Delving into the Latest Updates on SEEK Ltd. with Synapse

Overview

Tags

Infectious Diseases

Respiratory Diseases

Therapeutic vaccine

Prophylactic vaccine

Disease domain score

A glimpse into the focused therapeutic areas

Technology Platform

Most used technologies in drug development

Targets

Most frequently developed targets

No Data

Disease Domain	Count

Infectious Diseases	2

Top 5 Drug Type	Count

Therapeutic vaccine	1
Prophylactic vaccine	1

Search platforms can have more than one type of user, e.g., those who provide and those who consume content. As an example, in a job/talent search platform, content providers are: (1) job seekers who provide CVs, and (2) hirers who provide job advertisements; content consumers, on the other hand, are: (3) job seekers searching for specific jobs, and (4) hirers/recruiters searching for candidates to fill particular positions. As a result, there are four types of users, each with potentially different patterns of language use.In this paper, we compare the language used by different groups of users in job/talent search, by way of word embeddings pre-trained over documents associated with distinct types of users. In doing so, we investigate whether there are systematic shifts/ mismatches in vocabulary or the use of the same term, and consider the implications for an integrated search solution. Our experiments unearth significant differences in language use, but also that there is a strong agreement between the results of our intrinsic and extrinsic comparisons of word embeddings.

03 Nov 2019·Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Investigating the Learning Process in Job Search

Author: Mao, Jiaxin ; Sadeghi, Sargol ; Sanderson, Mark ; Spina, Damiano ; Scholer, Falk

We investigated the learning process in search by conducting a log-based study involving registered job seekers of a commercial job search engine. The analysis shows that job search is a complex task: seekers usually submit multiple queries over sessions that can last days or even weeks. We find that querying, clicking, and job application rates change over time: job seekers tend to use more filters and a less diverse set of query terms. In terms of click and application behavior, we observed a significant decrease in click rate and query term diversity, as well as an increase in application rates. These trends are found to largely match information seeking models of learning in a complex search task. However, common behaviors are observed in the logs that suggest the existing models may not be sufficient to describe all of the users' learning and seeking processes.

30 Jan 2019·Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Joint Optimization of Cascade Ranking Models

Author: Culpepper, J. Shane ; Gallagher, Luke ; Blanco, Roi ; Chen, Ruey-Cheng

Reducing excessive costs in feature acquisition and model evaluation has been a long-standing challenge in learning-to-rank systems. A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems. However, learning a cascade model is often complex, and usually performed stagewise independently across the entire ranking pipeline. In this work we show that learning a cascade ranking model in this manner is often suboptimal in terms of both effectiveness and efficiency. We present a new general framework for learning an end-to-end cascade of rankers using backpropagation. We show that stagewise objectives can be chained together and optimized jointly to achieve significantly better trade-offs globally. This novel approach is generalizable to not only differentiable models but also state-of-the-art tree-based algorithms such as LambdaMART and cost-efficient gradient boosted trees, and it opens up new opportunities for exploring additional efficiency-effectiveness trade-offs in large-scale search systems.

100 Deals associated with SEEK Ltd.

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100 Translational Medicine associated with SEEK Ltd.

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Pipeline

Pipeline Snapshot as of 27 Dec 2024

The statistics for drugs in the Pipeline is the current organization and its subsidiaries are counted as organizations,Early Phase 1 is incorporated into Phase 1, Phase 1/2 is incorporated into phase 2, and phase 2/3 is incorporated into phase 3

Phase 1 Clinical

1

Phase 2 Clinical

Other

1

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Current Projects

Drug(Targets)	Indications	Global Highest Phase

Influenza virus vaccine(SEEK Ltd.)	Influenza, Human More	Phase 2
AGS-v(National Institute of Allergy & Infectious Diseases)	Virus Diseases More	Phase 1
HIV vaccine(SEEK Ltd.)	HIV Infections More	Pending