Robust Algorithms for Detecting Hidden Structure in Biological Data

Biological data, such as molecular abundance measurements and protein sequences, harbor complex hidden structure that reflects its underlying biological mechanisms. For example, high-throughput abundance measurements provide a snapshot the global state of a living cell, while homologous protein sequences encode the residue-level logic of the proteins\u27 function and provide a snapshot of the evolutionary trajectory of the protein family. In this work I describe algorithmic approaches and analysis software I developed for uncovering hidden structure in both kinds of data. Clustering is an unsurpervised machine learning technique commonly used to map the structure of data collected in high-throughput experiments, such as quantification of gene expression by DNA microarrays or short-read sequencing. Clustering algorithms always yield a partitioning of the data, but relying on a single partitioning solution can lead to spurious conclusions. In particular, noise in the data can cause objects to fall into the same cluster by chance rather than due to meaningful association. In the first part of this thesis I demonstrate approaches to clustering data robustly in the presence of noise and apply robust clustering to analyze the transcriptional response to injury in a neuron cell. In the second part of this thesis I describe identifying hidden specificity determining residues (SDPs) from alignments of protein sequences descended through gene duplication from a common ancestor (paralogs) and apply the approach to identify numerous putative SDPs in bacterial transcription factors in the LacI family. Finally, I describe and demonstrate a new algorithm for reconstructing the history of duplications by which paralogs descended from their common ancestor. This algorithm addresses the complexity of such reconstruction due to indeterminate or erroneous homology assignments made by sequence alignment algorithms and to the vast prevalence of divergence through speciation over divergence through gene duplication in protein evolution

En quête des mystères de la mémoire sensorielle : manipuler la mémoire affective. Le cas de « Sauvée » de Guy de Maupassant

As a fine observer of human psychology, Maupassant now goes even further. He plunges into the unconscious and the mystery of psyche. Maupassant’s inquiry is at work in the short story “Sauvée” narrating an episode of triggering involuntary sensory memory. The author leads us to an examination of the mechanics of forced sensual memory retrieval as a manoeuvre used in a manipulative seduction scheme. The amusing effect of the seemingly simple anecdotal storyline conceals the depth of meaning that surpasses the simplicity of the anecdote. This article discusses the way in which the provoked memory recollection scenes are depicted, by looking at how meaning is conveyed through a network of various psycho-sociocultural ‘factors’ at play: the sphere of senses involving sensory and sensual systems participation, impulsive behaviour, sociocultural context and the influence of relevant scientific theories known in 19th century France, while Maupassant’s writing style and technique give the text its complexity.Connaisseur de la psychologie humaine, Maupassant va plus loin encore : il se lance dans l’exploration de l’inconscient en s’interrogeant sur des zones obscures du psychisme. Le conte « Sauvée » inscrit ce questionnement au cœur de la représentation de la mémoire sensorielle involontaire. Le récit mettant en intrigue le ressouvenir sensuel, sollicité à des fins de séduction manipulatrice, l’effet anecdotique de l’aventure rocambolesque masque des niveaux de complexité du sens dépassant la légèreté de l’anecdote. Cet article examine la façon dont le texte travaille les scènes de ressouvenir qui s’articulent en interrelation entre la sphère des sens, le sensoriel et le sensuel, le comportement impulsif, les facteurs socioculturels et l’actualité scientifique, formant un réseau complexe dont les modalités de l’écriture maupassantienne approfondissent le sens

Category learning in a dynamic world

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The Role of Words in Cognitive Tasks: What, When, and How?

The current review focuses on how exposure to linguistic input, and count nouns in particular, affect performance on various cognitive tasks, including individuation, categorization and category learning, and inductive inference. We review two theoretical accounts of effects of words. Proponents of one account argue that words have top-down effects on cognitive tasks, and, as such, function as supervisory signals. Proponents of the other account suggest that early in development, words, just like any other perceptual feature, are first and foremost part of the stimulus input and influence cognitive tasks in a bottom-up, non-supervisory fashion. We then review evidence supporting each account. We conclude that, although much research is needed, there is a large body of evidence indicating that words start out like other perceptual features and become supervisory signals in the course of development

Timing matters:The impact of label synchrony on infant categorisation

The impact of labelling on infant visual categorisation has yielded contradictory outcomes. Some findings indicate a beneficial role while others point to interference effects in the presence of labels. The locus of these divergent outcomes is largely unclear. We explore the hypothesis that the timing of the label is of crucial importance, proposing that synchronous presentation of words and objects induces a higher processing load than asynchronous presentation (image onset before labelling) A novelty preference experiment with 12-month-olds reveals that synchronous presentation leads to a diminished preference for a novel object on test in comparison to asynchronous labelling, suggesting a detrimental impact on category learning. However, analyses of infants' gaze patterns to object parts reveal that even synchronous labels do not hinder learning completely. We conclude that synchronous labels interfere with the familiarisation process, but this process involves shifts in familiarity vs. novelty preference rather than overshadowing of visual learning. Besides offering detailed insight into the effects of labelling on infants' visual attention, these findings offer the potential to reconcile previous contradictory results. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Labels constructively shape object categories in 10-month-old infants

How do infants' emerging language abilities affect their organization of objects into categories? The question of whether labels can shape the early perceptual categories formed by young infants has received considerable attention, but evidence has remained inconclusive. Here, 10-month-old infants (N = 80) were familiarized with a series of morphed stimuli along a continuum that can be seen as either one category or two categories. Infants formed one category when the stimuli were presented in silence or paired with the same label, but they divided the stimulus set into two categories when half of the stimuli were paired with one label and half with another label. Pairing the stimuli with two different nonlinguistic sounds did not lead to the same result. In this case, infants showed evidence for the formation of a single category, indicating that nonlinguistic sounds do not cause infants to divide a category. These results suggest that labels and visual perceptual information interact in category formation, with labels having the potential to constructively shape category structures already in preverbal infants, and that nonlinguistic sounds do not have the same effect

Minimal model of associative learning for cross-situational lexicon acquisition

An explanation for the acquisition of word-object mappings is the associative learning in a cross-situational scenario. Here we present analytical results of the performance of a simple associative learning algorithm for acquiring a one-to-one mapping between $N$ objects and $N$ words based solely on the co-occurrence between objects and words. In particular, a learning trial in our learning scenario consists of the presentation of $C + 1 < N$ objects together with a target word, which refers to one of the objects in the context. We find that the learning times are distributed exponentially and the learning rates are given by $\ln{[\frac{N(N-1)}{C + (N-1)^{2}}]}$ in the case the $N$ target words are sampled randomly and by $\frac{1}{N} \ln [\frac{N-1}{C}]$ in the case they follow a deterministic presentation sequence. This learning performance is much superior to those exhibited by humans and more realistic learning algorithms in cross-situational experiments. We show that introduction of discrimination limitations using Weber's law and forgetting reduce the performance of the associative algorithm to the human level

Feature biases in early word learning : network distinctiveness predicts age of acquisition

Do properties of a word’s features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures; words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects’ distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning

Experience and maturation : The contribution of co-occurrence regularities in language to the development of semantic organization

With development knowledge becomes organized according to semantic links, including early-developing associative (e.g., juicy-apple) and gradually developing taxonomic links (e.g., apple-pear). Word co-occurrence regularities may foster these links: Associative links may form from direct co-occurrence (e.g., juicy-apple), and taxonomic links from shared co-occurrence (e.g., apple and pear co-occur with juicy). Four experiments (2017-2020) investigated this possibility with 4- to 8-year-olds (N = 148, 82 female) and adults (N = 116, 35 female) in a U.S. city with 58.6% White; 29.0% Black, and 5.8% Asian demographics. Results revealed earlier development of the abilities to form direct (ds > 0.536) than the abilities to form shared co-occurrence-based links (ds > 1.291). We argue that the asynchronous development of abilities to form co-occurrence-based links may explain developmental changes in semantic organization