perfectlytinyworkspace

[perfectlytinyworkspace]

Neural Networks

feedforward, fully-connected neural networks

composition of a neural network

input node (s) : put the data into the neural network

hidden node:

linearizes the data with (weight • x + bias) = z

warps that linearized data with a nonlinear factor known as sigma with (z • sigma)

returns an activation output a that is then passed to the next hidden node as its input

hidden node weights are written atop the lines connecting one node to another

the last node is the output node whose activation is churned out as the y-hat prediction

because each input is the output of the last node, we could theoretically plug them all in by hand

general flow of the neural network

forward propagation – feeding activations into the next node »»»»

loss function – the error that is generated with the output of the forward propagation

back propagation – the minimization of the loss function via altering the nodal weights/biases

where do networks get deep and wide?

depth is layered growth and width is growth along the same layer

depth = parent + many child nodes and width = many sibling nodes

a fully-connected neural network is a network in which every node is connected to all of the nodes in the layers above and below

(but not between sibling nodes because info flows layer-by-layer)

express all of these weights associated with the connections in matrices and linear algebra in order to visualize it efficiently

training assigns useful or non-useful weights to each of the connections in a fully-connected neural network

the process of using a neural network

input node(s) can take on many different dimensions of data to run through the hidden nodes. the general form of your input shape is the number of dimensions/traits you have in your X-matrix

forward propagation training: first start with randomized weights and run the data through the neural network

back propagation training: for a single layer, take the derivative of the loss function with respect to each of the nodal weights (gradient descent!) in order to minimize loss and find its sensitivity to the given random weights. So with 12 nodal weights, the gradient descent would be calculated across a 12-element vector

back propagation training: all leftover error from a single layer back propagates to the layer preceding it and then new estimates are generated forward, so that error back propagates and estimates forward propagate

the determination of the output node's dimensions is super critical to the proper interpretation of the NN

the output shape of an NN is often composed of a probability distribution of all possible labels (i.e., the % likelihood that the datapoint belongs to each label)

often a little output layer is created to generate (6)

the loss function is a measure of error – but actually there are many different loss functions that can be utilized

the optimizer is that which is utilized to minimize the given loss function (i.e., gradient descent)

for large networks and neural networks

normal gradient descent is too computationally heavy to run on all of these nodes and datapoints

so, the dataset is split into groups called batches of data for ease of updating weights iteratively, and an epoch is one round of training with the entire dataset

and stochastic (random) gradient descent is utilized to train the neural network instead

you can also add different algorithms to your SGD to further improve its efficacy (there are many)

#machine #notes

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Short-notes: K-Means Clustering

unsupervised learning method

iteratively update random clusters based on distances from the datapoints in each cluster

E-step: "how many datapoints are closest to this centroid and not to the other centroids?" assign each random centroid to a cluster

M-step: "how do I reduce the distance between the cluster and its newly assigned centroid?"

#machine #notes #shorts

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Genome Wide Association Studies (GWAS)

why genome-wide studies?

looking at the genotype can reveal useful information about a disease

i.e., use genotypes to develop treatments related to gene expression inhibition or upregulation

other uses: personalized/custom treatments, disease risk predictions for screening & recommendations for lifestyle changes

types of genomic associations

information varies based on nature of genome-disease relationship

mendelian disease: complete penetrance w/ categorical + or - disease phenotype, single mutational cause, clear inheritance patterns

complex trait disease: quantitative + or - disease phenotype influenced by multiple gene loci & environment in a possibly non-additive manner

linking loci to complex traits/diseases

categorical linking via association mapping: split a sample group into the case and control groups and look at allelic variations between the two

find the significance of your allelic differences via 1) odds ratio and 2) chi-squared analysis

chi-squared = ∑ (observed-expected)^2 / expected, where expected = total cases in case group • control group's percentage of a certain allele

quantitative linking via regression analysis: (y) phenotypic trait v. (x) allelic variation

collection of box & whisker plots for each allelic variant, that is then placed into a linear regression trendline

GWAS linking via association mapping across SNPs

use a Manhattan plot (-log p-value v. SNP's chromosomal position) to visualize associative relationship between disease p-value & SNP

utilize correction methods (Bonferroni & Benjamini-Hochberg) to lower false positives with thousands of SNPs across the genome

can also utilize microarray analysis with specifically significant SNP loci

Not all SNPs across the genome need to be looked at, however

SNPs may be in non-coding regions, or SNP linkage w/ other SNPs/the disease phenotype may not be perfect

SNP linkage falls across a sliding spectrum of equilibrium that affects the association between a SNP and the observed phenotype

challenges to GWAS

distinguishing between causal & linkage disequilibrium linkage between SNPs and disease phenotype

finding and observing rare SNP linkage to diseases

heritable variation (h^2) at quantitative trait loci (QTL, or loci that do contribute to a complex trait) is around 1-50%

new innovations for the future

use machine learning to consider different variables as you determine which SNPs are causal

i.e., determine likelihood that the SNP at hand is causally linked to a disease based on the environment it rests in

#genome #notes

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Principle Component Analysis

unsupervised method that reduces the dimensional space of a dataset (gives it some structure)

PCA creates a space for/version of the X-dataset with redundant correlations minimized, that can then be utilized by other ML algorithms

while linear regression's error is minimized with respect to the x-axis, PCA's error is minimized with respect to the trendline itself (think diagonal)

PCA: find the slope of the trendline that minimizes the error relative to the line itself

projection: drop all of the points onto whatever axis or trendline. minimal variance of projected points = large error of actual datapoints' y-values

PCA = find maximum variance of PROJECTED POINTS and thus minimum variance of ACTUAL y-VALUES. the line that gives this is the first principle component

the centroid is that which the projection variance/trendline is rotated around to find the line of maximum projected variance

to sum:

PCA can be defined in one of two ways:

find the trendline that gives the least dataset error relative to said line (rather than the x-axis)

find the trendline that gives the most dataset variance when the dataset is projected onto said line

» and finding the maximum projected variance also finds the minimum dataset error relative to the trendline

» the trendline is known as the first principle component and the mean squared error of that FPC is the explained variance

after the FPC – second principle component

each additional principle component must be perpendicular (orthogonal if in multiple dimensions) to the others

find the orthogonal SPC that gives you the best analysis of the dataset

the SPC takes the variance that falls outside of the FPC's explained variance and explains it, wrapping up all remaining variance

the distances between a given point and the FPC/SPC are known as the eigenvalues of each, and capture the relative amounts of variance that each component is able to explain

PCA allows for as many principle components as there are dimensions in/aspects of your X-dataset matrix

on the whole, the dataset may have been collected in terms of feature A and feature B, but may be best described as a collection of linear combinations AB and BA (FPC, and SPC)

graphing PCA

take the eigenvalue vector directions and make them the new axes. plot the datapoints along those component eigenvalue vectors.

the farther your points are along this new graph, the better – the distance is a measure of the efficacy of your PCA compression

so in this way, PCA is an unsupervised method that acts as a dimensionality reducer

so, why reduce the dimensionality of the dataset?

because the more correlated your individual X-matrix features are, the less useful they are (i.e, 2 or more features tell you the same thing about the y-outcome)

so PCA reduces those dimensions/features that give us redundant correlations by mapping the dataset only along the principle components

still, no information is lost because the full dimensionalities of the dataset are required to form the PCA in the first place.

» this is known as the manifold hypothesis! i.e., the dataset's relationships exist on a lower dimensional subspace than that which it was collected in

explained variance efficacy

a dataset has a total variance, some of which PCA attempts to explain in reducing the X-matrix's dimensionality

the space in which the dataset was collected generates certain relationships that have certain variances

by optimally altering this space and reducing dimensions the PCA is able to change those variances – explaining them through new relationship depictions

the more variance the PCA is able to explain, the better!

[cumulative explained variance plot]

the cumulative explained variance v. number of components relationship gives the efficacy with which a relevant PCA can capture variance

this is known as a cumulative explained variance plot and returns how many original X-dataset matrix dimensions can be discarded without losing explained variance

discarding dimensions and reducing the space of the X-dataset via PCAdecreases the likelihood of your algorithm overfitting to a complex/detailed dataset

#machine #notes

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Ensembles

fancy word for averaging (good for reducing variance!)

majority voting classifier

multiple models make separate decisions, which are then counted and the majority decision is the final output

hard voting: singular decision, with each model only returning a yes/no to the final output

in hard voting, the model is NOT allowed to give a certainty probability value of its decision, a la logistic regression classification

soft voting: each model gives a vector array [P(class 0) P(class 1) etc etc] and the final output averages the models' probabilities for each classification category before the "vote"

in soft voting, you can place weights on each of the models so that if a model has been right a lot of the time, it gets more of a say in the final vote » this is the idea behind boosting

bagging

bootstrap [make the bags] aggregating [train the models on the bags], or creating different datasets from your original in order to generate ensembles

create a bag from your original data by sampling from your dataset with replacement (aka bootstrapping); several bits of data are always left out of the bag

make a lot of bags and fit different models to them, and these become your ensemble of predictions that you take the final vote from!

a random forest is this process, with the models used being decision trees!

bagging decision trees takes the low-bias, high-variance decision tree model (a deep tree) and DECREASES the high variance! i.e., addresses the decision tree problem set

boosting

takes stumps (high-bias, low-variance small power decision trees) and builds them into trees sequentially

conglomerate the stumps into a tree, boosting the total effect by training each stump in sequence

steps to boosting:

all datapoints carry a uniform weight at the beginning

a stump classifies the datapoints. decisions that are correct get an increased weight

the next stump in the sequence now gets the datapoints with their new weights. it classifies the datapoints.

loop classifications using all the stumps in the sequence, with each next stump receiving the weighted datapoint classifications from the last loop's stump

all the models share the same updated weights, which is essentially how this "tree of trees" communicates.

random forest

two components:

bagged data with each bag being fed into a different tree

from one bag, only a SUBSET of the X-matrix is shown to a single node

(i.e., if X has 10 features, two random columns of features are shown to node A and two different random columns are shown to node B)

[then of course you aggregate and majority hard/soft vote at the end. scikit-learn uses soft voting and then an ensemle-level thresholding at the end]

why do this? to further prevent overfitting! i.e., if you underfit a single node, you can balance the overfitting of the entire tree and reduce the variance

forest evaluation – without a test set, use out of bag error i.e., test the data with unincluded dataset portions from each bag

#machine #notes

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Evolutionary Comparative Genomics

unified evolutionary theory

modern synthesis of historic ideas about evolution

Darwin: natural selection

Mendel: mendelian inheritance

population genetics: neutral evolution, genetic drift

tenets of unified evolutionary theory

undirected mutation and recombination gives rise to population variation, which is then pushed here and there by evolutionary pressure

∆gene frequency results from: genetic drift, gene flow (gene transfer between populations of the species), & natural selection itself

most adaptive mutations generate slight phenotypic changes, and actual alteration occurs slowly through accumulated mutations

cladogenesis: diversification arises through speciation due to gradual reproductive isolation between populations of a single species

higher order taxonomy arises with sufficient time to evolve

evolutionary genetics: how genomes differ between species (macroevolution) and within species (microevolution)

inferring gene age

a phylogenetic tree between different species can be utilized to compare the ages of two genes

the gene age can then be approximated via either the fossil record or the mutational rate

i.e., MYH16 gives most primates strong jaw muscles but humans have an inactivating mutation in MYH16 – so we can approx its age

gene function and evolutionary rate

catalytic gene function preceded regulatory gene function, historically

for cancer: caretaker genes maintain DNA integrity, gatekeeper genes regulate the cell cycle/proliferation (your oncogenes and tumor suppressors)

if two species share a gene and one species shows an accelerated rate of evolution in that gene, the gene's responsibility for that species' phenotypic differences may be indicated

use dN/dS for expressed genes because it involves codons; there are other measures of mutation rates in intronic regions

#notes #genome

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Decision Trees

Decision tree: a mechanism for making decisions based on a series of "if" factors

Root node – the decision that matters the most, ergo the decision that precedes all others

Leaf node – the last outcome of a decision tree branch

format: if (node1) and (node2) and (node3)...and (node n), then (leaf node: final decision)

determining the root node: intuitive in a simple human context BUT with data, one must use an algorithm

utilize mechanisms from previous studies (i.e., data visualization with histograms, regression) to find one that gives the most classifying information

each node is "greedily constructed," i.e., the model extracts as much information as it can for an individual node without thinking about info that could be derived from future nodes

so the decision tree algorithm is run recursively, again and again, until the data is sifted perfectly into individual categories

you can use the same features over and over again (recursive!!) to separate out stat at different levels of "purity"

each time there is new data, you can run them through the tree and observe where they end up!

growing a tree

split the parent node and pick the feature that results in the largest "goodness of fit"

repeat until the child nodes are pure, i.e., until the "goodness of fit" measure <= 0

preventing tree overfitting

overfitting: too pure, too in-depth tree, too many nodes that are specifically fitted to the given dataset rather than the real-world function

set a depth cut-off (max tree depth)

set a min. number of data points in each node

stop growing tree if further splits are not statistically significant

and cost-complexity pruning: utilizing some alpha regularization threshold to prevent overfit

cost-complexity pruning

regularizing the decision tree via alpha at the cost of tree purity

so as alpha increases, impurity also increases. number of nodes and depth of tree decrease

real-world accuracy increases to a certain point before decreasing – as prevention of overfitting with regularization becomes underfitting

#notes #machine

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Detecting Evolutionary Selection in the Genome

natural selection: an organism that is better adapted to the environment will survive and reproduce

natural selection of a trait requires the following:

heritable variation of the trait

differential reproduction of the trait

types of selection:

negative selection: stabilizing/purifying selection AGAINST variation from some norm or conserved/constant original

positive selection: directional selection FOR variation from the norm (see: environmental change as a selection factor)

neutral evolution: trait becomes dominant/common via random chance, i.e., no selective pressure pushes possession of this trait. allows mutations to freely occur

Kimura's neutral theory of evolution: most evolution is neutral!

in the noncoding region of the genome, this especially holds because these areas are not expressed phenotypically!

in the coding region of the genome, neutral and deleterious mutations will be more common (enhancing/beneficial mutations are RARE)

detecting selection in the genome

why detect? map evolutionary history, predict disease selection, and identify functional regions of the genome

how to detect selections? use the d(n)/d(s) ratio

d(n) = number of observed mutations ÷ number of possible mutations for non-synonymous mutations that alter the corresponding amino acid

d(s) = the above but for synonymous mutations that do NOT alter the corresp. amino acid

for neutral evolution, d(n) = d(s) and omega=1

in positive/directional selection, omega > 1 | in negative/stabilizing selection, omega < 1

need to account for POSSIBLE as well as OBSERVED single-point mutations, n.s. and s., to correct for different mutational possibilities

assumptions made in the initial modeling for selection in the genome

lack of accounting for codon bias in some organisms, which leads to an overestimate of s mutations

sampling bias towards individuals and sample groups analyzed

synonymous and non-synonymous mutations are not equally likely! different mutation locations and pathways will produce various ns/s ratios

transitions are more likely than transversions (T-C, G-A) – solve resulting underestimation of s mutations via k = transitions:transversions = 2 for whole genome and 3 for coding regions

maximum likelihood estimation

i.e., which detected selection model is correct??

MLE allows comparison of various evolutionary models/parameters

use MLE to find the evolutionary selection model that has the highest likelihood of generating the observed mutations

you will need!

observations to be mapped by a model

evolutionary model to be tested

a method for computing the likelihood of getting mapped observations with tested evolutionary model

the math of things involve the following symbols:

k = transition:transversion ratio

π(j) = codon usage for any given codon j

omega = d(n)/d(s) from above

q(i, j) = the rate of transition from codon i to codon j

calculate the rates of a transition between different codons w/ a single point mutation:

q(i, j) = 0 for > 1 mutation

= π(j) for syn. transversions

= π(j) • k for syn. transitions

= π(j) • omega for nsyn. transversions

= π(j) • k • omega for syn. transitions

site or branch-specific selection detection

conduct an analysis on whole genome/gene + whole tree with the assumption that selection is equal across gene/tree

» this assumption of equivalent selection across the genome is often mistaken, though :(

most mutations are either neutral or deleterious, so generally across an averaged genomic selection analysis the omega is = 1 or < 1

» this purification favoring trends towards missing the rare positive solutions when conducting a genome-wide analysis of selection rates

looking at the whole tree generally requires long time scales; there are other models, however, that can vary the time scale of the selection analysis

» McDonald-kreitman test (MKT) – calculate an omega within species [substitutions] via short time scale; calculate omega between species [SNPs] via longer time scale

#notes #genome

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Confusion Matrix!

a box that contains the 2 correctly and 2 incorrectly classified values from a model | i.e., [true, false] x (predicted-true, predicted false)

<^ and v> are true positives and true negatives, ^> and <v are false negatives and false positives

the 4 numbers can be utilized to generate a large number of different calculations for classification error, prediction, performance, etc etc etc

don't memorize the whole confusion matrix – just look it up on wikipedia

accuracy of a model (true v false predictions and their overlay on reality) is not all equal!!

if you have a model that is supposed to detect something rare and isn't able to do it, the rarity of the true positive makes that model 99.9% accurate – but useless

ACCURACY = TP + TN, i.e., true predictions ÷ FP + FN + TP + TN, i.e., everything | ERROR = 1 – ACCURACY

there are many different 'scoring' performance metrics that can be generated additionally

» again, why exactly do we have this confusion matrix?

well, accuracy and error alone sometimes do not tell you enough about the model's true performance

so, we split the possible model outcomes into four different categories and conduct different nuanced scoring processes with them

rates scoring from confusion matrices

true positive rate = true positives / positives 1 – FNR

true negative rate = true negatives / negatives 1 – FPR

false positive rate = false positives / positives, or 1 – TNR

false negative rate = false negatives / negatives 1 – TPR

there is a positive-negative trade-off for each model!

moving the decision threshold cut-off for negative/positive model prediction will reduce one error at the cost of another

Type I v. Type II error [visualize two different distributions on a 3D plot]

precision/recall scoring from confusion matrices

precision [aka the positive predictive value]: true positives / predicted positives i.e., TP + FP

this tells you more about your model's ability to classify

i.e., out of the predictions you made about positive, how many did you get right

recall is just the TPR but in a precision context, i.e., there is a tradeoff between precision and recall

recall: how many positives did you get | precision: how many positives where you right about

precision y v. recall x is often graphed to describe a model

F1 score: 2 • (Pre • Rec)/(Pre + Rec), summing up their relationship

TPR = precision = sensitivity | TNR = recall = specificity

receiver operating characteristic – ROC curve

this curve describes the TPR v. FPR tradeoff as the decision threshold (i.e., your classification model) is moved around

using the ROC curve, you can determine the optimal tradeoff for your particular problem

common way to assess the performance of a model!!

AUC is area under the ROC curve, the 1-number metric that sums it up and can be used to compare models!

best AUC is 1.0 and worst AUC is 0.5 (the random guesser model)

if your AUC is less than 0.5, you have a reversed model and just flip it for better classification results!

combine ROC (AUC) and cross validation!!

generate 1 ROC curve per fold/round of cross validation

average the fold-ROC curves and take the AUC of that average ROC

this averaged AUC measures your performance and helps you handle variability when you decide on a model to use

#notes #machine

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Cross-Validation

When dataset is insufficiently big to split into training and test sets, use cross-validation

cut dataset into 5ths and assign 1/5=validation, 4/5=training in a rotating manner

The training chunks are known as a training fold and the validation set is the validation fold

with each round of train and test, use a different 1/5 of the dataset as a validation subset [without replacement after each round]

average all performances on each round of validation to get the fully-trained and tested model

subset size can be modified but too many subsets leads to too many rounds of training

to sum: cross-validation is the evaluation and averaging the performances of [smaller models] of [portions of the dataset]

instead of using an entire small dataset to train

KEY: you are not modeling your entire dataset, only portions of it

standard of splitting into subsets: 10-fold validation, k=10

LOOCV – Leave-one-out Cross Validation

the highest benchmark standard of performance that doesn't actually get utilized often because it's inefficient

in LOOCV there are N training folds

using cross-validation to select for the best model in a dataset that is large enough for a train-test split

i.e., try various models on the same data to determine which yields the best predictive models

[steps]

so split your data into train and test sets, then split the training set into those training and validation folds & vary the models and k-values used

look at the performances returned for each validation fold and select the highest-performing model

retrain the training set on that highest-performance model to get the best fit for the entire training set.

[optional: train on the whole dataset (training+testing sets) and return a whole bunch of estimates for its performance from above]

then test it on the test set!!

#notes #machine

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Regularization in ML

In linear regression, regularization counteracts overfitting

An optimizer will assign a weight to each of your datapoints in the dataset

Regularization demands that the optimizer find a weight w that falls within a certain threshold C

A higher C value decreases regularization; smaller C penalizes the slope of the line's ability to fit the dataset » slope-fit trade-off

the new function is called a regularized or augmented function

min Ein(w) + (lamba/N)w™w where as C increases, lambda decreases

watch your regularization parameter (lambda/N) – mindlessly increasing it to decrease C and regularize may result in turning the algorithm off

if the parameter is larger than the slope, the slope becomes a constant and the algorithm underfits the data

regularization normalizes the slopes of lines generated in the hypothesis space. so over-regularization over-narrows the slope distribution

knobs to control over/underfitting:

dataset size | hypothesis space | regularization parameter

modern ML is mostly the field between overfitting & regularization

regularization with gradient descent: weight decay

w(t+1) = w(t) (1-2n(lambda/N)) – n ∆ Ein(w(t))

when lambda is small, the first term goes to something less than 1 and the steps get smaller and smaller as the function goes on

#notes #machine

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Repetitive Sequences and Transposable Elements

(link to genetics notes on transposable elements here)

introduction

> 50% of the human genome is repetitive elements

simple repeats / short tandem repeats (STRs) – 3% human genome | trinucleotide repeats – common STR pattern

transposable elements – 44% human genome | eukaryotes have a larger portion of repetitive TE's than prokaryotes

large-scale duplications – 5-6% human genome | chunks of chromosome duplicated due to unequal crossing over

do these segments have a function, or are they simply either neutral or disease-causing?

expansion process = multiply repeats, generally pretty stable

i.e., short repeats can form hairpin loops on a single strand, with repeat extending after replication as a result

disease standpoint in coding regions: repeat that encodes for an amino acid may result in extra AAs and protein misfolding | EX: Huntington's

disease standpoint in noncoding regions: too many repeats results in strand/chromosomal instability & breakage and/or transcriptional suppression | EX: fragile X syndrome

sequence complexity of repeating and non-repeating segments

take the length of the sequence L and the size of the option pool (i.e., GTAC is N=4, proteins is N=20) and input them into a complexity equation

equation is K = (1/L) logN [L! / πn(i)] to find the complexity score of the sequence

you can also calculate the number of reading frame windows that could be generated from the segment, assign complexity according to a relevant threshold, and join adjacent regions w/ matching complexity

SEG and DUST complexity measures for proteins and nucleotides, respectively

transposable elements

class 1: retrotransposon – Ctrl+C, Ctrl+V

RNA copy of a DNA segment is made, moved to a different portion of the genome

RNA copy is reverse-transcribed to DNA at the new location and incorporated into the genome

LINES & SINES: see genetics notes

class 2: transposon – Ctrl+X, Ctrl+V

original DNA segment itself is excised, moves to a different portion of the genome

original DNA segment is inserted at the new location and reincorporated into the genome

autonomy of genomic movement

autonomous: all machinery is included in movement, i.e., transposase/retrotransposase genes are included within the sequence

non autonomous: relies on host proteins for movement across the genome

resolution of the C-value paradox:

genome size does not directly correlate with organismal complexity

rather the confounding variable is the individualized TE composition/replication

identifying TEs in a sequence of interest

check for repetitive flanking regions using the Dfam database, a bank of known TE sequences

RepeatMaster: identifies all occurrences of low complexity regions and specific TE repeats in a genome/region

reproductive selection for TEs

host organismal level: must be in germline without lethality or presence in host-selected for regions

genome level: need to be able to be transcribed, therefore high selection for TEs that bind host transcription factors in germline or early development

TE insertions, especially if TE binds transcription factors, can determine host gene expression. if beneficial regulatory function, the new insertion is reproductively selected for

host selection mechanisms can silence deleterious TE insertions via sRNA or DNA methylation defenses » evolutionary race

gene duplications & pseudogenes

large-scale duplications can be caused by misalignment & unequal crossing over of chromosomes

often facilitated by repetitive sequences along the genome

this leads to copy number variants in different individuals along certain portions of the genome

two-tailed beyond a certain CNV gene dosage can lead to disease and disruption of gene expression regulation

pseudogenes look like genes but don't code for proteins because they've lost some aspect of function

most often they result from duplications of a gene that were either selected to be silenced or had some inactivating mutation

another mutation in a pseudogene region could possibly reactivate it!

#genome #notes

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From Charts That Lie

#notes

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Conceptualizations in Machine Learning

training set error

error on training dataset, which should lower as a model is optimized

arbitrary goal is to lower the training set error

test set error

error on test dataset (reserved for testing in particular, without any contact with training process)

withholding the test set means that the test set becomes an ESTIMATE of real world data behaviors

"output"/generalization error

model's performance in the "real world" outside of training and testing datasets

different from "general gap" that defines ∆(training error, test error)

training and test set errors are estimates of this generalized error

generalizing models to data

a goal is for the model to generalize well to UNSEEN "real world" data

unfitted/untrained models are expected to have high training and test set errors

fitted/trained models are expected to have low training error and (hopefully) low test set error

underfitting

both the training and testing errors are very high because the model has insufficiently learned all the info from the data

solution: train for longer, with more data

overfitting

as more training goes on, the training error keeps decreasing and the test error suddenly starts to grow larger

essentially the model begins to "learn the noise" of the training dataset and HYPERFITS to it

the hyperfit makes the model BRITTLE with relation to testing and real-world data generalization

a simple learning problem

given: a sinusoidal function***

goal: approximate the sinusoid via your ML model

when H(0): h(x) = b, a constant, use the mean to approx. the sinusoid. this returns error across the sinusoid = 0.5

enrich the space of hypothesis! when H(0): h(x) = ax+b, the linear trendline cuts diagonally across the sinusoid. this returns error across the sinusoid = 0.2

and so on. BUT....

...in ML, you don't have the actual sinusoid*** – only a set of datapoints along that sinusoid f that give you an approx. picture of it

so training your model to a given training dataset equates to enriching/increasing the hypothesis space/complexity of the hypothesis as above

plotting training, testing, and generalized error v. hypothesis richness gives you a visualization of the underfit/overfitted model status

error v. richness for training and testing errors indicate well-fitted modeling as long as both graphs are still decreasing, even if the gap between the two grows

"sweet spot" that divides under- and overfitting occurs at the minimum of the testing error v. richness graph

going back to the constant/linear trendline hypothesis H(0): h(x) = b

taking many different hypotheses with random y-points in the dataset gives you various trendline models across the sinusoidal function

all these hypotheses average to about the same mean of the function above

so if you don't have the sinusoid f and you can't find H(0): h(x) = b from the get-go, this is how you find that average

same goes for the enriched linear hypothesis H(0): h(x) = ax + b

actually, this can only be done with a simulation in which you have multiple datasets from which to derive multiple random hypotheses that you then average. You still can't do this in the real world.

the problem of ML returns to the fact that you only have a dataset of points along some function f and not f itself. so essentially what you're doing is attempting to find the optimal H(0) for a particular richness level (constant, linear, etc) through generation of multiple random H(0)'s and averages/measures of their error

bias and variance

bias and variance are the sort-of-equivalent

low variance == precision | low bias == accuracy

the bias + variance decomposition tradeoff: Eout(x) = var(x) + bias(x)

high-variance, low-bias visualizations of a model for a function f are hard to create – imagine a 15th order polynomial made to each dataset in your data

the various 'best' hypotheses based on each dataset would look very different to one another

match the model complexity to the dataset, not the target complexity

conceptualization of the variance-bias trade-off

a mismatch between the complexity of H(0) and that of the unknown f necessarily creates some bias no matter how well your H(0) fits your dataset

on the other hand, a very complex H(0) could include f in its range, but because its range is so large, the variance is high

thus, the variance+bias trade-off

#machine #notes

[perfectlytinyworkspace]

Epigenetics

epigenetics: genomic elements that do not affect basic nucleotide sequence of DNA itself

histone modification [explained in previous notes here]

DNA methylation at 5'C of cytosine (cytosine » 5methylatedC or 5mC) in 5'-CpG-3' and complementary 5'-GpC-3' island clump motifs

DNA methylation

methylation generally co-occurs with promoters in non-coding intronic areas to repress expression

general theory is that either a) the TF-binding site is blocked by methylation or b) methylation increases likelihood of histone incorporation

methylation in exons can either up- or downregulate expression; usually UPregulated (reasons are more poorly understood)

evolutionarily, CpG and CHG methylation percentages vary by species – and methylation effects vary by species as well

methylation identification methods

(1) digest DNA » pull down methylated regions via 5mC-specific ABs or natural 5mC-binding proteins » sequence | con: low-resolution results

(2) add sodium bisulfite to DNA, converting non-mC's to uracil » PCR to make U's to T's and non-mC-G's to A's » only 5mC's are preserved, w/ methyl group as protector

(3) the above bisulfite method to the entire genome, whole genome bisulfite sequencing (WGBS) » seq via next-gen sequencing » find % methylation at each C | con: hard to align because of C»T bisulfite alterations

(4) methylation pattern-specific PCR primers to find particular motifs and methylation sites

(5) microarrays as an alternative approach to the same endpoint of (4)

epigenetic marker propagation

i.e., does a daughter cell receive the same epigenetic markers as its parent?

DNA methylation is pretty robust during replication: the parent strand keeps its mC, and the DMNT1 protein copies methylation to the synthesized strand w/ 96% accuracy [methylation degrades over time]

histone marker propagation is less stable, even in the parent cell itself – markers stay for days only, & histones themselves are removed during replication so markers cannot be directly transferred

hypothesized histone marker propagation: a) proximity effect, where old marker goes to new histone after getting kicked off, and b) histone modification enzymes actively add same modifications to new histones during replication

generational epigenetic marker transmission

i.e., does an entire offspring receive the markers of its parent organism? » "genetic memory"

sperm and eggs have their own specific epigenetics, so markers in the adult organism's somatic cells will not transfer to the gametes

sperm themselves package DNA without???histones???

many epigenetic rewrites occur during development anyway to support tissue differentiation, so "genetic memory" would somehow need to persist through this process

HOWEVER: since developmental demethylation and loss of histone modification is NOT uniform, some parental epigenetics may remain

metabolism and trauma are two particular areas where studies yield evidence of generational epigenetic transmissions

EX: famine is transferred to F1 but not F2 from P gen | EX: POW trauma is transferred from father to son & environmentally contingent | EX: Holocaust survivor offspring have OPPOSITE effect to direct epigenetic transmission

how much of the genome is functional?

one question here is: defining "functional" in the first place!

functional: coding regions? informational v. structural? phenotypically impactful? important for survival? this definition affects considerations of % functional genome!

human genome percentages: 1.5% coding, 5% introns, < 1% functional RNAs, ~20% functional (??? purpose)

> 50% of the genome is repetitive sequences that have no informational purpose and simply self-replicate and propagate

onion paradox from reading: organismal complexity does not correlate w/ genome size

#genome #notes

[perfectlytinyworkspace]

Gene Regulation II

chromatin organization

chromatin = DNA + related higher order structural packing

DNA is packed into nucleosomes with tightly-packed, non-accessible areas

use a DNAse sensitivity assay to analyze higher-order chromatin structure

chromatin organizational formatting allows for distal enhancers to be brought to promoter region for increased or decreased gene expression

cellular context

DNA occupies "chromosome territories" in the nucleus, in nuclear lamina

nuclear lamina are clustered into compartment types: accessible and inaccessible

inaccessible compartments include: lamina-associated domains (LADs) and nucleolar-associated domains (NADs)

gene expression regulation via topologically-associated domains (TADs)

a TAD includes a network of a gene, its promoter, and various enhancers that are looped together w/ cohesin proteins

an enhancer in a TAD can regulate the gene of that TAD but not other TADs' genes

Hi-C: a way to map TADs into triangular regions for analysis, that returns the level of interaction between two TADs and the genes within them using color

Hi-C: the varying color and triangular signifiers give you a boundary between two close TADs

mutations that disrupt TAD boundaries can result in an altered phenotype

new enhancers in an altered TAD region (shortened, shifted, or prolonged) have unpredictable effects on the TAD genes and result in ∆phenotype

EX: brachydactyly: branching patterns of hands diverge from the norm

EX: polydacty: more than typical number of fingers

EX: F-syndrome: fingers merge, inverse, and duplicate

histone modifications

methylation, acetylation, phosphorylation of the histone tail

code: Histone Amino_acid Position modification# i.e., H3K4me3 is "histone 3's lysine 4 is tri-methylated

[well-studied modification] active enhancer combo: H3K4me1, H3K27ac

[well-studied modification] active promoter combo: H3K4me3, H3K27ac

[well-studied modification] closed or poised (selectively active) enhancer combo: H3K4me1, H3K27me3

[well-studied modification] primed enhancer: H3K4me1

determining modification frequencies along the genome

(1) tag acetyl, methyl & other modification group linkages w/ specific ABs

(2) run ChiP-seq to find modifications and their frequencies

limitations of DNA/histone modification analyses:

correlation v. causation of modifications (i.e., a mod happens alongside a change in expression but it isn't necessarily the CAUSE of said change)

direction of elicited causations is uncertain (i.e., which one first causes the other is unclear)

ENCODE Project findings

1.5% of human genome is coding; 80% genome gets a hit for regulatory analyses at least once

so...somewhere between 1.5% and 80% of the human genome is functional

ENCODE generated a very useful MAP of modification markers and areas along the genome

#genome #notes

[perfectlytinyworkspace]

Gene Regulation

prokaryote

full description found in genetics notes here

RNAP binding mechanism

operon gene cluster structure

additional notes:

regulatory region is just upstream of promoter

RNAP binds at promoter [signature binding site: -35, -10bp of gene]

positive regulation: enhancer at reg. site increases RNAP binding & transcription after small molecule binding

negative regulation: repressor at reg. site is removed by small molecule binding, which allows RNAP to bind & increase transcription

most of genome is composed of coding DNA

eukaryote

full description found in genetics notes here and here

TATA(+ WAW, where W==T or A) box

enhancer regions – activating & inhibiting

transcription factors (TFs) – activators & repressors w/ short DNA seq [seq logo] binding specificity

additional notes:

regulatory region can be up or downstream, near or far

different TFs' seq logos can be near each other in an enhancer region if they bind together

diff combos of TFs bind together for varying regulation

cohesins [also seen in meiosis] bring far-off enhancers to a promoter in a loop

insulators block certain enhancer-promoter interactions for some genes and facilitate those for others to properly regulate transcription

studying gene regulation: analytical techniques & assays

[RNA-seq]

transcriptome: set of RNA transcripts in cell

utilize RNA seq to determine changes in expression levels (think Collins lab RNA seq purposes)

∆expression levels can then tell you info about gene regulation alterations

RNA-seq general process:

gene » mRNA transcripts, so exons only » sequence RNA fragments

get double-stranded cDNA (ds cDNA) » align exons

different #s of reads generate a relative expression rate for a gene via RPKM calculations

reads per kilo base per million mapped reads RPKM = total exon reads / (mapped reads • exon length)

however! this leaves info about the actual enhancer region out, so utilize chromatin immunoprecipitation followed by deep sequencing (CHIP-seq) and chromatin interaction analysis with paired-end tag sequencing CHIA-PET

[CHIP-seq & ChIA-PET]

CHIP-seq general process:

cross-link signaling proteins to DNA of interest [enhancer region]

shear DNA into 200-600 bp fragments

add signaling protein-specific antibodies and let bind

conduct immunoprecipitation step: purify anything bound to the AB

after purification, reverse signal protein-DNA crosslink & align purified sequence to genome to locate enhancer region

ChIA-PET general process:

fix active enhancer/promoter interaction site & all bound TFs

shear the DNA loop and ligate all loose ends » sequence all DNA

find spikes in sequencing to get pol II and enhancer binding seq and genomic locations

[SELEX]

finds an unknown TF-binding motif

for a TF with an unknown binding site:

generate mutant library of potential binding sites

select all fragments that are able to bind the TF

sequence those fragments & conduct PCR to add them again to the library

thus, find the motif that the TF binds to

#genome #notes