AI News, 6 Types of Artificial Neural Networks Currently Being Used in ML artificial intelligence

Defining host–pathogen interactions employing an artificial intelligence workflow

HeLa cells were stimulated with 100 IU/mL IFNγ, infected with Salmonella enterica Typhimurium (STm) and analyzed 2 hr post-infection.

(B) Cellular readouts showing the proportion of cells that contain a certain number of bacteria vacuoles, the mean vacuole size of STm and the vacuole position as the value of the mean euclidian distance of STm vacuoles to the host cell nucleus.

(E) Cellular response to infection with STm measured through the percentage of cells that decorate vacuoles and the average proportion of vacuoles per cell that are being decorated simultaneously and the overall proportion of ubiquitin decorated STm vacuoles.

Limitations of Deep Learning in AI Research

Deep learning a subset of machine learning, has delivered super-human accuracy in a variety of practical uses in the past decade.

In contrast to machine learning where an AI agent learns from data based on machine learning algorithms, deep learning is based on a neural network architecture which acts similarly to the human brain, and allows the AI agent to analyze data fed in — in a structure similar to the way humans do.

assume a bank utilizes AI to assess your credit-value, and afterward denies you a loan, in numerous states there are laws that state that the bank needs to clarify why — if the bank is using a deep learning model for its loan decision making, their loan department (likely) will not be able to give a clear explanation as to why the loan was denied.

In addition, deep learning is absolutely restricted in its current form, on the grounds that practically all the fruitful uses of it [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32], utilize supervised machine learning with human-comment annotations which has been noted as a significant weakness — this dependence prevents deep neural networks from being applied to problems where input data is scarce.

He then dives in and reflects, before humanity starts to build AI systems that posses human capabilities (reasoning, understanding, common-sense), how can we evaluate AI systems on such tasks? — in order to thoroughly understand and develop true intelligent systems.

His research proposes the use of standardized tests on AI systems (similarly to the tests that students take towards progressing in the formal education system) by using two frameworks as to further develop AI systems, with notable benefits which can be applied in the form of social good and education.

Artificial neural networks, which try to mimic the architecture of the brain posses a multitude of connections of artificial neurons (nodes), the network itself is not an algorithm but a framework on which a variety of machine learning algorithms can function on to achieve desired tasks.

The foundations of neural network engineering are almost completely based on heuristics, with a small emphasis on network architecture choices, unfortunately there is no definite theory which tell us how to decide the right number of neurons for a certain model.

Bumblebee visual allometry results in locally improved resolution and globally improved sensitivity

Major concerns: The reviewers raised some concerns regarding the technical novelty of this analysis pipeline.

The goal of stage 2 HRMAn is to make accessible and tailor a neural network-driven analysis to the researcher to define host-pathogen interaction in an unbiased, highly reproducible, sound and robust fashion.

To denote that we indeed have tailored a neural network approach to a problem rather than newly developed from scratch a neural network-driven solution, we have changed the title to: “Defining Host-Pathogen Interactions Employing an Artificial Intelligence Workflow”.

Although the authors present some applications of this analysis pipeline, it would be advisable to provide additional data, demonstrating that HRMAn can detect additional, previously established phenotypes, such as those for ROP18 or ROP5.

Given that HRMAn only provides a modest improvement over existing software, primarily in the user interphase, it would be appropriate to more extensively test its function in more authentic experimental settings (see also comments of reviewer 3).

However, we do agree with the reviewers that to further test HRMAn’s function in what reviewer 3 calls more authentic experimental settings is of benefit for the manuscript and the use of the program.

We therefore added additional sets of analysis as described above (Figure 5A and 5B) specifically comparing isogenic Toxoplasma strains and we have added a murine cell line to the analysis.

We have also added a comparison of our analysis platform versus what is currently available in the Introduction: “Solutions existing to date can largely be split into two major categories: user-friendly turn-key GUI (TK-GUI)-based solutions and scripts ensembles (SE) solutions.

Due to the large support burden of the TK-GUI, these programs generally lack the implementations of the latest engineering advances.” While all reviewers agree that the experiments have been carefully performed, there is some scepticism regarding the novelty as a method (Reviewer 2) and the breadth of applications this tool will be used for in the field (Reviewer 1 and 3).

In support of its more than 1,200 views and 360 full pdf downloads on BioRxiv in 8 weeks (22.11.2018), suggests that HRMAn is already being employed by the field.

While the reviewers did not comment on the fact that HRMAn can be employed for analysis of Salmonella, the anti-Salmonella immunity field is largely focused on vacuole recruitment and marking of cytosolic bacteria.

Being able to recognise two pathogens with a magnitude of difference in size points to the fact that HRMAn can be adapted to an ever expanding range of pathogens, making it an attractive and important advance for the host-pathogen community at large.

Separate reviews (please respond to each point): Reviewer #1: […] Own opinion: The described technology will be very useful for researchers planning to perform image based screens on host-pathogen interactions or on intracellular pathogens in general.

This data alone is of great use to the Toxoplasma immunity field as we demonstrate that various cell lines require different levels of IFNγ to mount an efficient anti-Toxoplasma response.

Further, by carefully assessing a larger dataset of ubiquitin recruitment to cytosolic Salmonella in HeLa cells than has ever been analysed before, we find that HeLa cells seem to have reached their capacity of clearing Salmonella by ubiquitination and subsequent autophagy, independent of IFNγ treatment.

As the authors mention, there are many open source platforms available (not to mention commercial software) that allow HCI analysis of parasite growth, invasion, etc.

A recent publication used a relatively simple HCI analysis to perform chemical screens in Toxoplsma gondii (see Touquet et al., 2018), which is certainly inferior to the platform presented in this study.

size of parasitophorous vacuoles, host cell nuclei, etc.) As such I am not fully convinced if the platform is indeed superior to other imaging analysis software.

The authors should provide some examples or an in depth discussion regarding the advantages of their pipeline, when compared to other As detailed above, stage 1 uses well-established image analysis methods.

Rather than simple pixel enumeration, stage 2 employs a deep learning neural network to assess host protein recruitment to the pathogen.

We have also added a comparison of our analysis platform versus what is currently available in the Introduction: “Solutions existing to date can largely be split into two major categories: user-friendly turn-key GUI (TK-GUI)-based solutions and scripts ensembles (SE) solutions.

Due to the large support burden of the TK-GUI, these programs generally lack the implementations of the latest engineering advances.” Saying that, the described image analysis pipeline is very well designed and if widely used in the field will allow to analyse quantitative phenotypic data that are comparable in between different laboratories.

At this point the analysis pipeline is well suited for the analysis of host-pathogen interactions, in particular the characterisation of host-protein recruitment to the PV, a key interest of the Frickel lab and this aspect is of somewhat narrow interest.

Parasite lines expressing for example dense granule proteins are well described in the literature and it should be straight forward to add this parameter to the analysis pipeline.

An illumination correction step followed by a segmentation and infection detection step (named 'Stage 1'), followed up by an analysis of host protein recruitment ('Stage 2').

The proposed pipeline, a combination of default analysis components, solves the task at hand as long as the provided images (after illumination correction) can be segmented via a simple threshold.

The relatively shallow network architecture and all parameter and training decisions are sensible set to values used in many neural network applications.

While, as before, nothing here is even close to being a methodological advance in the field of machine learning, all decisions seem well thought trough and I have no problems believing that final classification results are good.

As such, we decided to keep the machine learning algorithms as simple as possible, to save on computing resources and to prevent overcomplicating the analysis and its set-up.

Instead the optimisation of our network runs on a single GPU making it more accessible for the end user (setups with dual GPUs are quite rare and difficult to operate in biological labs).

Provided the fact that the network has been optimized on the real-world data, rather than publicly available datasets, proved to be solvable, alighting as close as possible to a known solution is dictated by the good engineering practice.

(The authors acknowledges the existence of many other tools for high throughput analysis, see Supplementary file 1, but little to no discussion of similarities or difference to these tools is given.) As suggested, to complement Supplementary file 1, we now review other analysis solutions in light of HRMAn in the Introduction part as mentioned above.

Through class-activation map generation (occlusion map method) we show the fraction of the pathogen micrograph important for evaluation of the host-pathogen interaction (Figure 2).

Considering the reviewers concerns, we have significantly improved the presentation of HRMAn through additional experimental analysis, a refined description of HRMAn and its novelty, as well as by comparing our analysis solution to other existing programs.

Reviewer #3: […] Major concerns: a) Since there are many differences between Type I and Type II parasites (including growth rate and viability, which could affect the recruitment measurements), it would be appropriate for the authors to look at isogenic lines that differ only in a particular effector.

d) It is important that the training dataset used for the manuscript be released in its entirety to ensure that readers can replicate the results of the paper and account for any differences between lab-specific assignments and HRMAn.

Additionally, we have now stated that “images used for training of the machine learning algorithms will be available through the Crick Institute’s online file exchange system upon request.” in the Materials and methods section.

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