Function SIG Meetings:

Function-SIG 2017 will be held on July 24-25, 2017 as part of ISMB/ECCB 2017.

Function-SIG 2017 Keynote Speakers

Hagit Shatkay, University of Delaware

Talk Title: Tell Us where You are Going: Pondering Literature, Locations, Life and Proteins.

Speaker Bio: Hagit Shatkay is an Associate Professor and Director of the Computational Biomedicine and Machine Learning Lab at the Dept. of Computer and Information Sciences, University of Delaware, with cross-appointments at the Dept. of Biomedical Engineering, and the Delaware Biotechnology Institute. Prior to joining the University of Delaware (2010) she was an Associate Professor and Director of the Computational Biology and Machine Learning Lab at Queen’s University, Kingston, Ontario. Before moving to academia, she was an Informatics Research Scientist with the Informatics Research group of Celera Genomics, and a post-doctoral fellow at NCBI.
She holds a PhD in Computer Science from Brown University, and an MSc and BSc in Computer Science from the Hebrew University of Jerusalem.

Her research aims to develop and to employ machine learning and data-mining methods for addressing data-intensive problems in biology and medicine. She has been among the first, leading researchers in the area of biological text mining, working in the area for about 20 years now, and has co-authored (with Mark Craven) the book “Mining the Biomedical Literature” (MIT Press, 2012). She also works along with her colleagues and graduate students toward developing computational tools that can utilize data from diverse sources, including sequence, image and time-series data, in order to gain better understanding of protein location and function, and to better identify/predict certain medical conditions and disease.

John Moult, University of Maryland

Talk Title: Community Driven Critical Assessment: how well does it work, what have we learned, and what next?

Speaker Bio: John Moult is a Fellow of the Institute for Bioscience and Biotechnology Research at the University of Maryland and Professor in the Department of Cell Biology and Molecular Genetics. He is co-founder and chair of CASP (Critical Assessment of Protein Structure Prediction), an organization that conducts large-scale experiments in protein structure modeling, and co-founder and co-chair of CAGI, which conducts community wide experiments to advance genome interpretation. His research interests include the relationship between genetic variation and human disease, computer modeling of protein structure and function, and large scale community experiments.

Important Dates:

  • Wednesday, January 25, 2017: Call for Abstracts Opens
  • Thursday, April 13, 2017: Abstracts Submission Deadline
  • Wednesday, May 10, 2017: Abstracts Acceptance Notification
  • July 24-25, 2017: Function SIG meeting at ISMB 2017

Talks are sought in, but not limited to, the following topics:

  • Sequence, structure, function connection in proteins
  • Prediction of protein function
  • Evolution of function
  • Biocuration
  • Assessment of function prediction methods
  • Research related to the Critical Assessment of Function Annotation, or CAFA challenge

Programs from previous years

2017 Meeting Program

Time Session: Chair Title Speaker PDF
Day 1, July 24
Session 1: Predrag Radivojac
10:00-10:10 Welcome
10:10-10:30 Talk 1 The landscape of microbial phenotypic traits and associated genes Maria Brbic abstract
10:30-10:50 Talk 2 fusionDB: assessing microbial diversity and environmental preference via functional similarity networks Yannick Mahlich abstract
10:50-11:10 Talk 3 Region-specific Function Prediction: automatically inferring function labels for protein regions Emily Koo abstract
11:10-11:30 Talk 4 Investigation of Multi-task Deep Neural Networks in Automated Protein Function Prediction Ahmet Süreyya Rifaioğlu abstract
11:30-11:40 Break*
Session 2: Mark Wass
11:40-12:00 Talk 5 Determining Rewiring Functional Effects of Alternative Splicing Variants on Protein-Protein Interactions Dmitry Korkin abstract
12:00-12:30 Keynote Community Driven Critical Assessment: how well does it work, what have we learned, and what next? John Moult
12:30-14:00 Lunch
Session 3: Iddo Friedberg
14:00-14:20 Talk 6 Reasoning on Gene Ontology Networks Predicts Novel Protein Annotations Ilya Novikov abstract
14:20-14:40 Talk 7 Predicting Protein Function Directly from STRING Network Topology using Deep Learning Techniques Cen Wan abstract
14:40-15:00 Talk 8 ISMB/ECCB  Proceedings: Orthologous Matrix (OMA) algorithm 2.0: more robust to asymmetric evolutionary rates and more scalable hierarchical orthologous group inference Christophe Dessimoz Proceedings
15:00-15:10 Break*
15:10-15:30 Talk 9 All-to-all spectra comparisons within minutes for peptides identification in tandem mass spectrometry Matthieu David abstract
15:30-15:50 Talk 10 Comparing residue-coevolution networks across protein families Cristina Marino-Buslje abstract
16:00-16:30 Coffee break
Session 4: Sean Mooney
16:30-16:50 Talk 11 Automatic Generation of Functional Annotation Rules Using Inferred GO-Domain Associations Seyed Ziaeddin Alborzi abstract
16:50-17:10 Talk 12 Protein Function Prediction by COFACTOR in CAFA3 Peter Freddolino abstract
17:10-17:40 Lightning talks A Domain-Based Machine Learning Approach for Function Prediction using CATH FunFams Jonathan Lees abstract
Computational Functional Annotation: The Predictive Power of Different Data Sources Itamar Borukhov abstract
Predicting protein functions from sequence using a neuro-symbolic deep learning model Maxat Kulmanov abstract
Crowdsourcing Protein Family Database Curation Matt Jeffryes abstract
17:40-18:00 Talk 13 Proteome-wide chemical-genetic interaction map in human cells reveals drug mechanisms and novel gene functions Jasmin Coulombe-Huntington abstract
18:00-19:00 Posters
Day 2, July 25
Session 5: Casey Greene
8:30-8:40 Welcome
8:40-9:10 Keynote Tell Us where You are Going: Pondering Literature, Locations, Life and Proteins. Hagit Shatkay
9:10-9:30 Talk 14 ISMB/ECCB Proceedings: DextMP: Deep dive into Text for predicting Moonlighting Proteins Daisuke Kihara Proceedings
9:30-10:00 Break
Session 6
10:00-11:00 CAFA session CAFA3: lessons learned and prelimnary results Iddo Friedberg
11:00-11:20 Talk 15 AHRD: Using lexical analysis for the CAFA3 challenge Florian Boecker
11:20-11:30 Break*
Session 7: Claire O’Donovan
11:30-11:50 Talk 16 GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank Shanfeng Zhu
11:50-12:10 Talk 17 Phylogenetic- based gene function prediction in the Gene Ontology Consortium Huaiyu Mi abstract
12:10-12:30 4x lightning talks A Self-training Approach for Functional Annotation of UniProtKB Proteins Maryam Abdollahyan abstract
BAR 3.0: going beyond protein function annotation Giuseppe Profiti abstract
PANNZER 2: Annotate a complete proteome in minutes! Alan Medlar abstract
Thinking outside the informatics box: Computed chemical properties for protein function annotation Mary Jo Ondrechen abstract
12:30-14:00 Lunch & posters
Session 8:
14:00-14:20 Talk 18 Artificial Dilution Series: A General Framework for Benchmarking Classifier Evaluation Metrics Petri Toronen abstract
14:20-14:40 Talk 19 Elucidating the Function Space of Proteins Defined by Ontologies Yuxiang Jiang
14:40-15:00 Talk 20 Label-Space Dimensionality Reduction and a Similarity-Based Representation for Protein Function Prediction Stavros Makrodimitris abstract
15:00-15:20 Talk 21 Predicting Novel Abnormal Circadian Phenotypes in Mouse John Williams abstract
15:20-15:30 Break*
15:30-15:50 Talk 22 Structure-based prediction of protein-peptide binding regions using Random Forest Ghazaleh Taherzadeh abstract
15:50-16:10 Talk 23 Automating Genomic Context Analysis with a Probabilistic Model of Protein Function and Relatedness Jeffrey Yunes abstract
16:10-16:30 Awards & close

The Function-SIG meeting is funded, in part, by awards DBI-1458359 and DBI-1458477 from the US National Science Foundation.
In the past, the Function-SIG meetings were funded, in part, by R13 HG006079 and R13 HG007807 from the US National Institute of Health, as well as US Department of Energy grant DE-SC0006807TDD.