Platelet cell data may be obtained from analysis of a blood sample by a hematology analyzer or like device. Systems and apparatus may process the platelet cell data in accordance with platelet parameter thresholds selected by a user. The platelet cell data may then be categorized and displayed in one or more useful forms for medical diagnostic and/or research purposes. The platelet cell data may be categorized and displayed in, e.g., tabular and/or graphical form based on the user-selected platelet parameter thresholds. Methods of processing platelet cell data for categorizing and displaying the platelet cell data in one or more useful forms are also provided, as are other aspects.

Methods, apparatuses, systems, computing devices, computing entities, and/or the like are provided. An example method may include selecting at least one client profile data object from a plurality of client profile data objects; retrieving at least one initial transcriptome data object and at least one subsequent transcriptome data object associated with the at least one client profile data object; generating at least one dynamic multigraph data object based at least in part on the at least one initial transcriptome data object, the at least one subsequent transcriptome data object, and at least one clinical event data object; training a temporal graph network based at least in part on the at least one dynamic multigraph data object to generate a risk window prediction data object; and performing at least one data operation based at least in part on the risk window prediction data object.

Methods, apparatuses, systems, computing devices, computing entities, and/or the like are provided. An example method may include selecting at least one client profile data object from a plurality of client profile data objects; retrieving at least one initial transcriptome data object and at least one subsequent transcriptome data object associated with the at least one client profile data object; generating at least one dynamic multigraph data object based at least in part on the at least one initial transcriptome data object, the at least one subsequent transcriptome data object, and at least one clinical event data object; training a temporal graph network based at least in part on the at least one dynamic multigraph data object to generate a risk window prediction data object; and performing at least one data operation based at least in part on the risk window prediction data object.

A computational model may be used to predict targets of a candidate, or predict candidates that interact with a target. A plurality of pairs may be established, each including a candidate and a respective one of a plurality of controls, each of the plurality of controls known to bind with a target. For each pair, values of at least two datatypes of the candidate may be compared to values of the at least two datatypes of the respective one of the plurality of controls in the pair to generate a similarity score for each of the at least two datatypes of each pair. Similarity scores may be converted to likelihood values indicating likelihood that the candidate and the controls have a shared target based on the respective one of the at least two datatypes. Tests may be performed to validate predictions regarding interactivity of candidates and targets.

A computational model may be used to predict targets of a candidate, or predict candidates that interact with a target. A plurality of pairs may be established, each including a candidate and a respective one of a plurality of controls, each of the plurality of controls known to bind with a target. For each pair, values of at least two datatypes of the candidate may be compared to values of the at least two datatypes of the respective one of the plurality of controls in the pair to generate a similarity score for each of the at least two datatypes of each pair. Similarity scores may be converted to likelihood values indicating likelihood that the candidate and the controls have a shared target based on the respective one of the at least two datatypes. Tests may be performed to validate predictions regarding interactivity of candidates and targets.

The disclosure includes: acquiring first data; grouping the first data to obtain K packet sub-data; inputting a preset primer into a random generator to obtain 4T random number sequences, 4.sup.T>K; determining the packet sub-data corresponding to the ith random number sequence, and performing exclusive or (XOR) operation on the determined packet sub-data to obtain data information DATAi, and obtaining a DNA molecular chain according to the data information DATAi, the preset primer and the generation times capacity of the random generator; performing DNA sequence synthesis on the plurality of DNA molecular chains to obtain target storage data. In the disclosure, in the process of coding the first data to obtain a DNA molecular chain, a random generator is added to greatly simplify the coding process and implement efficient and accurate coding on the first data. The disclosure may be widely applied to a field of data storage technologies.

The disclosure includes: acquiring first data; grouping the first data to obtain K packet sub-data; inputting a preset primer into a random generator to obtain 4T random number sequences, 4.sup.T>K; determining the packet sub-data corresponding to the ith random number sequence, and performing exclusive or (XOR) operation on the determined packet sub-data to obtain data information DATAi, and obtaining a DNA molecular chain according to the data information DATAi, the preset primer and the generation times capacity of the random generator; performing DNA sequence synthesis on the plurality of DNA molecular chains to obtain target storage data. In the disclosure, in the process of coding the first data to obtain a DNA molecular chain, a random generator is added to greatly simplify the coding process and implement efficient and accurate coding on the first data. The disclosure may be widely applied to a field of data storage technologies.

Mechanisms are provided for performing automated monitoring and retraining of infectious disease computer models. A trained infectious disease computer model is executed on case report data for a target region to generate prediction results predicting a state of an infectious disease spread within the target region for a given time. The prediction results generated by the trained infectious disease computer model are automatically compared to ground truth data to determine a deviation between the prediction results and the ground truth data. The ground truth data comprises at least one of actual case report data collected and reported by source computing systems for the given time, or a previous prediction result generated by the trained infectious disease computer model. Statistical test(s) are applied to the deviation to determine if it is statistically significant, and if so, re-training of the trained infectious disease computer model is automatically initiated.

Mechanisms are provided for performing automated monitoring and retraining of infectious disease computer models. A trained infectious disease computer model is executed on case report data for a target region to generate prediction results predicting a state of an infectious disease spread within the target region for a given time. The prediction results generated by the trained infectious disease computer model are automatically compared to ground truth data to determine a deviation between the prediction results and the ground truth data. The ground truth data comprises at least one of actual case report data collected and reported by source computing systems for the given time, or a previous prediction result generated by the trained infectious disease computer model. Statistical test(s) are applied to the deviation to determine if it is statistically significant, and if so, re-training of the trained infectious disease computer model is automatically initiated.

Described herein are a secure system for sharing private data and related systems and methods for incentivizing and validating private data sharing. In some embodiments, private data providers may register to selectively share private data under controlled sharing conditions. The private data may be cryptographically secured using encryption information corresponding to one or more secure execution environments. To demonstrate to the private data providers that the secure execution environment is secure and trustworthy, attestations demonstrating the security of the secure execution environment may be stored in a distributed ledger (e.g., a public blockchain). Private data users that want access to shared private data may publish applications for operating on the private data to a secure execution environment and publish, in a distributed ledger, an indication that the application is available to receive private data. The distributed ledger may also store sharing conditions under which the private data will be shared.