Automatic Image Captioning with Style

This thesis connects two core topics in machine learning, vision and language. The problem of choice is image caption generation: automatically constructing natural language descriptions of image content. Previous research into image caption generation has focused on generating purely descriptive captions; I focus on generating visually relevant captions with a distinct linguistic style. Captions with style have the potential to ease communication and add a new layer of personalisation. First, I consider naming variations in image captions, and propose a method for predicting context-dependent names that takes into account visual and linguistic information. This method makes use of a large-scale image caption dataset, which I also use to explore naming conventions and report naming conventions for hundreds of animal classes. Next I propose the SentiCap model, which relies on recent advances in artificial neural networks to generate visually relevant image captions with positive or negative sentiment. To balance descriptiveness and sentiment, the SentiCap model dynamically switches between two recurrent neural networks, one tuned for descriptive words and one for sentiment words. As the first published model for generating captions with sentiment, SentiCap has influenced a number of subsequent works. I then investigate the sub-task of modelling styled sentences without images. The specific task chosen is sentence simplification: rewriting news article sentences to make them easier to understand. For this task I design a neural sequence-to-sequence model that can work with limited training data, using novel adaptations for word copying and sharing word embeddings. Finally, I present SemStyle, a system for generating visually relevant image captions in the style of an arbitrary text corpus. A shared term space allows a neural network for vision and content planning to communicate with a network for styled language generation. SemStyle achieves competitive results in human and automatic evaluations of descriptiveness and style. As a whole, this thesis presents two complete systems for styled caption generation that are first of their kind and demonstrate, for the first time, that automatic style transfer for image captions is achievable. Contributions also include novel ideas for object naming and sentence simplification. This thesis opens up inquiries into highly personalised image captions; large scale visually grounded concept naming; and more generally, styled text generation with content control

Novel computational methods for studying the role and interactions of transcription factors in gene regulation

Regulation of which genes are expressed and when enables the existence of different cell types sharing the same genetic code in their DNA. Erroneously functioning gene regulation can lead to diseases such as cancer. Gene regulatory programs can malfunction in several ways. Often if a disease is caused by a defective protein, the cause is a mutation in the gene coding for the protein rendering the protein unable to perform its functions properly. However, protein-coding genes make up only about 1.5% of the human genome, and majority of all disease-associated mutations discovered reside outside protein-coding genes. The mechanisms of action of these non-coding disease-associated mutations are far more incompletely understood. Binding of transcription factors (TFs) to DNA controls the rate of transcribing genetic information from the coding DNA sequence to RNA. Binding affinities of TFs to DNA have been extensively measured in vitro, ligands by exponential enrichment) and Protein Binding Microarrays (PBMs), and the genome-wide binding locations and patterns of TFs have been mapped in dozens of cell types. Despite this, our understanding of how TF binding to regulatory regions of the genome, promoters and enhancers, leads to gene expression is not at the level where gene expression could be reliably predicted based on DNA sequence only. In this work, we develop and apply computational tools to analyze and model the effects of TF-DNA binding. We also develop new methods for interpreting and understanding deep learning-based models trained on biological sequence data. In biological applications, the ability to understand how machine learning models make predictions is as, or even more important as raw predictive performance. This has created a demand for approaches helping researchers extract biologically meaningful information from deep learning model predictions. We develop a novel computational method for determining TF binding sites genome-wide from recently developed high-resolution ChIP-exo and ChIP-nexus experiments. We demonstrate that our method performs similarly or better than previously published methods while making less assumptions about the data. We also describe an improved algorithm for calling allele-specific TF-DNA binding. We utilize deep learning methods to learn features predicting transcriptional activity of human promoters and enhancers. The deep learning models are trained on massively parallel reporter gene assay (MPRA) data from human genomic regulatory elements, designed regulatory elements and promoters and enhancers selected from totally random pool of synthetic input DNA. This unprecedentedly large set of measurements of human gene regulatory element activities, in total more than 100 times the size of the human genome, allowed us to train models that were able to predict genomic transcription start site positions more accurately than models trained on genomic promoters, and to correctly predict effects of disease-associated promoter variants. We also found that interactions between promoters and local classical enhancers are non-specific in nature. The MPRA data integrated with extensive epigenetic measurements supports existence of three different classes of enhancers: classical enhancers, closed chromatin enhancers and chromatin-dependent enhancers. We also show that TFs can be divided into four different, non-exclusive classes based on their activities: chromatin opening, enhancing, promoting and TSS determining TFs. Interpreting the deep learning models of human gene regulatory elements required application of several existing model interpretation tools as well as developing new approaches. Here, we describe two new methods for visualizing features and interactions learned by deep learning models. Firstly, we describe an algorithm for testing if a deep learning model has learned an existing binding motif of a TF. Secondly, we visualize mutual information between pairwise k-mer distributions in sample inputs selected according to predictions by a machine learning model. This method highlights pairwise, and positional dependencies learned by a machine learning model. We demonstrate the use of this model-agnostic approach with classification and regression models trained on DNA, RNA and amino acid sequences.Monet eliöt koostuvat useista erilaisista solutyypeistä, vaikka kaikissa näiden eliöiden soluissa onkin sama DNA-koodi. Geenien ilmentymisen säätely mahdollistaa erilaiset solutyypit. Virheellisesti toimiva säätely voi johtaa sairauksiin, esimerkiksi syövän puhkeamiseen. Jos sairauden aiheuttaa viallinen proteiini, on syynä usein mutaatio tätä proteiinia koodaavassa geenissä, joka muuttaa proteiinia siten, ettei se enää pysty toimittamaan tehtäväänsä riittävän hyvin. Kuitenkin vain 1,5 % ihmisen genomista on proteiineja koodaavia geenejä. Suurin osa kaikista löydetyistä sairauksiin liitetyistä mutaatioista sijaitsee näiden ns. koodaavien alueiden ulkopuolella. Ei-koodaavien sairauksiin liitetyiden mutaatioiden vaikutusmekanismit ovat yleisesti paljon huonommin tunnettuja, kuin koodaavien alueiden mutaatioiden. Transkriptiotekijöiden sitoutuminen DNA:han säätelee transkriptiota, eli geeneissä olevan geneettisen informaation lukemista ja muuntamista RNA:ksi. Transkriptiotekijöiden sitoutumista DNA:han on mitattu kattavasti in vitro-olosuhteissa, ja monien transkriptiotekijöiden sitoutumiskohdat on mitattu genominlaajuisesti useissa eri solutyypeissä. Tästä huolimatta ymmärryksemme siitä miten transkriptioitekijöiden sitoutuminen genomin säätelyelementteihin, eli promoottoreihin ja vahvistajiin, johtaa geenien ilmentymiseen ei ole sellaisella tasolla, että voisimme luotettavasti ennustaa geenien ilmentymistä pelkästään DNA-sekvenssin perusteella. Tässä työssä kehitämme ja sovellamme laskennallisia työkaluja transkriptiotekijöiden sitoutumisesta johtuvan geenien ilmentymisen analysointiin ja mallintamiseen. Kehitämme myös uusia menetelmiä biologisella sekvenssidatalla opetettujen syväoppimismallien tulkitsemiseksi. Koneoppimismallin tekemien ennusteiden ymmärrettävyys on biologisissa sovelluksissa yleensä yhtä tärkeää, ellei jopa tärkeämpää kuin pelkkä raaka ennustetarkkuus. Tämä on synnyttänyt tarpeen uusille menetelmille, jotka auttavat tutkijoita louhimaan biologisesti merkityksellistä tietoa syväoppimismallien ennusteista. Kehitimme tässä työssä uuden laskennallisen työkalun, jolla voidaan määrittää transkriptiotekijöiden sitoutumiskohdat genominlaajuisesti käyttäen mittausdataa hiljattain kehitetyistä korkearesoluutioisista ChIP-exo ja ChIP-nexus kokeista. Näytämme, että kehittämämme menetelmä suoriutuu paremmin, tai vähintään yhtä hyvin kuin aiemmin julkaistut menetelmät tehden näitä vähemmän oletuksia signaalin muodosta. Esittelemme myös parannellun algoritmin transkriptiotekijöiden alleelispesifin sitoutumisen määrittämiseksi. Käytämme syväoppimismenetelmiä oppimaan mitkä ominaisuudet ennustavat ihmisen promoottori- ja voimistajaelementtien aktiivisuutta. Nämä syväoppimismallit on opetettu valtavien rinnakkaisten reportterigeenikokeiden datalla ihmisen genomisista säätelyelementeistä, sekä aktiivisista promoottoreista ja voimistajista, jotka ovat valikoituneet satunnaisesta joukosta synteettisiä DNA-sekvenssejä. Tämä ennennäkemättömän laaja joukko mittauksia ihmisen säätelyelementtien aktiivisuudesta - yli satakertainen määrä DNA sekvenssiä ihmisen genomiin verrattuna - mahdollisti transkription aloituskohtien sijainnin ennustamisen ihmisen genomissa tarkemmin kuin ihmisen genomilla opetetut mallit. Nämä mallit myös ennustivat oikein sairauksiin liitettyjen mutaatioiden vaikutukset ihmisen promoottoreilla. Tuloksemme näyttivät, että vuorovaikutukset ihmisen promoottorien ja klassisten paikallisten voimistajien välillä ovat epäspesifejä. MPRA-data, integroituna kattavien epigeneettisten mittausten kanssa mahdollisti voimistajaelementtien jaon kolmeen luokkaan: klassiset, suljetun kromatiinin, ja kromatiinista riippuvat voimistajat. Tutkimuksemme osoitti, että transkriptiotekijät voidaan jakaa neljään, osittain päällekkäiseen luokkaan niiden aktiivisuuksien perusteella: kromatiinia avaaviin, voimistaviin, promotoiviin ja transkription aloituskohdan määrittäviin transkriptiotekijöihin. Ihmisen genomin säätelyelementtejä kuvaavien syväoppimismallien tulkitseminen vaati sekä olemassa olevien menetelmien soveltamista, että uusien kehittämistä. Kehitimme tässä työssä kaksi uutta menetelmää syväoppimismallien oppimien muuttujien ja niiden välisten vuorovaikutusten visualisoimiseksi. Ensin esittelemme algoritmin, jonka avulla voidaan testata onko syväoppimismalli oppinut jonkin jo tunnetun transkriptiotekijän sitoutumishahmon. Toiseksi, visualisoimme positiokohtaisten k-meerijakaumien keskeisinformaatiota sekvensseissä, jotka on valittu syväoppimismallin ennusteiden perusteella. Tämä menetelmä paljastaa syväoppimismallin oppimat parivuorovaikutukset ja positiokohtaiset riippuvuudet. Näytämme, että kehittämämme menetelmä on mallin arkkitehtuurista riippumaton soveltamalla sitä sekä luokittelijoihin, että regressiomalleihin, jotka on opetettu joko DNA-, RNA-, tai aminohapposekvenssidatalla

gene regulatory element prediction with bayesian networks

Ph.DDOCTOR OF PHILOSOPH

Integration and visualisation of clinical-omics datasets for medical knowledge discovery

In recent decades, the rise of various omics fields has flooded life sciences with unprecedented amounts of high-throughput data, which have transformed the way biomedical research is conducted. This trend will only intensify in the coming decades, as the cost of data acquisition will continue to decrease. Therefore, there is a pressing need to find novel ways to turn this ocean of raw data into waves of information and finally distil those into drops of translational medical knowledge. This is particularly challenging because of the incredible richness of these datasets, the humbling complexity of biological systems and the growing abundance of clinical metadata, which makes the integration of disparate data sources even more difficult. Data integration has proven to be a promising avenue for knowledge discovery in biomedical research. Multi-omics studies allow us to examine a biological problem through different lenses using more than one analytical platform. These studies not only present tremendous opportunities for the deep and systematic understanding of health and disease, but they also pose new statistical and computational challenges. The work presented in this thesis aims to alleviate this problem with a novel pipeline for omics data integration. Modern omics datasets are extremely feature rich and in multi-omics studies this complexity is compounded by a second or even third dataset. However, many of these features might be completely irrelevant to the studied biological problem or redundant in the context of others. Therefore, in this thesis, clinical metadata driven feature selection is proposed as a viable option for narrowing down the focus of analyses in biomedical research. Our visual cortex has been fine-tuned through millions of years to become an outstanding pattern recognition machine. To leverage this incredible resource of the human brain, we need to develop advanced visualisation software that enables researchers to explore these vast biological datasets through illuminating charts and interactivity. Accordingly, a substantial portion of this PhD was dedicated to implementing truly novel visualisation methods for multi-omics studies.Open Acces

Selected Works in Bioinformatics

This book consists of nine chapters covering a variety of bioinformatics subjects, ranging from database resources for protein allergens, unravelling genetic determinants of complex disorders, characterization and prediction of regulatory motifs, computational methods for identifying the best classifiers and key disease genes in large-scale transcriptomic and proteomic experiments, functional characterization of inherently unfolded proteins/regions, protein interaction networks and flexible protein-protein docking. The computational algorithms are in general presented in a way that is accessible to advanced undergraduate students, graduate students and researchers in molecular biology and genetics. The book should also serve as stepping stones for mathematicians, biostatisticians, and computational scientists to cross their academic boundaries into the dynamic and ever-expanding field of bioinformatics

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy

Discovering discriminative and class-specific sequence and structural motifs in proteins

Finding recurring motifs is an important problem in bioinformatics. Such motifs can be used for any number of problems including sequence classi cation, label prediction, knowledge discovery and biological engineering of proteins t for a speci c purpose. Our motivation is to create a better foundation for the research and development of novel motif mining and machine learning methods that can extract class-speci c and discriminative motifs using both sequence and structural features. We propose the building blocks of a general machine learning framework to act on a biological input. This thesis present a combination of elements that are aimed to be applicable to a variety of biological problems. Ideally, the learner should only require a number of biological data instances as input that are classi- ed into a number of di erent classes as de ned by the researchers. The output should be the factors and motifs that discriminate between those classes (for reasonable, non-random class de nitions). This ideal work ow requires two main steps. First step is the representation of the biological input with features that contain the signi cant information the researcher is looking for. Due to the complexity of the macromolecules, abstract representations are required to convert the real world representation into quanti able descriptors that are suitable for motif mining and machine learning. The second step of the proposed work ow is the motif mining and knowledge discovery step. Using these informative representations, an algorithm should be able to nd discriminative, class-speci c motifs that are over-represented in one class and under-represented in the other. This thesis presents novel procedures for representation of the proteins to be used in a variety of machine learning algorithms, and two separate motif mining algorithms, one based on temporal motif mining, and the other on deep learning, that can work with the given biological data. The descriptors and the learners are applied to a wide range of computational problems encountered in life sciences

Profiling patterns of interhelical associations in membrane proteins.

A novel set of methods has been developed to characterize polytopic membrane proteins at the topological, organellar and functional level, in order to reduce the existing functional gap in the membrane proteome. Firstly, a novel clustering tool was implemented, named PROCLASS, to facilitate the manual curation of large sets of proteins, in readiness for feature extraction. TMLOOP and TMLOOP writer were implemented to refine current topological models by predicting membrane dipping loops. TMLOOP applies weighted predictive rules in a collective motif method, to overcome the inherent limitations of single motif methods. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity and 1,392 topological models described in the Swiss-Prot database were refined. The subcellular location (TMLOCATE) and molecular function (TMFUN) prediction methods rely on the TMDEPTH feature extraction method along data mining techniques. TMDEPTH uses refined topological models and amino acid sequences to calculate pairs of residues located at a similar depth in the membrane. Evaluation of TMLOCATE showed a normalized accuracy of 75% in discriminating between proteins belonging to the main organelles. At a sequence similarity threshold of 40%, TMFLTN predicted main functional classes with a sensitivity of 64.1-71.4%) and 70% of the olfactory GPCRs were correctly predicted. At a sequence similarity threshold of 90%, main functional classes were predicted with a sensitivity of 75.6-92.8%) and class A GPCRs were sub-classified with a sensitivity of 84.5%>-92.9%. These results reflect a direct association between the spatial arrangement of residues in the transmembrane regions and the capacity for polytopic membrane proteins to carry out their functions. The developed methods have for the first time categorically shown that the transmembrane regions hold essential information associated with a wide range of functional properties such as filtering and gating processes, subcellular location and molecular function

Deep Neural Networks on Genetic Motif Discovery: the Interpretability and Identifiability Issues

Deep neural networks have made great success in a wide range of research fields and real-world applications. However, as a black-box model, the drastic advances in the performance come at the cost of model interpretability. This becomes a big concern especially for domains that are safety-critical or have ethical and legal requirements (e.g., avoiding algorithmic discrimination). In other situations, interpretability might be able to help scientists gain new ``knowledge'' that is learnt by the neural networks (e.g., computational genomics), and neural network based genetic motif discovery is such a field. It naturally leads us to another question: Can current neural network based motif discovery methods identify the underlying motifs from the data? How robust and reliable is it? In other words, we are interested in the motif identifiability problem. In this thesis, we first conduct a comprehensive review of the current neural network interpretability research, and propose a novel unified taxonomy which, to the best of our knowledge, provides the most comprehensive and clear categorisation of the existing approaches. Then we formally study the motif identifiability problem in the context of neural network based motif discovery i.e., if we only have access to the predictive performance of a neural network, which is a black-box, how well can we recover the underlying ``true'' motifs by interpreting the learnt model). Systematic controlled experiments show that although accurate models tend to recover the underlying motifs better, the motif identifiability (a measure of the similarity between true motifs and learnt motifs) still varies in a large range. Also, the over-complexity (without overfitting) of a high-accuracy model (e.g., using 128 kernels while 16 kernels are already good enough) may be harmful to the motif identifiability. We thus propose a robust neural network based motif discovery workflow addressing above issues, which is verified on both synthetic and real-world datasets. Finally, we propose probabilistic kernels in place of conventional convolutional kernels and study whether it would be better to directly learn probabilistic motifs in the neural networks rather than post hoc interpretation. Experiments show that although probabilistic kernels have some merits (e.g., stable output), their performance is not comparable to classic convolutional kernels under the same network setting (the number of kernels)