Abstract

India’s cooling demands are growing exponentially with rapid urbanization, increase in global warming due to change in climate and an increase in affordability and aspiration levels of rising number of people. Due to the availability of limited energy resources, meeting these increasing cooling demands is a big challenge. A mixed-mode building can operate both as a naturally ventilated building and air-conditioned (AC) building. It allows natural ventilation (NV) when the outside weather conditions are favorable and deploys air-conditioning when natural ventilation is not able to provide sufficient comfort. A well-designed mixed-mode building can prove to be one of the solutions to address this challenge. Therefore, there is a need to evaluate the energy saving potential if the building is operated in mixed-mode. Also, the current practice is to have static setpoint throughout the summers which is not energy-efficient. Based on the recent studies on adaptive thermal comfort, more energy savings can be achieved further if this setpoint can change according to outdoor conditions. In this thesis, a simulation-based study has been done to evaluate the potential of operating buildings in mixed-mode for five Indian climatic zones according to Energy Conservation Building Code (ECBC) 2017 (India) – hot-dry, warm-humid, composite, temperate and cold. Five representative cities were selected for each climatic zone namely Ahmedabad, Kolkata, Lucknow, Bangalore and Srinagar respectively. Initially, a methodology for climate potential of natural ventilation has been applied and results have been discussed. Two adaptive thermal comfort models of ASHRAE:55 Adaptive Model of Thermal Comfort (ASHRAE ATC) and Indian Model of Adaptive Comfort (IMAC) have been used to calculate the favourability of natural ventilation for the five climatic zones of India. Bangalore in temperate climate was observed to be the most suitable with almost 60% of hours favouring natural ventilation from a climatic point of view using IMAC. On the other hand, Kolkata was observed to be the least suitable with only 35% of hours in favour of natural ventilation. To estimate the cooling energy savings, a model for the mixed-mode building has been proposed and simulated in EnergyPlus for all the five locations for two indoor operative thermostat (OT) setpoint controls – fixed thermostat of 26℃ and adaptive thermostat based on IMAC. While evaluating the energy consumption of mixed-mode building, care has been taken to evaluate the sensitivity of NV to wind velocity. This is because weather files are developed by taking the values available at the airport and those wind velocities might not be the same in densely populated parts of the cities. Results have shown that significant cooling energy saving potential is present in both temperate and cold climate. Potential energy saving of 60% is obtainable for Srinagar (cold), 58% in Bangalore (temperate), 29% in Lucknow (composite), 26% in Ahmedabad (hot-dry) and 17% in Kolkata (Warm-humid) when deploying adaptive OT thermostat control according to IMAC Model. Since adaptive model offers higher flexibility range, potential for energy savings using an adaptive model is greater than a fixed thermostat model. A methodology is also proposed to enable real-time control of operable windows for mixed mode ventilation building. The methodology consists of using real-time weather forecast and predicting the internal loads to estimate the working of windows and correspondingly the mechanical cooling system, for the next timestep. The model is simulated in natural ventilation mode for the next timestep and checked if there is potential to save cooling energy due to inside conditions. Windows remain open if the inside conditions remain favourable, otherwise the mechanical cooling system is scheduled to ON and this repeats for the next window. The methodology has been shown using AERIS Weather Data for hourly forecast of weather parameters and deploying Seasonal Autoregressive Integrated Moving Average (SARIMA) Models to model a day-ahead prediction for the lighting load, electric load and the occupancy profiles. Co-simulation of EnergyPlus is carried out with Building Controls Virtual Test Bed (BCVTB) to enable real-time simulation wherein; the EnergyPlus weather file parameters and input building loads are updated every hour. Based on the indoor and outdoor conditions, EnergyPlus calculates the schedule for opening/closing of windows. A simulation-based case study for Hyderabad, India is performed to demonstrate the working of the proposed methodology. Reduction of 25% in cooling energy demand was estimated in a mixed mode building as compared to the fully air-conditioned building during the study period.

Abstract

Installation, deployment and maintenance of an NLP system, can be a daunting dask; based on the number and complexity of the components involved. One such system can be a hybrid Machine Translation system, composed of several modules which define the transformation of a given word, phrase or sentence, from one language to another. The end users of such a system can be developers themselves who want to improve it. To achieve this, the system as whole, needs to adopt an architecture which lets the users control and change the order of components in the pipeline, be able to intervene the execution in middle to debug one or more components, tweak inputs/outputs without having to rewrite the components, be able to easily replicate the system on their local box without having to worry about the hassle of compiling everything from source, be able to quickly replace a component with a higher or lower version and be able to see the impact on the final result quickly; basically make development iterative and fast. The system should also expose an interface for those users who want to build something on top of it, without having to worry about the internal details. The ideas proposed in this thesis try to cater to the needs of a broad category of users, in an attempt to keep their work-flow as simple as possible. We propose an architecture where we show how to identify and transform a monolithic system into small, individual components (each being a linguistic unit), identify bottlenecks from an operating system’s point of view, identify scalable components and finally provide an easy mechanism to interact with the system. To achieve this, we apply the proposed architecture over an existing system (Sampark MT System) and walk through it’s transformation. Towards the end, we show the creation of a web client which shows how easy it becomes to interact with the modified system. We also apply our proposition to show that it can be applied to any pipeline based system by thinking of it as a disconnected, directed acyclic graph. We also show how the modified system can be deployed on the cloud easily and how individual components can be scaled up or down as per needs. To be able to plan the overall architecture and produce guidelines for enabling large scale collaborative development, a structured systems analysis and design, from the point of view of both, a computational linguist and a systems engineer is required. This thesis provides the foundation in that direction by enhancing an existing system, reducing the overall runtime of it’s components by greater than 85%, improving the test-dev-deploy cycle for computational linguists and discussing a generalized architecture on top of which, further complex systems can be built for specific purposes.

Abstract

Systems biology focuses on the integration of experimental, mathematical and computational techniques to develop systemic views and predictive models of biological systems. Transcriptomics has enabled to obtain genome-wide measurements in health and disease. The analysis of transcriptomic data in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays. Biological network reconstructions are important tools in systems biology in order to model the behavior as a whole biological system. Biological networks can also be used as a scaffold for integrative analysis where high throughput data from different conditions can be integrated into the biological network to obtain condition-specific molecular biomarkers and mechanisms. In this thesis, we aimed to gain a systems-level understanding of two obesity-linked metabolic disorders: Non-alcoholic fatty liver disease (NAFLD) and Type 2 Diabetes (T2D) using transcriptomics data obtained from healthy and disease groups. NAFLD is a complex spectrum of diseases ranging from simple steatosis to Non-Alcoholic Steato hepatitis (NASH) with fibrosis, which can progress to cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD is complex, involving crosstalk between multiple organs, cell-types, and environmental and genetic factors. Dysfunction of the adipose tissue plays a central role in NAFLD progression. Here, we analysed transcriptomics data obtained from the Visceral Adipose Tissue (VAT) of NAFLD patients to understand how the VAT metabolism is altered at the genome scale and co regulated with other cellular processes during the progression from obesity to NASH with fibrosis. For this purpose, we constructed weighted gene co-expression network and studied the organization of the disease transcriptome into functional modules of cellular processes and pathways. Our analysis revealed the coordination of metabolic and inflammatory modules (termed ”immunometabolism”) in the VAT of NAFLD patients. We found that genes of arachidonic acid, sphingolipid and glycosphingolipid metabolism were upregulated and co-expressed with genes of proinflammatory signalling pathways and hypoxia in NASH/NASH with fibrosis. We hypothesize that these metabolic alterations might play a role in sustaining VAT inflammation. Furthermore, immunometabolism related genes were also coexpressed with genes involved in Extracellular Matrix (ECM) degradation. Our analysis indicates that upregulation of both ECM degrading enzymes and their inhibitors (incoherent feedforward loop) poten- tially leads to the ECM deposition in the VAT of NASH with fibrosis patients.Further, we studied the pancreatic islets in health and T2D. Studies on how pancreatic islets respond under physiological and pathological conditions are obtained mostly based on the analysis of whole islet transcriptome. However, the measurement from the whole islets quantifies the average behaviour of dominant cell types, thereby making it difficult to understand the cell-type-specific changes. Recently, the advent of single-cell RNA sequencing (scRNA-seq) technique has generated valuable resource on islet biology and T2D. This provides an opportunity to understand the different cell types/states at both the network and individual gene expression levels. Here, we inferred the gene regulatory networks (GRNs) of pancreatic cells from scRNA-seq data in healthy and T2D. Clustering of cells based on GRNs identifies endocrine and exocrine cells and multiple stable cell states in each alpha, beta and ductal cells. The phenotypic variations in cell states due to obesity and T2D are indistinguishable. Therefore, the trajectory of cells in pseudotime was constructed based on the cell-type-specific gene expression. The analysis shows that continuous spectrum of cell states exists with phenotypic-dependent branching and donor cellcell variability in endocrine and exocrine cell types. We characterized the genes that give rise to bifurcation in the trajectory. Our study demonstrates that the network and trajectory inference approaches can be used to better understand the behaviour of pancreatic cells in health and disease. Overall, this thesis generates tissue- and cell-type specific networks that can be further used to understand the underlying pathology of metabolic disorders.

Abstract

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for phar- macovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. This poses a significant challenge to the existing Natural Language Processing (NLP) and Information Retrieval (IR) methods. In this thesis, we try to address the problem of “Adverse Drug Reaction (ADR) Mention Extraction”, a specific form of pharamacovigilance, from social media. Specifically, we try to address the problem in the setting where we have a limited access to labeled data but potentially unlimited access to unlabeled data, which is often the case in the real world. We investigate the applicability of two of the popular and well-studied approaches for handling such problems: Semi-supervised learning and Multi-task learning.In the first approach, we propose a novel semi-supervised method which utilizes a novel task of “Unsupervised Drug-Name Prediction”, which is the prediction of drug-name from the tweet context where the actual drug-name is identified and masked from the tweet and the objective is to predict the drug-name from the masked tweet. Enforcing the network to predict the drug-name allows it to learn the context around the drug-name mention. Through experiments we demonstrate that pre-training the network with such task helps in improving performance on the main task. In the second work, we make use of more traditional and well-studied semi-supervised method: co-training, for ADR extraction. To the best of our knowledge, we are the first ones to apply a co-training based methods for entity extraction using deep learning. Through experiments we demonstrate that co-training based network gives a significant improvement in the main task as compared to its fully supervised counterpart. In the third work, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task of our interest, i.e., ADR extraction. Further, in the absence of auxiliary task dataset, we propose a novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available. Experiments with a large unlabeled pool of tweets, we show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by ∼7.2% in terms of F1 score.

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder contributing to a rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using gene expression profiles of young (20–50 years), aging (70–99 years), and AD (70–99 years). Also, these approaches were used to determine regional vulnerability in aging and AD. Hippocampus (HC) is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD in HC. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with either aging or disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14- 3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork.Further, network analysis on regions Entorhinal Cortex (EC), Post Central Gyrus (PCG) and Superior Frontal Gyrus (SFG) revealed similar cell-types were affected in aging and AD but the extent of changes is more diverse in aging than AD. Glial and endothelial changes were prominent in aging whereas neuronal changes were prominent in AD. Synaptic transmission, protein processing, mitochondria energy metabolism, and inflammation were the major processes affected in these regions. In aging, EC showed minimal changes followed by HC and PCG while SFG showed maximum changes. In AD, EC and HC showed maximum changes while PCG and SFG showed minimal changes. Overall, the network-level analysis showed differences in regional vulnerability to aging and AD. Taken together, our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.

Abstract

In bilingual and multilingual communities, people often use a fused lect or mixed language while communicating with each other. This intermixing of languages during communication is called Code- Mixing and it is emerging as a new variation in the field of language. Various terms are even given to the mixture of languages, for example the mixture of Hindi and the English language is called Hinglish or the mixture of French and English is called Franglais in layman terms. Social Networking platforms like Twitter and Facebook gives individuals a stage to express their perspectives and to impart their thoughts everywhere throughout the globe openly. On these platforms the communication take place via short texts often using multiple languages in a single text. In the past decade, there has been an exponential growth in social media users. This gives rise to an immense amount of user-generated data which consists of Twitter and Facebook posts. A considerable amount of this data consists of code-mixed texts as users from bilingual and multilingual communities often type in the same way as they communicate in an actual conversation. These type of texts makes the text classification a challenging task in the field of natural language processing. Hence, there is a need to study the computational aspects of text processing of code-mixing. But due to the inadequacy of annotated datasets, it is difficult to carry out such studies. In this thesis, we study code-mixing of Hindi and English language in Twitter posts by addressing two automatic text classification problems. First is identifying humor in code-mixed texts. Humor detection is one of the difficult tasks in the field of computational linguistics. An in-depth semantic understanding of the text is required to detect humor which makes the problem difficult to automate.Humor detection has been studied widely on monolingual texts but it lacks resources in the field of code-mixed texts. Then we study the problem of gender detection of the author in tweets which is a part of Author Profiling. Author profiling is the problem of automatically determining profiling aspects like the author’s gender and age group through a text. It is gaining popularity in computational linguistics as in the present digital world it is quite easy to make a fake profile with false name, gender, age and location which can be harmful for the users as well as to the community as a whole. In this work, We present for the first time a corpus containing Hindi-English code-mixed tweets annotated with author’s gender and presence of humor in the tweet. We construct a corpus containing 7558 code-mixed tweets in Hindi and English and describe the process of tweet annotation in detail.Furthermore, We develop a baseline classification system for the two problem discussed above which is based on a classification model tested on a dataset which consist of about 1.5 million Spanish election tweets in which a tweet is to be classified in one of the four pre-defined classes. We use character and word level features to build a system which uses Kernel SVM as the classifier and perform 10-fold cross validation to evaluate our classification system.

Abstract

The effectiveness of machine learning techniques is strongly dependent on the choice of data representation and the features available. In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications. For others, feature engineering requires substantial manual effort in designing and selecting features and is often tedious and non-scalable. An AI system must fundamentally understand the world around us which can only be done if it can learn to identify and disentangle the underlying implicit patterns present in the observed low-level sensory data. Feature learning presents a way to take advantage of human ingenuity and prior knowledge to compensate for that weakness. In order to make it possible to apply machine learning to different domains, it is very important to make learning algorithms less dependent on manual feature engineering, so that novel AI based applications could be developed faster.This thesis is about automated feature learning, i.e., learning representations of the data that make it easier to extract useful information when building classifiers or other predictors. A good representation is one that is useful as input to a supervised predictor. Among the various ways of learning representations, this thesis focuses on automating the task of feature learning without any domain knowledge via regression models. Our proposed algorithm is scalable regression-based feature learning algorithm. Being data driven, it requires no domain knowledge and is applicable to any dataset having numeric attributes. Such a generic representation is learnt by mining pairwise feature associations, identifying the linear or non-linear relationship between each pair, applying regularized regression and selecting those relationships that are stable. Our experimental evaluation on benchmark UC Irvine and gene expression datasets across different domains provides evidence that the features generated through our learning model can improve the prediction accuracy significantly for different classifiers without using any domain knowledge.

Abstract

The last decade has witnessed digitization of much governmental data. With the increasing usage of internet, tons of data is being digitized which includes parliamentary proceedings as well. A major downside of this digitization is that the data is in an unstructured format. There is a need to structure the data for use in machine learning, data mining and NLP. Much work has been done on parliamentary data such as Hansard, American congressional floor-debate data on various aspects but there isn’t enough work on the argumentation aspects of it using computational methods. Text summarization of the political discourse, particularly in the parliamentary proceedings is an area of research that is rela- tively explored less. Also, argumentation is relatively a new area of research which has many potential applications and can be used as a feature in machine learning models. In this thesis, we work on these rough edges: creating structured data of parliamentary debates, creating an annotated dataset of arguments on the debates and then propose a novel approach for summarizing these debates using argumentation theory. Creating structured data from unstructured PDF’s was a challenge. We developed a software parser which leveraged the patterns associated with each debate type to convert it into a structured format and store it into a database. The dataset created consisted of 768 debates from the Lok Sabha of the Indian Parliament spanning over 189 sessions. We also discuss the potential use cases of the dataset such as experimenting various natural language processing techniques like stance classification, sentiment analysis, detection of blame and appreciate speeches, generating statistical analysis of parties and their members and statistical analysis of the parliamentary debates. We explain the significance of the dataset by performing a few preliminary experiments such as stance classification, calculating sentiment po- larity and detection of speeches which capture raising of Issues, Blame, Appreciate or Call for Action. We achieved an accuracy scores of 0.80, 0.74, 0.60, 0.84 and 0.80 for stance classification, detection of issue, blame, appreciate and call for action speeches respectively. In the next part of the thesis, we harness this dataset for creating an argument annotated dataset. This dataset is ideally suited for creation of argument dataset because of the argumentative nature of the parliament debates. We annotate the debates with the argumentative theory which contains two argument components ‘claim’ and ‘premise’ and two argument relations support and attack which connect these two components. Few researchers have developed argument datasets of essays, news articles and online debate data but not of parliamentary proceedings. Creating an argument annotated dataset is a non-trivial task due to the complexity in the theory. Annotation of arguments is a non-trivial task as identification of argument components and relations between them needs critical attention and complete understating of the theory. We achieve an average score of 0.31 using multi-π inter annotator agreement metric before training of the annotators. After providing guidelines and training the annotators we achieve an average score of 0.73 using using multi-π inter annotator agreement metric which is a significant improvement and shows that arguments can be reliably annotated in parliamentary proceedings. To show the potential and novel application of the argumentation theory, we created a pipeline based approach for generating extractive summaries of the parliamentary debates using the argumentative theory. We use argumentation as a feature and design a semi automatic pipeline for generating debate sum- maries. The proposed approach considers the relevance of the topic, argumentative nature, sentiment and context features for generating summaries. We test our approach on the argument annotated dataset of debates developed previously. We discuss and analyze the results obtained using the pipeline. Our proposed methodology achieves F-scores of 0.44, 0.21, 0.41 using ROUGE-1, ROUGE-2 and ROUGEL evaluation metrics over other summarization algorithms in the case of 1500 word summary generation.We also evaluate our approach with other high performing popular summarization algorithms such as TextRank, Nenkova and LexRank.

Abstract

How to make machines speak more like humans? With the emerging technologies and computing abilities, once prescient ideas to emulate the human nature in machines are eventually achievable in the recent past. We specifically discuss the artificial production of speech by leaning into Text-to-Speech (TTS) synthesis and Voice Conversion (VC) sys-tems. TTS systems carry out the conversion of linguistic information present in the text to the minimal sound units which are further synthesized into speech using a vocoder. TTS systems are employed for many applications such as public announcements, voice assistants in mobile phones, screen readers for visually challenged and language learning systems. Incorporating the speaker variation would facilitate us in building a variety of synthetic voices. VC technology enables the speaker variations through the artificial imitation of the desired target speaker. We have thus explored VC frameworks for speaker variations (conversion) with and without parallel corpus. VC systems mainly find their applications in emotion conversion, personalized TTS systems, Normal to Lombard speech conversion and speech to singing conversion. In our thesis, we discuss artificial speech generation for: expressive speech synthesis, text-dependent Voice (speaker) conversion, and the major contributions through our work to Cross-Lingual Voice Conversion (CLVC). TTS Synthesis and VC involve modeling of sequential data. Thus, they highly rely on the contextual information. Accordingly, various approaches to appropriate feature representation and modeling of sequences in building the above-mentioned speech systems are discussed in this thesis. Our contributions: the continuous representation of text for prosody modeling, integrating signal correlation as one of the sub costs for phone-based concatenative speech synthesis. Furthermore, a light-weighted approach to CLVC is presented. Key ideas of this thesis are: • The sentence level modeling of text provides supra-segmental information required for the pre- diction of phone durations in Text-to-Speech Synthesis. Hence, we have incorporated a Recurrent Neural Network based Language Modeling (RNNLM) for continuous representation of sentences in addition to the matrix factorization for phones and words. Accordingly, a unique representation of text has been leveraged for the expressive speech synthesis. Embedding the signal correlation information while calculating the join cost facilitates an optimal selection of units for concatena-tive speech synthesis. Thus, Cross-Correlation was included in the join cost to further minimize the total cost when selecting the units. • A text-independent and transcription free approach was proposed for CLVC. Stacking of source and target speaker’s speech has enabled the projection of the language and speaker dependent features onto a common space. An auto-encoder is utilized as a parallel corpus generator to mitigate the issues involved in CLVC. A traditional parallel VC is then carried out between the generated and natural speech in target speaker’s voice. Further, a cross-language conversion is conducted for the target language. We conclude that an error reduction network can also be utilized to address the mapping of unaligned sequences.

Abstract

Tandemly repeated structural motifs within a protein form highly stable super secondary structural folds and provide multiple binding sites giving rise to diverse protein-protein/DNA/RNA interactions. Thus, these proteins provide a repertoire of cellular functions such as cell-cycle regulation, transcriptional regulation, cell differentiation, apoptosis, plant defense, bacterial invasion, etc.The evolutionary conservation and disease association of repeat proteins show their structural and functional importance and suggest the need for their identification. The multiple copies of repeats originate by the intragenic duplication and recombination events resulting in identical repeating units, both at the sequence and structure level. During the course of evolution, individual copies of the repeat accumulate mutations resulting in undetectable sequence similarity within repeating units, though the structural fold remains conserved. The traditional approaches of repeat identification and classification have mainly been based on identifying sequence patterns, and have limited coverage, especially for repeats with low sequence conservation. Thus, identification and classification of repeats at the structural level is desirable. In this thesis, we propose computationally efficient graph based approaches for the identification of repeats in protein structures, and use them to build a database of structural repeat proteins. The three dimensional topology of a protein structure is well captured by a graph, making it possible to analyze a complex protein structure as a mathematical entity. We observe that the principal eigen spectra of the adjacency matrix (eigenvector centrality) is able to capture the conserved residue interaction pattern within and between the repeating units, which are responsible for the regular tertiary fold of the repeat domain. The efficacy of the measure is tested on one of the most abundant repeat families, Ankyrin, by developing a rule based approach based on the observed pattern in eigenvector centrality profile of all known members of the family. It is evaluated on a benchmark dataset and the prediction compared with UniProt annotation, and other state-of-the-art sequence and structure based approaches. We extended the approach by developing a generalized algorithm, named PRIGSA, for the identification of structural repeats using the eigenvector centrality profile (Alevc) and secondary structure architecture (SSA). The algorithm identifies members of known families by comparing the Alevc profile and SSA of a query structure with pre-computed profiles of known families, and also de novo captures novel patterns to identify previously uncharacterized novel repeat proteins. The performance of the approach is tested on other known benchmark datasets and the performance compared with two repeat identification methods. The algorithm is improved to develop PRIGSA2 algorithm by incorporating structure based validation of predicted repeats by structural alignment and checking the relative orientation of secondary structures within the repeat copy. The predictions of the approach for 13 well-represented repeat families are compared with annotation in UniProt, comprising both solenoid and non-solenoid repeats. The PRIGSA2 algorithm is executed on all protein chains reported in the Protein Data Bank to build a database of structural repeats in proteins, DbStRiPs. The de novo predicted repeats are clustered and manually curated. We report one novel repeat family, Left-handed Beta Helix (LbH), and 31 De novo Protein Repeat Clusters (DPRCs). We anticipate the clusters of structurally similar repeat proteins, DPRCs, may lead to novel families in subsequent updates of DbStRiPs with the increase in structural data. In DbStRiPs, 11,901 repeats are reported in 10,816 PDB chains, which are categorized into known protein repeat families (KPRF) (74%), DPRC (6%) or Unclassified (20%). The repeat families and clusters are grouped in the database based on Kajava’s structural classification, and the unclassified repeats are grouped based on the secondary structure composition of the repeating unit. We also developed a web server for network analysis of protein structures, NAPS, providing a comprehensive platform for construction and analysis of protein structure graphs. Five types of networks can be constructed and various types of analyses including node/edge centrality, shortest paths, k-clique and graph spectral analyses can be carried out. The web server provides browser independent interactive platform for visual analysis of protein structure and network along with options to download the results in suitable formats.