The organization of behavior: A neuropsychological theory

Interactome Networks and Human Disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Why most published research findings are false

https://research.birmingham.ac.uk/portal/files/33294910/Larson_et_al_State_Vaccine_Confidence_EBioMedicine.pdf

The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey

The idea of “date” and “party” hubs has been influential in the study of protein–protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.

/pdf/revisiting-date-and-party-hubs-novel-approaches-to-role-3z4zujkwbc.pdf

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

Noise is a stark reality in real life data. Especially in the domain of text analytics, it has a significant impact as data cleaning forms a very large part of the data processing cycle. Noisy unstructured text is common in informal settings such as on-line chat, SMS, email, newsgroups and blogs, automatically transcribed text from speech, and automatically recognized text from printed or handwritten material. Gigabytes of such data is being generated everyday on the Internet, in contact centers, and on mobile phones. Researchers have looked at various text mining issues such as pre-processing and cleaning noisy text, information extraction, rule learning, and classification for noisy text. This paper focuses on the issues faced by automatic text classifiers in analyzing noisy documents coming from various sources. The goal of this paper is to bring out and study the effect of different kinds of noise on automatic text classification. Does the nature of such text warrant moving beyond traditional text classification techniques? We present detailed experimental results with simulated noise on the Reuters- 21578 and 20-newsgroups benchmark datasets. We present interesting results on real-life noisy datasets from various CRM domains.

/pdf/how-much-noise-is-too-much-a-study-in-automatic-text-57hzwf0idl.pdf

How Much Noise Is Too Much: A Study in Automatic Text Classification

http://spiral.imperial.ac.uk/bitstream/10044/1/34487/2/manuscript-final-black-figs-sm%281%29.pdf

Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years

Background: It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a “context-sensitive” Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification. Results: The probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this “trained” CSHMM was evaluated by: (a) cross-validation estimates using known human sequences, (b) predictions on a dataset of known miRNAs, and (c) prediction on a dataset of non coding RNAs. The CSHMM is compared with two recently developed methods, miPred and CID-miRNA. The results suggest that the CSHMM performs better than these methods. In addition, the CSHMM was used in a pipeline that includes filters that check for the presence of EST matches and the presence of Drosha cutting sites. This pipeline was used to scan and identify potential miRNAs from the human chromosome 19. It was also used to identify novel miRNAs from small RNA sequences of human normal leukocytes obtained by the Deep sequencing (Solexa) methodology. A total of 49 and 308 novel miRNAs were predicted from chromosome 19 and from the small RNA sequences respectively.

/pdf/prediction-of-novel-precursor-mirnas-using-a-context-2k7rgudg0i.pdf

Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM)

https://web.iitd.ac.in/~sumeet/nc-span.pdf

Sumeet Agarwal

Papers

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

How Much Noise Is Too Much: A Study in Automatic Text Classification

Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years

Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM)

Kernel-based online machine learning and support vector reduction