Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification.

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://github.com/soham-chitnis10/WSI-domain-specific.

A review of some techniques for inclusion of domain-knowledge into deep neural networks.

We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.

Erythrocytes lacking the Langereis blood group protein ABCB6 are resistant to the malaria parasite Plasmodium falciparum.

The ATP-binding cassette transporter ABCB6 was recently discovered to encode the Langereis (Lan) blood group antigen. Lan null individuals are asymptomatic, and the function of ABCB6 in mature erythrocytes is not understood. Here, we assessed ABCB6 as a host factor for Plasmodium falciparum malaria parasites during erythrocyte invasion. We show that Lan null erythrocytes are highly resistant to invasion by P. falciparum, in a strain-transcendent manner. Although both Lan null and Jr(a-) erythrocytes harbor excess porphyrin, only Lan null erythrocytes exhibit a P. falciparum invasion defect. Further, the zoonotic parasite P. knowlesi invades Lan null and control cells with similar efficiency, suggesting that ABCB6 may mediate P. falciparum invasion through species-specific molecular interactions. Using tandem mass tag-based proteomics, we find that the only consistent difference in membrane proteins between Lan null and control cells is absence of ABCB6. Our results demonstrate that a newly identified naturally occurring blood group variant is associated with resistance to Plasmodium falciparum.

An Ab Initio Method for Designing Multi-Target Specific Pharmacophores using Complementary Interaction Field of Aspartic Proteases.

For past few decades, key objectives of rational drug discovery have been the designing of specific and selective ligands for target proteins. Infectious diseases like malaria are continuously becoming resistant to traditional medicines, which inculcates need for new approaches to design inhibitors for antimalarial targets. A novel method for ab initio designing of multi target specific pharmacophores using the interaction field maps of active sites of multiple proteins has been developed to design 'specificity' pharmacophores for aspartic proteases. The molecular interaction field grid maps of active sites of aspartic proteases (plasmepsin II & IV from Plasmodium falciparum, plasmepsin from Plasmodium vivax, pepsin & cathepsin D from human) are calculated and common pharmacophoric features for favourable binding spots in active sites are extracted in the form of cliques of graphs using inductive logic programming (ILP). The two pharmacophore ensembles are constructed from largest common cliques by imposing size of receptor active site (L) and domain-specific receptor-ligand information (S). The overlap of chemical space between two ensembles and the results of virtual screening of inhibitor database with known activities show that this method can design efficient pharmacophores with no prior ligand information.

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

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. CONCLUSION: The results suggest that the CSHMM is likely to be a useful tool for miRNA discovery either for analysis of individual sequences or for genome scan. Our pipeline, consisting of a CSHMM and filters to reduce false positives shows promise as an approach for ab initio identification of novel miRNAs.

CID-miRNA: a web server for prediction of novel miRNA precursors in human genome.

microRNAs (miRNA) are a class of non-protein coding functional RNAs that are thought to regulate expression of target genes by direct interaction with mRNAs. miRNAs have been identified through both experimental and computational methods in a variety of eukaryotic organisms. Though these approaches have been partially successful, there is a need to develop more tools for detection of these RNAs as they are also thought to be present in abundance in many genomes. In this report we describe a tool and a web server, named CID-miRNA, for identification of miRNA precursors in a given DNA sequence, utilising secondary structure-based filtering systems and an algorithm based on stochastic context free grammar trained on human miRNAs. CID-miRNA analyses a given sequence using a web interface, for presence of putative miRNA precursors and the generated output lists all the potential regions that can form miRNA-like structures. It can also scan large genomic sequences for the presence of potential miRNA precursors in its stand-alone form. The web server can be accessed at http://mirna.jnu.ac.in/cidmirna/.

Statistical evaluation of the Predictive Toxicology Challenge 2000-2001.

MOTIVATION: The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. RESULTS: Fourteen machine learning groups generated 111 models. The use of Receiver Operating Characteristic (ROC) space allowed the models to be uniformly compared regardless of the error cost function. We developed a statistical method to test if a model performs significantly better than random in ROC space. Using this test as criteria five models performed better than random guessing at a significance level p of 0.05 (not corrected for multiple testing). Statistically the best predictor was the Viniti model for female mice, with p value below 0.002. The toxicologically most interesting models were Leuven2 for male mice, and Kwansei for female rats. These models performed well in the statistical analysis and they are in the middle of ROC space, i.e. distant from extreme cost assumptions. These predictive models were also independently judged by domain experts to be among the three most interesting, and are believed to include a small but significant amount of empirically learned toxicological knowledge. AVAILABILITY: PTC details and data can be found at: http://www.predictive-toxicology.org/ptc/.