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# System prepended metadata

title: GIW/BIOINFO 2017 Conference
tags: [shared, conference]

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# GIW/BIOINFO 2017 Conference
###### tags: `shared` `conference`
[TOC]

## GIW/BIOINFO 2017 in Seoul Paper presentations notes
## GIW/BIOINFO Day 1
### Tutorial 1: Machine Learning
+ Introduction to Sparse Coding in Genomics Data Analysis
    - Sparse coding to find biomarkers
    - First part of part I: introduction to ML, from definition (weighting, activation function) to mentioning high dimensional data (and thus sparse coding)
    - Idea of sparse coding: signs do not matter but the magnitudes matter => make some weights to be zero
    - Machine learning
        - Training data: parameter learning; validation data: hyper-parameter learning (for learning)
        - Testing data: for performance evaluation
        - Assumption: data points are iid
        - Main objective is the regularizer
        - Prediction models and data models
            - Prediction model: y <- f_w(x)
            - Data model: y=f_w(x) + epsilon, zero-mean Gaussian noise N(0, sigma^2)
            - Model space -> (x,y): w (sampling); (x,y) -> model space: w^hat (estimation)
            - Model space is also known as hypothesis space
    - Sparse coding
        - s-sparse if nnz(w) <= s (nnz == number of non-zero)
        - Estimation (sparse coding)
        - Least Absolute Shrinkage and Selection Operator  (LASSO)
        - Fused LASSO: D matrix as a penalty matrix
            - 1-D fused LASSO: find step-wise functions to find differentially-expressed data
            - 2-D fused LASSO: fMRI images
        - Dantzig selector 
        - Three forms of sparse coding
    - What are we looking for by LASSO?
        - Types of error
            - Estimation error: w* = w ?
            - Variable selection error: supp(w*) != supp(w), where supp(w):={i in {1, …, p}: w_i != 0}
            - Second type error often matters (what biomarkers you use)
            - How to choose the hyper-parameters? (figure)
            - CV has no guarantee on the variable selection error
        - Notable results for LASSO
        - LASSO: finite sample control 
            - LASSO selects correct variables even when s is very small
            - LASSO beings to include false variables even when s < n/(2log p)
    - R packages: glmnet, SLOPE
        - HIV dataset available: https://goo.gl/c6fCMx
        - Genotypic predictors of human immunodeficiency virus type 1 drug resistance, Soo-Yon Rhee et al., PNAS 2005
        - hiv.train$x  # 704 observations of 208 binary mutation variables
        - fit=glmnet(hiv.train$x, hiv.train$y)  # fit the model
        - plot(fit, xvar=“lambda”, main=“HIV model coefficient paths”)  # plot the paths for the fit
    - Why graphical models? (recent researches)
        - We are often interested in their relations
    - Gaussian Markov random field (Gaussian MRF, GMRF)
        - Correlation (local perspective) v.s. conditional correlation (global perspective)
        - Conditional correlation can be obtained by observing the interacting nodes 
        - Sparser graph -> more interpretable
        - GMRF optimization problem (can be solved in a reasonable time even if the number of genes is large)
        - Problem: the matrix is NOT sparse -> not a sparse graph
        - Grade 1 breast cancer (unsupervised: on used microarray data)
    - Choosing lambda
    - Biomarker discovery
        - Genetic data
        - Classical model: multiple hypothesis testing, calculating p-values for them
        - Banjamini-Hochberg v.s. Bonferroni
        - Multivariate hypothesis testing: combinatorial explosion but computationally expensive
            - MHT as model selection: X * beta -> y, where beta is a sparse vector
            - Model selection by sparse coding
            - Using SL1: SLOPE
            - fit.slpoe=SLPOE (hiv.train$x, hiv.train$y, fdr=0.2)
            - fit.slope
            - Biomarkers -> relations
    - On-going research

### GIW 1: Biological networks
+ BTNET: Boosted tree based gene regulatory network inference algorithm using time-course measurement data
    - Transcription factors - target genes
    - How to identify GRN
        - Correlation, information theoretic, dynamic bayesian network, tree-based ensemble
        - Problem: steady-state expression data but dynamic biological process
    - Adaboost: Focus on samples with poor predictions
    - Gradient boosting: Trained based on the error
    - Variable Important Score (VIS): contribute to the reduction of the variance of target value (i.e., target gene expression)
    - AUROC/AUPR as evaluation metrics
    - Future work: Web interface & dynamic inference
    - BTNET available on the network based on Python

+ SPSNet: Subpopulation-sensitive network-based analysis of heterogeneous gene expression data
    - Disease heterogeneity (lies at the molecular level)
    - Systematic biases enter in the form of batch effects
    - DE analysis at gene level -> pathway level -> sub-network level
    - Disease changes only certain part of the whole pathway (not the whole pathway)
    - Generating candidate subnetworks -> compute subnetwork scores -> determine statistical significance
    - Rank genes in the order of their expression -> fuzzy: top-10 (1) ~ bottom-10 (0)
    - Node & nodes within 5 steps -> pathway/subnetwork
    - Simulation methodology (SPSNet): N1 & N2 (artificially up/down-regulated) & subnetwork information
    - SPSNet can be used to recognize and eliminate extrinsic heterogeneity such as batch effect

+ Network-based identification of pathway-specific protein domains and their implication for human disease 
    - Network propagation
    - co-pathway protein network 
    - Pathway specificity
    - PPA score between protein-pathway
    - DPA score between domain-pathway

+ A homologous mapping  method for three-dimensional reconstruction of protein networks reveals disease-associated mutations
    - Network-based approach to cell behaviors and disease mechanism
    - PPI data is incomplete
    - High false positive rate
    - hDiSNet: human 3-dimensional structural PPI network
    - Validation: gene ontology (biological function) (biological process and cellular component)
    - Validation: gene co-expression profiles (gene expression profiles)
    - Mutations prefer occurring in the interacting domain
    - Method for predicting/construction of PPI network

+ Comorbidity Scoring with causal Disease Networks
    - Disease association network -> disease causality network
    - Main steps: Disease association -> disease causality (pathway & medical literatures) -> SSS^c algorithms
    - Proposed method
        - Settling directed edges (metabolic pathway & literatures)
        - Limitation to SSL for causality matrix: non-symmetry -> not good for Laplacian
    - Toy example: graph with(out) direction

### Keynote Speech 1
+ Studying Stem Cell Systems through multiple ‘omics data
    - Wnt: “wint signaling”; mesodermal; lineages
    - Biological sample: XXX-seq data
    - Integrated analysis -> SNP (“snip”)
    - Modeling Wnt signaling pathway
        - “Organizing the first 24 hours of life”
        - Rules of developmental signaling pathways: initiation -> progression -> termination
        - Terminator of Wint signaling
            - Highly included in response to Wnt
            - Transcription factor
                - Repressive domain
            - Acts globally
        - SP5 binds in promoter regions of many Wnt target genes - what if disabled ?
            - Wildtype (WT) v.s. disabled
            - SP5 reins in their response to Wnt
            - What makes it so selective?
    - Generation and characterization of a mesodermal progenitor population
        - RNA-seq reveals 2 key sets of genes
    - What organizes neural progenitor cells?
        - Wnt instructs A/P positional identity of NPCs
    - Challenge: biological -> computational
    - Question: Data driven analysis?
        - Haven’t done yet (presently, query-motivated)
        - Waited to be mined

### GIW 2: Sequence Analysis, Computational Methods
+ Introducing difference recurrence relations for faster semi-global alignment of long sequences
    - Genealogy of Alignment Algorithms -> Contribution: Difference DP/libgaba/new implementation technique
    - Narrower of variables allow for parallelization
    - libgaba available on GitHub

+ MUGAN: Multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment
    - EM maximization

### BIOINFO: RNA Informatics
+ Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer
    - Mutations acquired by tumor recurrence
        - Mutations in recurrent HCC
        - Reprogrammed Expression by Recurrence (RER)  (by heirarchical clustering/KM analysis)
        - GOLGB1 and SF3B3 are novel targets 
        - SOX4 and IL-8 are downstream targets (interaction networks)
    - DNA copy numbers and methylation
        - Look at: distributions and correlation
        - Multiomics-based classification
        - Validation using TCGA
        - Subtype markers

+ Differential, pathway, and network analysis of RNA-seq data
    - Read count data
        - RNA composition
        - Poisson model
            - calculate the probability to observe k read counts 
            - But really Poisson? No. Variance >> mean
            - Negative binomial distribution: composition of Poisson and gamma distribution
            - lambda ~ Gamma(alpha,beta), alpha: dispersion variable
            - R: DESeq, EdgeR, … etc.
            - Focus on AUC for performance valuation
    - Read count bias (Oshlack and Wakefield, 2009)
        - Dispersion is the upper and lower bounds of SNR
    - Pathway (GSEA) analysis of RNA-seq data
        - Step 1: Normalize the read count data using TMM, DESeq ,voom (not RPKM)
        - Step 2 Apply GSEA 
            - Small replicate: gene-permuting GSEA (often used)
            - But, it will cause many false positives
            - Instead, use absolute gene statistic in one-tailed GSEA
    - Gene-set clustering
        - Difficult to identify biological themes
        - GO, DAVID, Enrichment Map
        - Expathnet: new web server
        - PPI weighted distance

## GIW/BIOINFO Day 2
### BIOINFO: Cancer Genomics
+ Utilization of PDX library and cancer genome database for precision oncology
    - Why sequence cancer samples
        - Understand more about cancers
    - Ideal study plan for cancer genome research
        - Large number of samples
        - High depth of sequencing
            - small cancer cell proportion
            - cancer cell heterogeneity
        - Large area of the whole genome
            - structural variation
            - variation in noncoding region
        - Many samples from the same patient
            - Multiple site from the same tumor
            - Normal sample (blood, adjacent normal tissue)
            - Metastatic loci
            - Samples from different phase
        - Integration of multi-omics data
            - Transcriptome
            - Epigenome (DNA methylation, Histone modification, chromatin structure)
            - Proteome
        - (Important) With clinical outcome
    - Clinical information we need if possible
    - PDX: patient-derived xenograft (drug, clinical data, genomic data)
    - Targeted mainly on breast cancer (most in eastern Asia)
    - Make PDX -> experiment (now in process)
    - Metastasis study using xenograft tissues
    - Drug response test with PDX models
    - Clinical cancer genome database: http://ccgd.snu.ac.kr
    - Question: Since less patients recurred, it’s not good for the research (success rate is around 30%)

+ Systematic analysis of cancer omics and phenotypic data for finding synthetic lethal target and biomarker combinations
    - Challenges in cancer therapies
        - Wide variety of cellular response against target-specific cancer drugs
        - Difficult to predict clinical outcome
    - How to effectively combine different information of biomarkers?
    - Analysis of cancer omics and siRNA screening data: cancer phenotype
    - Cell line enrichment analysis (CLEA)
    - STK11 loss of function is a potential biomarker for lung cancer therapy
        - Major mutation pattern in patient samples
        - STK11mt is unique in lung cancers
    - ATP1A1 is a selective target for STK11 mutant cancers
    - Why siRNA library screening?
    - Algorithm for building network: weighted tanimoto score
    - Pathway analysis and validate the biological functions
    - On-going: screening of a CSC-inhibitor on colon cell line panel transcriptome analysis of the sensitive cell lines
    - Feature extraction from images
    - Omics data mining

+ Pilot targeted next generation sequencing data
    - HNSCC: head and neck cancer 
    - More precise target is needed
    - General design of umbrella trial
    - Part 1: pilot targeted next generation sequence (NGS pipeline … etc.)
    - Part 2: umbrella trials
    - Establish web-based report system
    - Molecular tumor board meeting (MTB) for interpreting NGS data
    - TP53 was the most common mutational event occurring in 47 patients (51%) followed by CDKN2A (n=23), CCND1 (n=22), PIK3CA (n=19)
    - Comparison of HPV-positive and -negative

+ Application of RNA sequencing data in cancer research
    - N=59: 38 for experiment; 21 for control
    - Use of WTS data to discern functionality

### Keynote Speech 2
+ Genome Editing in Plants, Animals, and Human Cells
    - Genome editing timeline (Kim JS. Nature Protocols)
    - Kim H and Kim Js:  comparison of programmable nucleases 
    - CRISPR: adaptive immune system in bacteria
    - Genome surgery: in-/ex-vivo therapy
    - DNA/genome-wide cleavage scores
    - How to avoid off-target effects?
    - CRISPR RGEN Tools (Cas-Designer)

### BIOINFO: Machine Learning for Bioinformatics
+ Learning Graphical Structure from High-Dimensional Data using Sorted L1 Regulation 
    - Bonferroni versus Banjamini-Hochberg (B-H: slightly large chance of false discovery but much more powerful)
    - MHT as model selection
        - linear combination of each interested biomarkers
        - Coefficient tell the significants of the biomarkers
        - Polynomial complexity (compared to combinatorial explosion: test every possible combinations)
        - Transformed problem: X * beta -> y  => convex optimization problem
        - New methods/algorithms modify the regularization term (R packages available)
        - Graphical LASSO/SLOPE: not requiring clinical outcome (unsupervised)
    - Graphical SLOPE
        - Precision matrix (n x n, Theta): representing a graph
        - Gaussian MRF
        - Markov property: conditionally independent if the nodes are disconnected (theta = 0), but difficult
        - Case study: Neuroblastoma (NRC), breast cancer
        - p-values versus graph structures (grade 1-3)
        - Difference of networks
        - Node degree distribution plot
        - FDR control: replacing this using graphical LASSO
    - Question: lambda sorted in decreasing order, also sorting the markers
    - Question: no matter much for the Gaussian noise assumption
    - Question: only need to provide FDR -> number of lambda’s -> graphical LASSO will calculate them for us

+ Network based ML Algorithms of Intra-relation, Integration, and Inter-relation for Diseases-Drugs
    - Integration of heterogeneous data
    - Layers == networks for different type of data (with causality)
    - Integration of networks: addition/subtraction of networks
    - Disease networks
        - Gene regulatory networks
        - Metabolic pathways
        - Protein interaction networks
        - Etc.
        - Connecting diseases
            - Cosine similarity over a disease vector
        - Scoring for the co-occurrence
            - Semi-supervised scoring
            - Will be only given a few pieces of information on one or two diseases contracted
            - Graph-based SSL algorithm (solution (probabilities) can be obtained in a closed-form)
            - Passing through activation function, sort the associated diseases
            - Case study: diabetes (Type I and Type II)
        - Hierarchical disease networks
            - Intra- and inter-relation
            - Tridiagonal block matrix (weight matrix)
            - Limitations
                - Existence of rectangular blocks
                - High computational complexity and sparseness; O(n^3)
    - Proposed method
        - Matrix separation
        - Nystrom method
        - Changed solution (only need cheap computational efforts)
        - 121.63 -> 12.85 (secs; 500 layers) in computation time
    - Casual network
        - Metabolic pathway + diseases association networks
        - Flow function: comparing inflow and outflow to decide the direction
    - Comorbidity scoring
    - Drug network
    - Future works: theoretic works & find the applications
    - Question: edges: connected or not (1/0)
    - Question: demographic data? prospect for precision medicine
    - Question: image such as MRI is another new area

+ Functional prediction of causal regulatory variants identifies a novel autism gene (topic changed)
    - Cancer dependency maps (by genome-wide screen)
    - Bayesian gene regulatory network
    - Cutoff values on dependency and estimate them respectively
    - Prediction and validation in PDX mice (still under validation)
    - Prediction in TCGA samples
    - Comparison with other algorithms (peaks separation)

+ Computational analysis of intra-tumor heterogeneity in cancer
    - Tumor heterogeneity -> challenge in cancer therapy
    - Major subjects
    - Apply NGS data
    - Analysis of tumor clonality
    - Tumor heterogeneity in colorectal cancer (CRC)
        - 46 positive/42 negative samples
        - PyClone model
        - Hierarchical Bayesian clustering model
        - Oligo-/Multi-clone (low/high heterogeneity)
        - Vascular invasion and stages are dramatically different in these two groups
        - Clinical relevance according to clonality
        - Characteristics of mutations according to clonality

### Keynote Speech 3
+ Fun stories in translational biomedical informatics
    - Translational bridge: basic and clinical
    - Patient data and phenotype is the key
        - e.g., social networks: big data (anonymous social network)
        - Database for comments is available online
        - The power of machine learning
        - Many datasets available 
        - Predictive analytics is finally making inroads into healthcare
    - UW medicine clinical data repository (UW == University Washington)
    - Statistical analysis on clinical data
    - Enrichment analysis on clinical data (term enrichment)
    - That was only only for coded data (20%)
        - 80% is unstructured data; e.g., clinical note/text, image
        - Pilot NLP engine
        - Identifying concepts in discharge notes
        - Lots of questions and opportunities
    - Interpreting genomes and precision medicine
        - 80% of rare diseases have genetic origins
        - Rare diseases are significant to human health
        - Many mechanisms of pathogenicity (what function/ why this function)
        - MutPred: using functional properties (MutPred2 is now available)
    - Community challenges enable real citizen science
        - Started in protein structure prediction -> CASP (Critical Assessment of Structure Prediction)
        - CASP targets can also be used for evaluating function prediction
        - CAGI challenge

## GIW/BIOINFO Day 3
### GIW 8: Cancer Bioinformatics
+ WISARD: a comprehensive workbench for the analysis of related database
    - WISARD: Workbench for Integrated Superfast Association Test with Related Dataset
    - Handling 9 genotype file formats
    - Support five operating systems
    - WISARD website
    - Why fast
        - Matrix optimization
        - Analysis-wise optimization

+ Isoform specific gene expression analysis of KRAS in the prognosis of lung adenocarcinoma patients
    - Lung cancer (3 types) -> KRAS mutant patients show worst prognostic records
    - KRAS4A & KRAS4B
    - Large-scale genome data from TCGA project
    - Isoform-level expression analysis (proportions of K-RAS isoforms)
    - Linear regression analysis (# mutation v.s. expression): show clear association, especially in K-RAS4A
    - Survival analysis (better in KRAS mutant/amplified groups)
    - Multivariate analyses 
    - Low K-RAS proportion and expression is the key factor for the disease
    - Limitations: the use of overall survival & the lack of proper independent cohort for validation
    - TCGA: largest cohort dataset with well-defined clinical records
    - Question: Better to use the results for predictions

+ BRCA-Pathway: a structural integration and visualization system of TCGA Breast Cancer data on KEGG Pathways
    - Motivation: biological mechanism
    - Webservice
    - TCGA data + pathway + driver genes, TFs, hallmark gene sets
    - Use correlation coefficients between genes
    - Survival analysis (mutation/no mutation; p-value=0.05) and oncoprint
    - User data visualization
    - REST-API: easy access by users
    - Mapping multi-omics data onto KEGG pathway
    - Question: Why breast cancer? The largest data size; other kinds of cancer is future work
    - Question: Possible for classification? Can choose subgroup for various combination of clinical records

+ Stage-Dependent Gene Expression Profiling in Colorectal Cancer
    - Previous works have focused on the correlation between cancer stage and individual gene expression
        - Examine 9 microRNAs
        - Gene expression patterns have been analyzed for limited stages with small samples, without proper data pre-processing
    - Workflow
        - Data aquisition
        - Data modification
        - Analysis of functional characteristics
        - Analysis of structural characteristics
    - Computing correlation using LMER (linear mixed-effect regression model)
        - A representative gene RPSY1 shows different correlation coefficients between LM and LMER
    - Functional characteristics of the stage-dependent genes (subgroups: INC/DEC)
    - Low expression of TAP1 is associated with the development or progression of colorectal cancer
    - Both types of genes are closely associated with each other
    - DFS using KM analysis
        - High expression and low expression for two subgroups
        - MPP2 (p-value=0.012)
        - TXNL1 (p-value=0.072)
    - Continuously increasing genes are related to nerves and development system while other genes are related to cell cycle and metabolic process
    - Question: Is really LEMR is really better? Or just few cases are different while other results are the same? Basically the same.

+ MGRFE: multilayer recursive feature elimination based on embedded genetic algorithm for cancer classification
    - (presenter not shown)

### BIOINFO: Big Data Informatics Supported by Center for Plant Aging Research
+ Cancer Precision Medicine through Transforming Biomedical Big Data to Actionable Knowledge
    - Big data challenges
        - Efficient storage and access
        - Data analytics to mine valuable information (most focus)
    - Biomedicine big data challenge: multi-scale, complex, heterogeneous, and distributed
    - Constructing BD2K intelligence engine
        - Biomedical entity Search Tool (BEST): “<disease>”+target:[mutation | drug] 
    - Predict best drug given patient information
        - Synergistic effect S = Observed affect E+ Additive affect A
        - Sub-challenge 1-A: predict drug synergy from all available data
        - Prediction model
            - RBF SVR (regression)
            - Choose rather simple model due to label imbalance
            - Use AWS to speed up the training time (~1hr. for 1800 cores)
        - Translatability of deep learning model (difficult; tried various types of models)
    - Regression tasks not good, but tree-based models provide reliable results
    - Synergistic rules generation (qualitative analysis)
    - Biomedical QA with deep neural reader (attention-sum reader & biological feature)

### Keynote Speech 4
+ Peer through your Brain with Multilevel Data
    - World largest brain dataset: Chinese southern biobank
    - How to integrate these data to make predictions?
        - Association study; causality; modeling
        - I. Nonlinear association (need/try nonlinear transformation using different kernels)
        - II. Causality inference (of time-course data)
        - Current trend: kernel methods
    - Intelligent image reader
    - Depression
        - Searching the roots of depression
        - Data driven approaches (time-course data); functional connectivity -> abnormal links
        - Reward and punishment processing in depression
        - Depression uncouples brain hate circuit
        - Painting the full depression circuit
    - Quantity of your brain
    - Dynamic brain 
        - ffMRI + EEG
        - Entropy v.s. entropy rate
        - Entropy gene: expression strongly related to entropy
    - Integrated Multi-scale data for Prediction (IMuDP)
    - Question: Animal models will be built later? (human first due to machines for fast scanning)
    - Question: How to identify specific areas for specific emotion? Literatures
    - Question: How to quantify the emotions such as creativity? All sorts: math, picture, images, etc.
    - Question: How to combine these data? Way too large data: dimension reduction -> prediction (but labour intensive; want to make this process automatic) 

### GIW 10: Biomarkers Discovery
+ Identifying statistically significant combinatorial markers for survival analysis
    - Survival analysis
        - Methods for statistical analysis of longitudinal data on the occurrence of events
        - Failure time (an event happened)
        - Censoring (survival time not exactly available; e.g., drop out)
        - For finding significant makers
        - Challenge: multiple-testing and finding ALL significant combination of biomarkers
    - Significant markers for precision medicine
        - Standard therapy -> alternative therapy 1 -> alternative therapy 2
    - Limitless-Arity Multiple-testing Procedure (LAMP)
        - Minimum p-value
        - x: frequency of mutation in GWAS data
        - If p_min > min. threshold -> not testable -> not include in hypothesis
        - Monototically-decreasing in x (for p-values)
        - Cannot be applied to survival data (no time information)
    - Log-rank test
        - Chi squared D.O.F.=1
    - Proposed method: contingency tables
        - p-value lower bound
        - Algorithm similar to LAMP
    - Experiment
        - TCGA breast and ovarian cancer data (gene expression profiles) (BRCA & OV)
        - z-score >=2 -> 1; else -> 0
    - SW available: rtrelator.github.io/SurvivalLAMP
    - Question: 1/0 omitting down-regulated genes -> try to deal with continuous data 
    - Question: simulated data? NO
    - Question: SNP? Not yet
    - Question: FDR control? Not yet

+ Identifying feature genes from gene expression data by twin kernel method
    - Motivation: biological aspect 
        - Curse of dimensionality
        - Only few groups of genes are related to them
        - Select “biological plausible” genes
        - Linear selection method cannot handle the nonlinear relationship in the dataset well
        - As a result, kernel methods were introduced to deal with the nonlinear relationship
        - Computationally complex due to SVD
        - Local v.s. global kernels
        - Want to a general unified mixture model with low computational cost and be able to select significant genes
        - CND (conditionally negative definite) (p,q)-kernels were introduced
    - (p,q)-based twin kernel
        - Less computationally intensive
        - Symmetric and CND
        - Q-analogue of a mixed kernel of two multi-quadric kernels
        - Rather a distance measure d=|| phi(x) - phi(x’) || ^ 2 = H_{pq} (x,x’)
    - Algorithm
        - Select to 10 ~ 100 genes as feature genes
        - 10 % as testing
        - SVM/KNN as classifier (two-class datasets)
    - Results
        - Easy separation using p,q-domain
        - PCA analysis was also applied
        - Select first 50 genes for survival analysis
    - Question: Parameter tuning? 10-fold CV
    - Question: Contain both global and local properties of polynomial and Gaussian kernels

+ Exact Association Test for Small Size Sequencing Data
    - NGS technology allows to identify all rare SNPs or other types of variants
    - Asymptotic tests
    - Exact Association Test (EXAT) not relying on the assumption of large sample size
    - Generalized CMH test (GCMH)
    - Analyze IPMN patients
    - Small sample size
    - Contingency table
    - Detection rate, QQ plot, Venn-diagram for testing methods, pair-wise scatter plot of p-value for testing methods
    - Question: Why normal noise? 

### Keynote Speech 5
+ Single Cell Tumor Transcriptomics: Algorithms and Applications
    - CellHub: tackling disease, one cell at a time (big program in Singapore; not completed)
    - Fluidigm C1; 10X Chromium
    - Promise of single cell tumor transcriptomics
        - Unbiased analysis of all cell types in tumor, cell type markers, biomarkers, patient stratification
        - EMT/cancer stem cells/new cell types?
    - Challenges
        - Cells stick together; e.g., epithelial cells => custom dissociaton
        - Noise => new algorithms
        - Distortion & bias => new algorithms
        - Cost => dropping
    - Algorithms (guiding principles)
        - Make as few assumptions as possible
        - Account for unique properties of the data
        - Exploit unique properties of the data; e.g. non-parametric methods
        - Scalability
        - Three algorithms for single cell transcriptomics
            - Normalization
            - Clustering
            - Differentially-expressed genes
    - Why new normalization method?
        - Scaling is NOT adequate
        - Too many zeros in scRNA-seq
        - imputed Quantile normalization (iQ)
        - Use normalization as the benchmark
        - iQ maximizes the tightness of clusters
        - semi-supervised clustering -> RCA clustering algorithm (R package)
        - NODES for DEs
    - Problem of tumor heterogeneity
        - genetic/stemness/microenvironment
    - EMT markers are up-regulated only in fibroblast

### GIW 11 Systems Biology and Biological Interactions
+ GxGrare: Gene-gene interaction analysis for rare variants from NGS data
    - Heritability
    - Why rare variants?
    - Issues for rare variants
        - Low power
        - Bi-directional effect
        - Gene-level test for rare variants: burden/non-burden test
    - Issues in genetic interaction analysis
        - Data sparsity
    - MDR overview
        - Validation methods
        - Information gain

## GIW/BIOINFO Day 4
### BIOINFO: Systems Biology
+ Systematic approach for predicting pluripotency of stem cells and human chronological age using DNA methylation data
    - Now is possible to extract information form identical cells (information in the cell level)
    - Research aims
        - Predict cell pluripotency from DNA methylation data
        - Find epigenetic markers for mouse ESCs
    - Windowing approach to extract features from sequence data
    - Cell-cell ordering
        - Using single cell gene expression profile 
        - SLICER/TSCAN/Wanderlust
        - Pseudo-time construction
    - Regression modeling
        - F-test; FDR < 0.05
        - Alpha selection of Elastic Net
    - Blood age predictors showed good results with small features
    - Objective
        - tissue-specific age prediction model
    - Methylation data from TCGA and GEO
    - Feature selection
        - Association test
        - Bootstrap analysis using Elastic Net
    - Feature analysis
        - Tissue-specific aging-related CpGs contains more negative ageCpGs
        - Ratio of CpG islands differentiate tissue-specific/common markers
    - Question: Cancer will affect the results? Depends on the type of diseases
    - Question: Aging information are the same from one person? Possibly different

+ A Proteogenomic Analysis of Early Onset Gastric Cancer
    - Early Onset Gastric Cancer (EOGC)
        - Most genomic studies focused on elderly GC patients
        - Poor prognostic
        - Young diffuse-type of GC (from data exploration)
    - 46 significantly mutated genes in GCs
    - Categorization of nonsynonymous somatic SNVs …
    - Utility of proteomic data

+ Network-based augmenting and interpreting disease genomics data
    - None of them (gene expression profiles, GWAS, WES of tumors) are sufficient alone
    - NGSEA
        - Overlap-based and rank-based
        - Drawbacks of GSEA
        - Overview of NGESA: DEGs -> scoring genes using their network neighbors -> network
        - Mapping drug-disease association
        - Showed very high prediction power for CRC (6 of them are previously known drugs for colorectal cancer)
        - Identify new drug candidates
    - GWAB (genome-wide association boosting)
        - More effectively retrieves disease genes than GWAS alone
    - Driver v.s. passenger mutations
        - Subject: how to distinguish them
        - Based on recurrent mutations: use deleteriousness of the mutation
        - Mutsig CV (including more information to reduce FPs)
    - MUFFIN
        - Predict driver through pathway information
    - No comprehensive gold standard cancer gene set
    - Each cancer gene set has a different trade-off criteria

+ Drug repositioning for cancer therapy based on large-scale drug-induced transcriptional signatures
    - Challenges of drug development; e.g., high risk of failure
    - Drug repositioning (DR)
    - Transcriptome-based drug discovery
    - CMAP approach

### Keynote Speech 6
+ (Sum Kim)
    - 3D time series analysis (genes, time, conditions)
    - Detecting cluster patterns
    - Domain knowledge can be used more aggressively -> biological networks (domain knowledge into a computational framework)
    - Perturbed pathway
        - Cross-correlation between differential expression vector
        - Influence maximization in the time-bounded network
        - Differential expression vectors
        - Delay -> maximum cross correlation
    - Wish to use literature and your input as domain expert knowledge in a single computational framework
        - No guarantee that all pathways selected by analysis are relevant to the context of data
    - AI modeling?
        - Deep learning with domain (biology) knowledge
        - We know little knowledge about biology though
        - DeepFam
            - Based on CNN
            - Alignment-free method
            - Modeling family without alignment (usually faster)
            - But still black box? Capturing motifs
        - Hybrid approach of relation network & CNN for breast subtype classification
            - DL need grid-like data; e.g., matrix
            - DeepMind for relation network paper?
            - Input (MA) -> PPIN -> convolution on graph -> pooling -> relation network for each edge -> MLP -> ensemble -> softmax(subtypes)
            - y=VgV^Tx, g: learned (from relation network)
            - LumA, LumB, Her2, Basal; TCGA RNA-seq expression
            - Monte-Carlo cross-validation is used
