesis: D will be 'correlated' with semantic distance
* Problem: method only scales to ~20 taxa because the clades have to be enumerated
* Diego interested in measuring these stats not over probability of trees but over probability of change on a branch (eg from a stochastic map) - as a way to detect clusters of characters changing at similar places on the tree
* December 6 -
* November 1 - Jim - Kulmanov et al (2019) EL Embeddings: Geometric construction of models for the Description Logic EL++
* October 4 - postponed
* September 6 - Todd - Braun IR, Lawrence-Dill CJ. Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction. https://www.biorxiv.org/content/10.1101/689976v1
* August 2 - Wasila - Eliason, Chad M., Scott V. Edwards, and Julia A. Clarke. "phenotools: An R package for visualizing and analyzing phenomic datasets." Methods in Ecology and Evolution.
* July 5 - Hilmar - Ebrahami et al. (2018) Reasoning over RDF Knowledge Bases using Deep Learning https://arxiv.org/abs/1811.04132
* May 3 - Josef - Ochoterena et al (2019) The search for common origin: homology revisited https://academic.oup.com/sysbio/advance-article/doi/10.1093/sysbio/syz013/5364027
* March 1 - Sergei - Tarasov et al. (preprint) PARAMO pipeline: reconstructing ancestral anatomies using ontologies and stochastic mapping https://www.biorxiv.org/content/biorxiv/early/2019/02/18/553370.full.pdf
* Feb 1 - Wasila - Endara et al (2018) Modifier Ontologies for frequency, certainty, degree, and coverage phenotype modifier. Biodiversity Data Journal 6: e29232. https://doi.org/10.3897/BDJ.6.e29232
* Dec 7 - Pasan Fernando- creating a gene network from phenotypic similarity and module analysis
* Nov 2 - Jim - "Opposite-of"-information improves similarity calculations in phenotype ontologies. BioRxiv Preprint: http://dx.doi.org/10.1101/108977
* Oct 5 - [cancelled]
* Sept 21 - SCATE project meeting
* Sept 7 - [cancelled]
* Aug 17 - Paula - Primary reading: Vogt L (2018) [Towards a semantic approach to numerical tree inference in phylogenetics](https://doi.org/10.1111/cla.12195). Cladistics 34, 200–224; Optional: Vogt L (2017) [Assessing similarity: on homology, characters and the need for a semantic approach to non-evolutionary comparative homology](https://doi.org/10.1111/cla.12179). Cladistics 33 (2017) 513–539.
* Aug 3 - Wasila - Wirkner et al. (2017) The First Organ-Based Ontology for Arthropods (Ontology of Arthropod Circulatory Systems - OArCS) and its Integration into a Novel Formalization Scheme for Morphological Descriptions, Systematic Biology, Volume 66, Issue 5, 1 September 2017, Pages 754–768,https://doi.org/10.1093/sysbio/syw108
* July 20 - Todd - Tarasov S (preprint) Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits. https://doi.org/10.1101/188672
* July 6 - skip - [Paula, Wasila gone]
* June 15 - [cancelled, moved to Aug 17]
* June 1 - skip
### March 6, 2020
* Lewis et al (2016) Estimating Bayesian
* Entropy: measure of uncertainty in a system; here, compare prior and posterior distributions
* measure how much conflict there is in partitions of the data (gene tree example in paper)
* run analysis with subsets of data, prior is the same, get posterior distribution for each partition
* if those partitions agree, will have similar posterior distributions and entropy will be lower
* if not ...entropy will be higher
* size of partition matters for ?
* will dissonance positively correlate with semantic distance?
* lower dissonance between two clusters of characters with high semantic similariy
* caveats: limited by number of taxa; 20 taxa upper limit with taxa that have good coverage
* missing data: up to 50% might be OK
* having small matrix to maximize taxa and traits
### May 3, 2019
* Ochoterena et al (2019) The search for common origin: homology revisited https://academic.oup.com/sysbio/advance-article/doi/10.1093/sysbio/syz013/5364027
* What is their goal for establishing homology? Phylogenetic inference, defining clades, which may lead to some disconnect with our own concerns.
* Discussion of levels of organization (ontogenetic, population , species) and their proposal that homology must be concordant among them.
* Discussion of relevance of the idea of paralogy to morphological traits
* what is the appropriate state space for phenotypes? we are trying to code phenotypes in that space of genetic/developmental networks but we will never be able to do so perfectly.
* does xenology preclude homology? doesn't seem like it needs to.
* how does it relate to ontological issues?
### Mar 1, 2019
* Tarasov et al. preprint:
* extracting presence/absence dependencies should be in place in time for workshop
* description of how algorithm works should be documented where?
* currently the algorithm is in OWL
### Feb 1, 2019
* Endara et al 2018 https://doi.org/10.3897/BDJ.6.e29232
* Some discussion questions (from Todd)
* Will this help reasoning escape from the implicit assumption of a normal and abnormal phenotype?
* How should position along a list be incorporated into semantic similarity?
* What would be the arguments for the open world form of the modifier ontology?
* Have we been misusing PATO, etc to serve as modifiers in the absence of these classes?
### Vogt 2018
Some interesting points (very rough notes from Todd):
* Paula makes the observation that the debate over the merits of atomizing anatomy into characters echoes a debate w/ "Berkeley school" of comparative morphology from her graduate days
* Would be interesting to see seeing whether use of a whole instance graph would lead to different max parsimony reconstructions than individual analysis of each ontologized character.
* In Vogt's approach, what a matrix would list as different characters could, in principle, be interdependent in Vogt's analysis (and hopefully reflective of integration in the organism)
* Might be able to test Vogt's idea with 'monograph to matrix', Andy Dean's wasp, or Laura J's fish dataset.
* Does Vogt's proposal really escape the assumptions of primary homology in phylo. analysis, or does annotation with common ontology terms mean the same thing in practice?
* Similarly, is the ontology taking on some of the burden of character state delimitation in Vogt's approach (as opposed to escaping the necessity of that step)
* Some interesting parallels between Tarasov preprint (from previous jclub) & Vogt